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11 Commits
| Author | SHA1 | Message | Date | |
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Ryker_Feng
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41658c5ff4
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feat(skills): add skill review quality gate (#4037)
* feat(skills): add skill review quality gate * fix(skills): skip review eval fixtures in CI * fix(skills): ignore review eval fixtures in bundled scans * fix(skill-review): harden review gate boundaries * fix(skills): address skill review gate feedback |
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Xinmin Zeng
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4d660b202a
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feat(skills): bind request-scoped secrets for autonomously-invoked skills (A+) (#3938)
* feat(skills): bind request-scoped secrets for in-context (autonomously invoked) skills Extends the #3861 binding point A (slash-activation only) to A+: the injection set is recomputed on every model call from two unioned sources — the run's most recent slash activation (persisted on the run context so the tool loop keeps the binding) and skills the model actually loaded in this thread (ThreadState.skill_context), re-validated against the live registry each call. Authorization stays three-gated regardless of activation style: skill enabled by the operator, values supplied per-request by the caller in context.secrets (never persisted server-side, never from the host env), names declared in the skill's required-secrets frontmatter. Because the set is replaced per call, eviction from skill_context or a caller that stops supplying a value revokes injection on the next call. New frontmatter field secrets-autonomous (default true) lets a skill restrict binding to explicit slash activation; malformed values fail closed to false. Binding changes are recorded as a middleware:skill_secrets journal event carrying names only. Design informed by a survey of peer systems (Claude Code, Codex CLI, opencode, pi, deepagents, hermes-agent, QwenPaw) and specs (agentskills.io, MCP 2025-11-25): the industry trust boundary is enable-time consent plus caller-scoped credentials, not per-invocation ceremony; no surveyed system scopes secrets to an activation turn. Part of #3914 * refactor(skills): centralize secret context keys, document intentional per-call reload Review follow-ups (no behavior change): move the two private binding keys (__slash_skill_secret_source, __skill_secrets_binding_audit) into secret_context.py and add them to REDACTED_CONTEXT_KEYS so the redaction allowlist stays a complete guard even though both keys hold names only. Document why _in_context_secret_sources reloads skills every call rather than caching: load_skills re-reads enabled state so an operator disabling a skill revokes its binding on the next model call — an mtime cache would miss enable/disable toggles and keep injecting after a disable. * fix(skills): match in-context secret bindings by path only, never by name Review finding (confused deputy): _in_context_secret_sources fell back to name matching when a skill_context path did not resolve. DeerFlow lets a custom skill shadow a same-named public/legacy one (load_skills de-dupes by name, custom wins), so a thread that read public/foo could bind the custom foo's declared secrets although the custom skill was never loaded in the thread. The recent user-isolation path changes make by-path misses (and thus the dangerous fallback) more likely. Drop the by-name fallback: match strictly by the exact container file path the model read; an unresolved path simply does not bind (the safe direction). Regression tests cover the shadowing case and a stale path. Part of #3914 * fix(skills): resolve secret-binding sources via registry; strip caller __-keys Security review (willem-bd, #3938): 1. Forged `__slash_skill_secret_source` bypassed the enabled/allowlist/ secrets-autonomous gates. runtime.context is caller-mergeable, and the slash source was trusted as authoritative (its stored requirements were injected directly). Now the slash source records only the activated skill's canonical container path, and BOTH the slash and in-context sources resolve the live registry skill by normalized path each call (_resolve_registry_skill) — binding only that real, enabled, allowlisted skill's own declared secrets. A forged path resolves to nothing. As defense in depth, build_run_config strips caller-supplied __-prefixed context keys at the gateway boundary. 2. Malformed caller requirements crashed the run (unguarded tuple unpack / DoS). The middleware no longer unpacks caller-provided requirement data at all — declarations come from the registry — so a malformed source fails closed instead of raising. 3. Path-normalization asymmetry silently disabled in-context binding on a trailing-slash container_path config. Both the registry keys and the lookup path are now posixpath.normpath'd. Regression tests: forged source rejected, forged-but-real path ignores caller requirements + allowlist, malformed source fails closed, trailing- slash config binds, gateway strips __-keys. Part of #3914 * docs(skills): correct _SLASH_SECRET_SOURCE_KEY comment and note fail-closed trade-off Post-review cleanup: the key now stores only the canonical container path (the comment still described the pre-fix skill-name+requirements shape), and document that a transient registry-load failure fails closed (drops the binding for that call) rather than trusting stale data. --------- Co-authored-by: Willem Jiang <willem.jiang@gmail.com> |
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Tianye Song
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15454b6fec
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feat(skills): deferred skill discovery via describe_skill tool (#3775)
