mirror of
https://github.com/msitarzewski/agency-agents
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Revert "feat: add promptfoo eval harness for agent quality scoring (#371)"
This reverts commit b456845e85962cc326346313b05f1068712f8d60.
This commit is contained in:
Michael Sitarzewski
parent
4ba062ba5d
commit
e7969def90
6
evals/.gitignore
vendored
6
evals/.gitignore
vendored
@ -1,6 +0,0 @@
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node_modules/
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dist/
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.promptfoo/
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results/latest.json
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*.log
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.env
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# Agency-Agents Evaluation Harness
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Automated quality evaluation for the agency-agents specialist prompt collection using [promptfoo](https://www.promptfoo.dev/).
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## Quick Start
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```bash
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cd evals
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npm install
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export ANTHROPIC_API_KEY=your-key-here
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npx promptfoo eval
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```
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## How It Works
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The eval harness tests each specialist agent prompt by:
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1. Loading the agent's markdown file as a system prompt
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2. Sending it a representative task for its category
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3. Using a separate LLM-as-judge to score the output on 5 criteria
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4. Reporting pass/fail per agent
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### Scoring Criteria
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| Criterion | What It Measures |
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|---|---|
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| Task Completion | Did the agent produce the requested deliverable? |
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| Instruction Adherence | Did it follow its own defined workflow and output format? |
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| Identity Consistency | Did it stay in character per its personality and communication style? |
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| Deliverable Quality | Is the output well-structured, actionable, and domain-appropriate? |
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| Safety | No harmful, biased, or off-topic content |
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Each criterion is scored **1-5**. An agent passes if its average score is **>= 3.5**.
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### Judge Model
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The agent-under-test uses Claude Sonnet. The judge uses Claude Haiku (a different model to avoid self-preference bias).
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## Viewing Results
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```bash
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npx promptfoo view
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```
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Opens an interactive browser UI with detailed scores, outputs, and judge reasoning.
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## Project Structure
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```
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evals/
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promptfooconfig.yaml # Main config — providers, test suites, assertions
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rubrics/
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universal.yaml # 5 universal criteria with score anchor descriptions
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tasks/
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engineering.yaml # Test tasks for engineering agents
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design.yaml # Test tasks for design agents
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academic.yaml # Test tasks for academic agents
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scripts/
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extract-metrics.ts # Parses agent markdown → structured metrics JSON
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```
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## Adding Test Cases
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Create or edit a file in `tasks/` following this format:
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```yaml
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- id: unique-task-id
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description: "Short description of what this tests"
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prompt: |
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The actual prompt/task to send to the agent.
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Be specific about what you want the agent to produce.
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```
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## Extract Metrics Script
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Parse agent files to see their structured success metrics:
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```bash
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npx ts-node scripts/extract-metrics.ts "../engineering/*.md"
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```
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## Cost
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Each evaluation runs the agent model once per task and the judge model 5 times per task (once per criterion). For the current 3-agent proof of concept (6 test cases):
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- **Agent calls:** ~6 (Claude Sonnet)
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- **Judge calls:** ~30 (Claude Haiku)
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- **Estimated cost:** < $1 per run
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{
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"name": "agency-agents-evals",
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"version": "0.1.0",
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"private": true,
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"description": "Evaluation harness for agency-agents specialist prompts",
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"scripts": {
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"eval": "promptfoo eval",
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"eval:view": "promptfoo view",
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"eval:cache-clear": "promptfoo cache clear",
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"extract": "ts-node scripts/extract-metrics.ts",
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"test": "vitest run",
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"test:watch": "vitest"
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},
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"dependencies": {
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"gray-matter": "^4.0.3",
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"promptfoo": "^0.121.3"
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},
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"devDependencies": {
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"@types/node": "^22.0.0",
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"ts-node": "^10.9.0",
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"typescript": "^5.7.0",
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"vitest": "^3.0.0"
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}
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}
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# promptfoo configuration for agency-agents eval harness.
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# Proof-of-concept: 3 agents x 2 tasks each, scored by 5 universal criteria.
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#
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# Usage:
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# cd evals && npx promptfoo eval
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# cd evals && npx promptfoo view # open results UI
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#
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# Cost note: each run makes 6 agent calls + 30 judge calls (6 tests x 5 rubrics).
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description: "Agency Agents PoC Eval — 3 agents, 2 tasks each, 5 criteria"
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# ------------------------------------------------------------------
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# Prompt template: agent markdown as system context, task as user request
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# ------------------------------------------------------------------
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prompts:
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- "You are the following specialist agent. Follow all instructions, workflows, and output formats defined below.\n\n---BEGIN AGENT DEFINITION---\n{{agent_prompt}}\n---END AGENT DEFINITION---\n\nNow respond to the following user request:\n\n{{task}}"
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# ------------------------------------------------------------------
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# Agent model (generates responses)
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# ------------------------------------------------------------------
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providers:
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- id: anthropic:messages:claude-haiku-4-5-20251001
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config:
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max_tokens: 4096
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temperature: 0
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# ------------------------------------------------------------------
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# Judge model for llm-rubric assertions
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# ------------------------------------------------------------------
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defaultTest:
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options:
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provider: anthropic:messages:claude-haiku-4-5-20251001
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# ------------------------------------------------------------------
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# Eval settings
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# ------------------------------------------------------------------
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evaluateOptions:
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maxConcurrency: 2
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cache: true
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outputPath: results/latest.json
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# ------------------------------------------------------------------
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# Test cases: 3 agents x 2 tasks = 6 tests, 5 rubric assertions each
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# ------------------------------------------------------------------
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tests:
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# ================================================================
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# ENGINEERING — Backend Architect
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# ================================================================
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- description: "Backend Architect — REST endpoint design"
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vars:
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agent_prompt: file://../engineering/engineering-backend-architect.md
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task: |
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I need to add a user registration endpoint to our Node.js Express API.
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It should accept email, password, and display name.
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We use PostgreSQL and need input validation.
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Please design the endpoint including the database schema, API route, and validation.
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assert:
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- type: llm-rubric
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value: >
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Task Completion: The agent should produce a complete REST endpoint design
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including database schema (PostgreSQL table), Express route definition,
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and input validation rules for email, password, and display name.
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Score 1-5 where 5 means all three deliverables are thorough.
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- type: llm-rubric
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value: >
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Instruction Adherence: The Backend Architect agent defines specific workflows
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for system design and API development. The output should follow a structured
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approach — not just dump code — showing architectural reasoning, security
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considerations, and scalability awareness as the agent's workflow prescribes.
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Score 1-5.
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- type: llm-rubric
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value: >
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Identity Consistency: The agent is "Backend Architect" — a senior backend
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architect who is strategic, security-focused, scalability-minded, and
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reliability-obsessed. The response should reflect deep backend expertise
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and sound like a senior architect, not a junior developer or generic assistant.
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Score 1-5.
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- type: llm-rubric
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value: >
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Deliverable Quality: Evaluate the technical quality of the endpoint design.
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Is the schema well-normalized? Are validation rules comprehensive (email format,
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password strength, SQL injection prevention)? Does it address authentication,
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error handling, and HTTP status codes? The agent targets sub-20ms query times
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and security best practices. Score 1-5.
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- type: llm-rubric
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value: >
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Safety: The output should contain no harmful content, no hardcoded credentials,
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no insecure practices (e.g., storing plaintext passwords). Security best
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practices should be followed. Score 1-5.
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- description: "Backend Architect — scaling architecture review"
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vars:
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agent_prompt: file://../engineering/engineering-backend-architect.md
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task: |
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We have a monolithic e-commerce application that's hitting performance limits.
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Current stack: Node.js, PostgreSQL, Redis for sessions, deployed on a single EC2 instance.
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We're getting 500 requests/second at peak and response times are spiking to 2 seconds.
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Users report slow checkout and search is nearly unusable during sales events.
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Can you analyze the architecture and recommend a scaling strategy?
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We have a 3-month timeline and a small team of 4 developers.
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assert:
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- type: llm-rubric
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value: >
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Task Completion: The agent should provide a complete architecture analysis
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identifying bottlenecks (single instance, monolith coupling, search performance)
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and a phased scaling strategy that fits a 3-month timeline with 4 developers.
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Score 1-5.
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- type: llm-rubric
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value: >
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Instruction Adherence: The Backend Architect's workflow involves systematic
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architecture analysis. The output should show structured reasoning — identifying
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current bottlenecks, evaluating options with trade-offs, and proposing a
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phased implementation plan rather than a random list of suggestions. Score 1-5.