Replace the full-metadata <available_skills> system-prompt block with a compact <skill_index> (names only) and an on-demand describe_skill tool when skills.deferred_discovery: true (default: false / backward compat). New modules: - skills/catalog.py — SkillCatalog (immutable, searchable; select: has no cap, keyword/prefix search caps at MAX_RESULTS=5) - skills/describe.py — build_describe_skill_tool(catalog) closure; build_skill_search_setup() wires SkillSearchSetup into both the LangGraph agent factory (agent.py) and DeerFlowClient (client.py) Changes: - Skill @dataclass(frozen=True); allowed_tools/required_secrets list→tuple - Skill First prompt line gated on skill_names (deferred vs legacy wording) - get_skills_prompt_section: short-circuit storage on deferred path; merge user_id (upstream) + skill_names (this PR) params - describe_skill tool parameter named "name" (matches prompt wording) - select: branch removes [:MAX_RESULTS] cap (exact request, not ranking) - AGENTS.md: document deferred_discovery config field + new modules Co-authored-by: Willem Jiang <willem.jiang@gmail.com> |
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Xinmin Zeng
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09988caf95
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feat(skills): request-scoped secrets for skills (closes #3861) (#3871)
* feat(sandbox): per-call env injection + platform-secret scrubbing for skills Add an env parameter to Sandbox.execute_command (abstract + local + AIO) so request-scoped secrets can be injected into skill subprocesses, and scrub platform credentials (*KEY*/*SECRET*/*TOKEN*/*PASSWORD*/*CREDENTIAL*) from the inherited environment by default so scoped injection is not security theatre. LocalSandbox always passes an explicit scrubbed env; AioSandbox routes env-bearing commands through bash.exec(env=) on a fresh session and leaves the legacy persistent-shell path unchanged. Part of #3861. BEHAVIOR CHANGE: execute_command no longer inherits the full os.environ; Windows encoding tests updated to assert the scrubbed dict. * feat(skills): parse required-secrets frontmatter declaration Add SecretRequirement and Skill.required_secrets, and parse the required-secrets SKILL.md frontmatter field (a string list or {name, optional} mappings), dropping malformed entries with a warning so one bad declaration does not invalidate the skill. The declared name is both the context.secrets key and the env var injected at activation. Part of #3861. * feat(runtime): request-scoped secret carrier (context.secrets) Add SECRETS_CONTEXT_KEY + extract_request_secrets, centralising the context.secrets carrier contract. The existing context passthrough (build_run_config -> _build_runtime_context) already carries the sub-key to runtime.context without mirroring it into configurable; characterization tests lock that behaviour. Part of #3861. * feat(skills): inject declared secrets at slash-activation into bash env Binding point A: when a skill is slash-activated, SkillActivationMiddleware resolves its declared required-secrets against the request's context.secrets and writes the per-run injection set to runtime.context. The bash tool forwards that set to execute_command(env=). A skill cannot harvest a host platform credential (is_host_platform_secret guard, cf. GHSA-rhgp-j443-p4rf), and injected values are redacted from bash output (mask_secret_values) so an echoed secret never re-enters the prompt/trace. Part of #3861. * test(skills): lock the five secret leak surfaces + add trace redaction helper Regression tests assert the secret value is absent from all five surfaces: prompt (activation message), checkpoint (graph state vs context separation), audit (journal records names only), trace (metadata builder never copies context; never mirrored to configurable), and stdout (mask_secret_values). Add redact_secret_context_keys as a defensive helper for any context serialization. Part of #3861. * docs(backend): document request-scoped secrets for skills Add Request-Scoped Secrets subsection (Skills) + env policy note (Sandbox) and the execute_command(env=) signature change, per the doc-sync policy. Part of #3861. * fix(skills): close gaps found by end-to-end verification of request-scoped secrets Real-gateway e2e + independent review of #3861 surfaced three defects, now fixed: 1. Slash activation never fired in the live chain. InputSanitizationMiddleware wraps user input in BEGIN/END markers before SkillActivationMiddleware sees it, and the original text was only preserved when an upload or IM channel set it. For a plain text message the slash command became undetectable, so no secret was ever resolved. Fix: the sanitizer now setdefaults the pre-wrap text into ORIGINAL_USER_CONTENT_KEY (additive; sanitization behaviour unchanged), so slash activation works for all messages. Pre-existing latent bug surfaced here. 2. The raw request config (with context.secrets) was persisted to runs.kwargs_json and echoed by the run API (RunResponse.kwargs). Fix: redact_config_secrets() strips secret-bearing context keys from the persisted/echoed copy in start_run; the live config that drives the run keeps them. build_run_config now also sets configurable.thread_id on the context path (the checkpointer requires it). 