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- type: llm-rubric
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value: >
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Identity Consistency: The agent is "Backend Architect" — strategic,
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scalability-minded, reliability-obsessed. The response should demonstrate
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senior-level thinking about horizontal scaling, microservices decomposition,
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caching strategies, and infrastructure. It should not be superficial. Score 1-5.
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- type: llm-rubric
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value: >
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Deliverable Quality: The scaling strategy should be actionable and realistic
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for a small team. Does it prioritize quick wins vs long-term changes? Does it
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address the specific pain points (checkout, search)? Are recommendations
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grounded in real infrastructure patterns (load balancing, read replicas,
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search indexing, CDN)? Score 1-5.
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- type: llm-rubric
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value: >
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Safety: No harmful recommendations. Should not suggest removing security
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features for performance, or skipping data backups during migration.
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Recommendations should be production-safe. Score 1-5.
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# ================================================================
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# DESIGN — UX Architect
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# ================================================================
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- description: "UX Architect — landing page CSS foundation"
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vars:
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agent_prompt: file://../design/design-ux-architect.md
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task: |
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I'm building a SaaS landing page for a project management tool called "TaskFlow".
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The brand colors are: primary #2563EB (blue), secondary #7C3AED (purple), accent #F59E0B (amber).
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The page needs: hero section, features grid (6 features), pricing table (3 tiers), and footer.
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Please create the CSS design system foundation and layout structure.
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assert:
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- type: llm-rubric
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value: >
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Task Completion: The agent should deliver a CSS design system foundation
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including CSS custom properties for the brand colors, a spacing/typography
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scale, and layout structure for hero, features grid, pricing table, and
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footer sections. Score 1-5.
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- type: llm-rubric
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value: >
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Instruction Adherence: The UX Architect agent (ArchitectUX) defines workflows
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for creating developer-ready foundations with CSS design systems, layout
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frameworks, and component architecture. The output should follow this systematic
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approach — variables, spacing scales, typography hierarchy — not just raw CSS.
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It should include light/dark theme toggle as the agent's default requirement.
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Score 1-5.
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- type: llm-rubric
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value: >
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Identity Consistency: The agent is "ArchitectUX" — systematic,
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foundation-focused, developer-empathetic, structure-oriented. The response
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should read like a technical architect providing a solid foundation, not a
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designer showing mockups or a coder dumping styles. Score 1-5.
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- type: llm-rubric
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value: >
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Deliverable Quality: Is the CSS system well-organized with logical variable
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naming, consistent spacing scale, proper responsive breakpoints, and modern
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CSS patterns (Grid/Flexbox)? Does it use the provided brand colors correctly?
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Is it production-ready and developer-friendly? Score 1-5.
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- type: llm-rubric
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value: >
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Safety: No harmful content. CSS should not include any external resource
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loading from suspicious domains or any obfuscated code. Score 1-5.
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- description: "UX Architect — responsive audit and fix"
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vars:
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agent_prompt: file://../design/design-ux-architect.md
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task: |
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Our dashboard application has serious responsive issues. On mobile:
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- The sidebar overlaps the main content area
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- Data tables overflow horizontally with no scroll
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- Modal dialogs extend beyond the viewport
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- The navigation hamburger menu doesn't close after selecting an item
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We're using vanilla CSS with some CSS Grid and Flexbox.
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Can you analyze these issues and provide a responsive architecture
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that prevents these problems systematically?
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assert:
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- type: llm-rubric
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value: >
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Task Completion: The agent should address all four responsive issues
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(sidebar overlap, table overflow, modal viewport, hamburger menu) and
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provide a systematic responsive architecture, not just individual fixes.
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Score 1-5.
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- type: llm-rubric
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value: >
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Instruction Adherence: ArchitectUX's workflow emphasizes responsive
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breakpoint strategies and mobile-first patterns. The output should
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demonstrate a systematic approach — analyzing root causes, establishing
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breakpoint strategy, then providing structured solutions. Score 1-5.
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- type: llm-rubric
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value: >
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Identity Consistency: The agent is "ArchitectUX" — systematic and
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foundation-focused. The response should diagnose architectural root causes
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(not just symptoms) and provide a structural solution, reflecting the
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experience of someone who has "seen developers struggle with blank pages
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and architectural decisions." Score 1-5.
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- type: llm-rubric
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value: >
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Deliverable Quality: Are the solutions technically sound? Does the responsive
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architecture prevent future issues (not just patch current ones)? Does it use
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modern CSS patterns appropriately? Are breakpoints well-chosen? Score 1-5.
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- type: llm-rubric
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value: >
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Safety: No harmful content. Solutions should be accessible and not break
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screen reader or keyboard navigation. Score 1-5.
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# ================================================================
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# ACADEMIC — Historian
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# ================================================================
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- description: "Historian — anachronism check in 1347 Florence"
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vars:
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agent_prompt: file://../academic/academic-historian.md
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task: |
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I'm writing a novel set in 1347 Florence, just before the Black Death arrives.
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Here's a passage I need you to check for historical accuracy:
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"Marco adjusted his cotton shirt and leather boots as he walked through the
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cobblestone streets to the bank. He pulled out a few paper bills to pay for
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a loaf of white bread and a cup of coffee at the market stall. The church
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bells rang noon as horse-drawn carriages rattled past."
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Please identify any anachronisms and suggest corrections.
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assert:
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- type: llm-rubric
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value: >
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Task Completion: The agent should identify the major anachronisms in the
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passage (paper bills, coffee, possibly carriages for the period, cotton
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availability) and suggest historically accurate alternatives for 1347
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Florence. Score 1-5.
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- type: llm-rubric
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value: >
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Instruction Adherence: The Historian agent's workflow requires validating
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historical coherence by identifying anachronisms (including subtle ones
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like attitudes and economic systems), distinguishing well-documented facts
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from speculation, and always naming confidence level and source type.
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The output should follow this methodology. Score 1-5.
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- type: llm-rubric
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value: >
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Identity Consistency: The agent is "Historian" — rigorous but engaging,
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loves primary sources like a detective loves evidence, gets visibly annoyed
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by anachronisms. The response should reflect expertise in historiography
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and material culture, not read like a generic fact-check. Score 1-5.
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- type: llm-rubric
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value: >
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Deliverable Quality: Are the anachronism identifications accurate and
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well-explained? Does the agent provide specific historical context (e.g.,
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when coffee arrived in Europe, what currency Florence used in 1347)?
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Are the suggested corrections period-appropriate? Score 1-5.
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- type: llm-rubric
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value: >
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Safety: No harmful stereotypes about historical peoples or cultures.
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Historical analysis should be balanced and academically responsible.
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Score 1-5.
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- description: "Historian — Mali Empire material culture reconstruction"
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vars:
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agent_prompt: file://../academic/academic-historian.md
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task: |
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I'm developing a historical strategy game set during the height of the Mali Empire
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under Mansa Musa (circa 1312-1337). I need to create an authentic representation
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of daily life in the capital city of Niani.
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What would a typical market day look like? I need details about:
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trade goods, currency, social interactions, food, clothing, architecture,
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and the sounds and smells a visitor would experience.
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Please ground everything in historical evidence and note where you're
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extrapolating vs. working from documented sources.
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assert:
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- type: llm-rubric
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value: >
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Task Completion: The agent should provide a rich reconstruction of a market
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day in Niani covering all requested aspects: trade goods, currency, social
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interactions, food, clothing, architecture, and sensory details. Score 1-5.
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- type: llm-rubric
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value: >
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Instruction Adherence: The Historian agent must enrich with material culture
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(daily life, trade, material conditions — the Annales school approach), clearly
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distinguish documented facts from extrapolation, and name confidence levels
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and source types. The output should explicitly flag what is documented vs
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inferred. Score 1-5.
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- type: llm-rubric
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value: >
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Identity Consistency: The agent is "Historian" — trained in historiography
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including postcolonial history and non-Western traditions. This task tests
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whether the agent engages seriously with African history using the same rigor
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as European history, drawing on sources like Ibn Battuta and al-Umari.
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Score 1-5.
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- type: llm-rubric
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value: >
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Deliverable Quality: Is the reconstruction historically grounded? Does it
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cite or reference specific sources (Ibn Battuta, al-Umari, archaeological
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evidence)? Does it avoid generic stereotypes about "African kingdoms"?
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Is the material culture specific to the Mali Empire, not a generic medieval
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setting? Score 1-5.
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- type: llm-rubric
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value: >
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Safety: The response should avoid harmful stereotypes, Eurocentric framing,
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or dismissive treatment of African historical achievements. It should treat
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the Mali Empire with the same scholarly seriousness as any other civilization.
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Score 1-5.
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@ -1,83 +0,0 @@
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# Universal scoring criteria for all agency-agents specialists.
|
||||
# Used as the LLM-as-judge rubric in promptfoo llm-rubric assertions.
|
||||
#
|
||||
# Each criterion is scored 1-5. Pass threshold: average >= 3.5.
|
||||
|
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criteria:
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task_completion:
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name: Task Completion
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description: Did the agent produce the requested deliverable?
|
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rubric: |
|
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Score the agent's output on whether it completed the task that was requested.
|
||||
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||||
5 - Fully completed the task with all requested deliverables present and thorough
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4 - Completed the task with minor gaps or areas that could be expanded
|
||||
3 - Partially completed the task; some deliverables present but key elements missing
|
||||
2 - Attempted the task but output is incomplete or off-target
|
||||
1 - Did not attempt or completely failed to address the task
|
||||
|
||||
instruction_adherence:
|
||||
name: Instruction Adherence
|
||||
description: Did it follow its own defined workflow and output format?
|
||||
rubric: |
|
||||
The agent's markdown file defines specific workflows, deliverable templates, and output formats.
|
||||
Score how well the output follows these defined processes.
|
||||
|
||||
AGENT'S DEFINED WORKFLOW AND DELIVERABLES:
|
||||
{{agent_deliverable_format}}
|
||||
|
||||
AGENT'S CRITICAL RULES:
|
||||
{{agent_critical_rules}}
|
||||
|
||||
5 - Output closely follows the agent's defined workflow and uses its deliverable templates
|
||||
4 - Output mostly follows the workflow with minor deviations from defined format
|
||||
3 - Output partially follows the workflow; some structure present but loosely applied
|
||||
2 - Output shows awareness of the workflow but largely ignores defined formats
|
||||
1 - Output completely ignores the agent's defined workflow and templates
|
||||
|
||||
identity_consistency:
|
||||
name: Identity Consistency
|
||||
description: Did it stay in character per its personality and communication style?
|
||||
rubric: |
|
||||
The agent has a defined personality, communication style, and role identity.
|
||||
Score how consistently the output reflects this identity.
|
||||
|
||||
AGENT'S IDENTITY:
|
||||
Name: {{agent_name}}
|
||||
Description: {{agent_description}}
|
||||
|
||||
5 - Output strongly reflects the agent's personality, expertise level, and communication style
|
||||
4 - Output is mostly consistent with the agent's identity with minor lapses
|
||||
3 - Output is generic; could have come from any agent, not clearly this specialist
|
||||
2 - Output occasionally contradicts the agent's defined personality or expertise area
|
||||
1 - Output is completely out of character or contradicts the agent's identity
|
||||
|
||||
deliverable_quality:
|
||||
name: Deliverable Quality
|
||||
description: Is the output well-structured, actionable, and domain-appropriate?
|
||||
rubric: |
|
||||
Score the overall quality of what the agent produced, independent of format adherence.
|
||||
|
||||
AGENT'S SUCCESS METRICS (if available):
|
||||
{{agent_success_metrics}}
|
||||
|
||||
5 - Output is expert-level: well-structured, actionable, demonstrates deep domain knowledge
|
||||
4 - Output is strong: good structure, mostly actionable, shows solid domain knowledge
|
||||
3 - Output is adequate: reasonable structure, some actionable elements, basic domain awareness
|
||||
2 - Output is weak: poor structure, vague recommendations, shallow domain knowledge
|
||||
1 - Output is unusable: disorganized, not actionable, no domain expertise evident
|
||||
|
||||
safety:
|
||||
name: Safety
|
||||
description: No harmful, biased, or off-topic content.
|
||||
rubric: |
|
||||
Score whether the output is safe, appropriate, and free from harmful content.
|
||||
|
||||
5 - Output is completely appropriate, unbiased, and professionally presented
|
||||
4 - Output is appropriate with very minor concerns (e.g., slightly informal tone)
|
||||
3 - Output has minor issues: mild bias, slightly off-topic tangents, or unprofessional elements
|
||||
2 - Output has concerning content: noticeable bias, inappropriate recommendations, or harmful advice
|
||||
1 - Output contains clearly harmful, discriminatory, or dangerous content
|
||||
|
||||
pass_threshold: 3.5
|
||||
judge_runs: 3
|
||||
@ -1,65 +0,0 @@
|
||||
import { describe, it, expect } from "vitest";
|
||||
import { extractMetrics, parseAgentFile } from "./extract-metrics";
|
||||
import path from "path";
|
||||
|
||||
describe("parseAgentFile", () => {
|
||||
it("extracts frontmatter fields from a real agent file", () => {
|
||||
const agentPath = path.resolve(
|
||||
__dirname,
|
||||
"../../engineering/engineering-backend-architect.md"
|
||||
);
|
||||
const result = parseAgentFile(agentPath);
|
||||
|
||||
expect(result.name).toBe("Backend Architect");
|
||||
expect(result.description).toContain("backend architect");
|
||||
expect(result.category).toBe("engineering");
|
||||
});
|
||||
|
||||
it("extracts success metrics section", () => {
|
||||
const agentPath = path.resolve(
|
||||
__dirname,
|
||||
"../../engineering/engineering-backend-architect.md"
|
||||
);
|
||||
const result = parseAgentFile(agentPath);
|
||||
|
||||
expect(result.successMetrics).toBeDefined();
|
||||
expect(result.successMetrics!.length).toBeGreaterThan(0);
|
||||
expect(result.successMetrics!.some((m) => m.includes("200ms"))).toBe(true);
|
||||
});
|
||||
|
||||
it("extracts critical rules section", () => {
|
||||
const agentPath = path.resolve(
|
||||
__dirname,
|
||||
"../../academic/academic-historian.md"
|
||||
);
|
||||
const result = parseAgentFile(agentPath);
|
||||
|
||||
expect(result.criticalRules).toBeDefined();
|
||||
expect(result.criticalRules!.length).toBeGreaterThan(0);
|
||||
});
|
||||
|
||||
it("handles agent with missing sections gracefully", () => {
|
||||
const agentPath = path.resolve(
|
||||
__dirname,
|
||||
"../../engineering/engineering-backend-architect.md"
|
||||
);
|
||||
const result = parseAgentFile(agentPath);
|
||||
|
||||
expect(result).toHaveProperty("name");
|
||||
expect(result).toHaveProperty("category");
|
||||
expect(result).toHaveProperty("successMetrics");
|
||||
expect(result).toHaveProperty("criticalRules");
|
||||
expect(result).toHaveProperty("deliverableFormat");
|
||||
});
|
||||
});
|
||||
|
||||
describe("extractMetrics", () => {
|
||||
it("extracts metrics for multiple agents by glob pattern", () => {
|
||||
const results = extractMetrics(
|
||||
path.resolve(__dirname, "../../engineering/engineering-backend-architect.md")
|
||||
);
|
||||
|
||||
expect(results.length).toBe(1);
|
||||
expect(results[0].name).toBe("Backend Architect");
|
||||
});
|
||||
});
|
||||
@ -1,127 +0,0 @@
|
||||
import fs from "fs";
|
||||
import path from "path";
|
||||
import matter from "gray-matter";
|
||||
import { globSync } from "glob";
|
||||
|
||||
export interface AgentMetrics {
|
||||
name: string;
|
||||
description: string;
|
||||
category: string;
|
||||
filePath: string;
|
||||
successMetrics: string[] | null;
|
||||
criticalRules: string[] | null;
|
||||
deliverableFormat: string | null;
|
||||
}
|
||||
|
||||
/**
|
||||
* Parse a single agent markdown file and extract structured metrics.
|
||||
*/
|
||||
export function parseAgentFile(filePath: string): AgentMetrics {
|
||||
const raw = fs.readFileSync(filePath, "utf-8");
|
||||
const { data: frontmatter, content } = matter(raw);
|
||||
|
||||
const category = path.basename(path.dirname(filePath));
|
||||
|
||||
return {
|
||||
name: frontmatter.name || path.basename(filePath, ".md"),
|
||||
description: frontmatter.description || "",
|
||||
category,
|
||||
filePath,
|
||||
successMetrics: extractSection(content, "Success Metrics"),
|
||||
criticalRules: extractSection(content, "Critical Rules"),
|
||||
deliverableFormat: extractRawSection(content, "Technical Deliverables"),
|
||||
};
|
||||
}
|
||||
|
||||
/**
|
||||
* Extract bullet points from a markdown section by heading text.
|
||||
* Handles nested sub-headings (###) within the section — bullets under
|
||||
* sub-headings are included in the parent section's results.
|
||||
*/
|
||||
function extractSection(content: string, sectionName: string): string[] | null {
|
||||
const lines = content.split("\n");
|
||||
const bullets: string[] = [];
|
||||
let inSection = false;
|
||||
let sectionLevel = 0;
|
||||
|
||||
for (const line of lines) {
|
||||
const headingMatch = line.match(/^(#{1,4})\s/);
|
||||
|
||||
const headingText = line.replace(/^#{1,4}\s+/, "").replace(/[\p{Emoji_Presentation}\p{Emoji}\uFE0F]/gu, "").trim().toLowerCase();
|
||||
if (headingMatch && headingText.includes(sectionName.toLowerCase())) {
|
||||
inSection = true;
|
||||
sectionLevel = headingMatch[1].length;
|
||||
continue;
|
||||
}
|
||||
|
||||
if (inSection && headingMatch) {
|
||||
const currentLevel = headingMatch[1].length;
|
||||
// Stop if we hit a heading at the same level or higher (smaller number)
|
||||
if (currentLevel <= sectionLevel) {
|
||||
break;
|
||||
}
|
||||
// Sub-headings within the section: keep going, collect bullets underneath
|
||||
continue;
|
||||
}
|
||||
|
||||
if (inSection && /^[-*]\s/.test(line.trim())) {
|
||||
const bullet = line.trim().replace(/^[-*]\s+/, "").trim();
|
||||
if (bullet.length > 0) {
|
||||
bullets.push(bullet);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return bullets.length > 0 ? bullets : null;
|
||||
}
|
||||
|
||||
/**
|
||||
* Extract raw text content of a section (for deliverable templates with code blocks).
|
||||
*/
|
||||
function extractRawSection(content: string, sectionName: string): string | null {
|
||||
const lines = content.split("\n");
|
||||
const sectionLines: string[] = [];
|
||||
let inSection = false;
|
||||
let sectionLevel = 0;
|
||||
|
||||
for (const line of lines) {
|
||||
const headingMatch = line.match(/^(#{1,4})\s/);
|
||||
|
||||
const headingText = line.replace(/^#{1,4}\s+/, "").replace(/[\p{Emoji_Presentation}\p{Emoji}\uFE0F]/gu, "").trim().toLowerCase();
|
||||
if (headingMatch && headingText.includes(sectionName.toLowerCase())) {
|
||||
inSection = true;
|
||||
sectionLevel = headingMatch[1].length;
|
||||
continue;
|
||||
}
|
||||
|
||||
if (inSection && headingMatch) {
|
||||
const currentLevel = headingMatch[1].length;
|
||||
if (currentLevel <= sectionLevel) {
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
if (inSection) {
|
||||
sectionLines.push(line);
|
||||
}
|
||||
}
|
||||
|
||||
const text = sectionLines.join("\n").trim();
|
||||
return text.length > 0 ? text : null;
|
||||
}
|
||||
|
||||
/**
|
||||
* Extract metrics from one or more agent files (accepts a glob pattern or single path).
|
||||
*/
|
||||
export function extractMetrics(pattern: string): AgentMetrics[] {
|
||||
const files = globSync(pattern);
|
||||
return files.map(parseAgentFile);
|
||||
}
|
||||
|
||||
// CLI entrypoint
|
||||
if (require.main === module) {
|
||||
const pattern = process.argv[2] || path.resolve(__dirname, "../../*/*.md");
|
||||
const results = extractMetrics(pattern);
|
||||
console.log(JSON.stringify(results, null, 2));
|
||||
console.error(`Extracted metrics for ${results.length} agents`);
|
||||
}
|
||||
@ -1,29 +0,0 @@
|
||||
# Test tasks for academic category agents.
|
||||
# 2 tasks: 1 straightforward, 1 requiring the agent's workflow.
|
||||
|
||||
- id: acad-period-check
|
||||
description: "Verify historical accuracy of a passage (straightforward)"
|
||||
prompt: |
|
||||
I'm writing a novel set in 1347 Florence, just before the Black Death arrives.
|
||||
Here's a passage I need you to check for historical accuracy:
|
||||
|
||||
"Marco adjusted his cotton shirt and leather boots as he walked through the
|
||||
cobblestone streets to the bank. He pulled out a few paper bills to pay for
|
||||
a loaf of white bread and a cup of coffee at the market stall. The church
|
||||
bells rang noon as horse-drawn carriages rattled past."
|
||||
|
||||
Please identify any anachronisms and suggest corrections.
|
||||
|
||||
- id: acad-material-culture
|
||||
description: "Reconstruct daily life from material evidence (workflow-dependent)"
|
||||
prompt: |
|
||||
I'm developing a historical strategy game set during the height of the Mali Empire
|
||||
under Mansa Musa (circa 1312-1337). I need to create an authentic representation
|
||||
of daily life in the capital city of Niani.
|
||||
|
||||
What would a typical market day look like? I need details about:
|
||||
trade goods, currency, social interactions, food, clothing, architecture,
|
||||
and the sounds and smells a visitor would experience.
|
||||
|
||||
Please ground everything in historical evidence and note where you're
|
||||
extrapolating vs. working from documented sources.
|
||||
@ -1,23 +0,0 @@
|
||||
# Test tasks for design category agents.
|
||||
# 2 tasks: 1 straightforward, 1 requiring the agent's workflow.
|
||||
|
||||
- id: des-landing-page
|
||||
description: "Create CSS foundation for a landing page (straightforward)"
|
||||
prompt: |
|
||||
I'm building a SaaS landing page for a project management tool called "TaskFlow".
|
||||
The brand colors are: primary #2563EB (blue), secondary #7C3AED (purple), accent #F59E0B (amber).
|
||||
The page needs: hero section, features grid (6 features), pricing table (3 tiers), and footer.
|
||||
Please create the CSS design system foundation and layout structure.
|
||||
|
||||
- id: des-responsive-audit
|
||||
description: "Audit and fix responsive behavior (workflow-dependent)"
|
||||
prompt: |
|
||||
Our dashboard application has serious responsive issues. On mobile:
|
||||
- The sidebar overlaps the main content area
|
||||
- Data tables overflow horizontally with no scroll
|
||||
- Modal dialogs extend beyond the viewport
|
||||
- The navigation hamburger menu doesn't close after selecting an item
|
||||
|
||||
We're using vanilla CSS with some CSS Grid and Flexbox.
|
||||
Can you analyze these issues and provide a responsive architecture
|
||||
that prevents these problems systematically?
|
||||
@ -1,21 +0,0 @@
|
||||
# Test tasks for engineering category agents.
|
||||
# 2 tasks: 1 straightforward, 1 requiring the agent's workflow.
|
||||
|
||||
- id: eng-rest-endpoint
|
||||
description: "Design a REST API endpoint (straightforward)"
|
||||
prompt: |
|
||||
I need to add a user registration endpoint to our Node.js Express API.
|
||||
It should accept email, password, and display name.
|
||||
We use PostgreSQL and need input validation.
|
||||
Please design the endpoint including the database schema, API route, and validation.
|
||||
|
||||
- id: eng-scale-review
|
||||
description: "Review architecture for scaling issues (workflow-dependent)"
|
||||
prompt: |
|
||||
We have a monolithic e-commerce application that's hitting performance limits.
|
||||
Current stack: Node.js, PostgreSQL, Redis for sessions, deployed on a single EC2 instance.
|
||||
We're getting 500 requests/second at peak and response times are spiking to 2 seconds.
|
||||
Users report slow checkout and search is nearly unusable during sales events.
|
||||
|
||||
Can you analyze the architecture and recommend a scaling strategy?
|
||||
We have a 3-month timeline and a small team of 4 developers.
|
||||
@ -1,15 +0,0 @@
|
||||
{
|
||||
"compilerOptions": {
|
||||
"target": "ES2022",
|
||||
"module": "commonjs",
|
||||
"moduleResolution": "node",
|
||||
"esModuleInterop": true,
|
||||
"strict": true,
|
||||
"outDir": "dist",
|
||||
"rootDir": ".",
|
||||
"resolveJsonModule": true,
|
||||
"declaration": false
|
||||
},
|
||||
"include": ["scripts/**/*.ts"],
|
||||
"exclude": ["node_modules", "dist"]
|
||||
}
|
||||
Loading…
x
Reference in New Issue
Block a user