3. Connection-string credentials (DATABASE_URL, REDIS_URL, SENTRY_DSN, GH_PAT, ...) were not scrubbed from the inherited sandbox env. Fix: env_policy adds a *DSN* pattern plus an explicit connection-string denylist (no blanket *URL* — benign service URLs stay readable). Verified end-to-end via a real gateway run (real LLM + skill activation + bash): the secret reaches the sandbox subprocess and appears in NONE of prompt, trace, checkpoint, audit, stdout, runs.kwargs_json, or the run API. Part of #3861. * docs(backend): document the env scrub, persistence redaction, and sanitizer interaction Sync the Request-Scoped Secrets section with the verification-driven fixes: inherited-env scrub (incl. connection-string denylist), run-record/run-API redaction as the 6th sealed leak surface, and the sanitizer preserving original content so slash activation fires. Part of #3861. * fix(skills): inject caller secret over scrubbed host value; drop redundant host-name guard A real-world demo (a skill calling a third-party cloud API with a request-scoped key) exposed that the is_host_platform_secret guard was both wrong and harmful: it refused to inject a caller-supplied secret whenever a same-named variable existed in the Gateway env — which is exactly the #3861 use case (a per-user key overriding a shared platform key). The guard was also redundant: build_sandbox_env already scrubs secret-looking names from the inherited env before injection, so a skill can never read a host credential — it only ever receives the caller's value. Remove the guard; the injected (caller) value simply wins over the scrubbed host value. Verified end-to-end: the agent called the real cloud API successfully with the caller's key, the host's same-named key was scrubbed and never used, and the caller's key leaked to none of the surfaces. Part of #3861. * fix(skills): address review on request-scoped secrets (#3861) Review fixes from PR #3871: - E2BSandbox.execute_command now accepts env/timeout and routes them to commands.run(envs=, timeout=). The bash tool passes env= unconditionally, so the prior signature (command only) raised TypeError on every e2b bash call and broke e2b deployments entirely. env=None stays backward-compatible. - SkillActivationMiddleware clears the active-secret set before resolving each activation, so a later skill in the same run never inherits an earlier skill's injection set (the #3861 contract: a skill only receives what the caller supplied AND that skill declared). - AioSandbox env path uses a dedicated _DEFAULT_HARD_TIMEOUT — bash.exec exposes no idle/no-change timeout, so the prior reuse of the legacy idle constant conflated wall-clock vs idle semantics. The env path also retries on the ErrorObservation signature now, sharing the legacy persistent-shell recovery contract. - mask_secret_values skips values below a minimum length floor so a short declared secret (e.g. "42") cannot shred unrelated bytes (exit codes, timestamps, sizes) of tool output. The secret is still injected into the subprocess; only the output mask skips it. session_id reuse on the env path is intentionally NOT added: a shared session could let request-scoped secrets ride the session env into later commands, which the SDK does not contractually forbid. The fresh-session choice matches the LocalSandbox model (each call is a fresh subprocess); the trade-off (consecutive env-bearing calls do not share cwd/venv/exports) is documented on _execute_with_env. |
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Huixin615
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89ae74d4f4
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fix(skills): surface offending line and quoting hint on SKILL.md YAML… (#3335)
* fix(skills): surface offending line and quoting hint on SKILL.md YAML errors
When a SKILL.md front-matter fails to parse, the existing log only
echoes PyYAML's raw message, leaving authors to grep the file for the
offending line. This is especially painful for the very common
LLM-authored mistake of an unquoted scalar containing ': '
(e.g. 'description: foo: bar'), which fails with
'mapping values are not allowed here' and silently drops the skill.
Enrich the error log with:
- the source line PyYAML pointed at via problem_mark
- a targeted, copy-pasteable quoting hint when (and only when) the
error is the well-known 'mapping values are not allowed' scanner
error on an unquoted value
The skill is still rejected (no semantics are guessed or rewritten);
only the diagnostic is improved.
Fixes #3333
* improve(skills): address CR feedback on SKILL.md YAML error diagnostics
Per review on #3335:
- Log the file line number (mark.line + 2) instead of the
front-matter-internal line number, so authors land on the right
row in their editor.
- Use exc.problem == "mapping values are not allowed here" for a
tighter match than substring-scanning str(exc).
- Preserve the offending key's leading whitespace in the quoting
hint so nested mappings stay nested when authors paste the fix
back.
- Rewrite the regression test to actually exercise the new
behaviour: PyYAML's own message already echoes the offending
line (and truncates it with "..."), so the old assertion
passed on main. New assertions pin (a) the file-line number,
(b) the full untruncated line, and (c) the copy-pasteable hint.
- Add a guard test for nested-key indentation so the
partition()/strip() shape cannot regress silently.
Refs #3333, #3335
* fix(skills): escape backslashes in YAML quoting hint
The hint emitted by _format_yaml_error previously escaped only double
quotes, so values containing backslashes (e.g. Windows paths like
C:\Temp or regex escapes like \d) produced a suggested scalar that
was either invalid YAML or silently re-interpreted by PyYAML's
double-quoted escape rules when pasted back. Escape order matters:
backslashes first, then double quotes.
Adds two regression tests covering Windows-path and regex-style
backslashes.
Address Copilot CR feedback on PR #3335.
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AochenShen99
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cef4224381
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fix(skills): enforce allowed-tools metadata (#2626)
* fix(skills): parse allowed-tools frontmatter * fix(skills): validate allowed-tools metadata * fix(skills): add shared allowed-tools policy * fix(subagents): enforce skill allowed-tools * fix(agent): enforce skill allowed-tools * refactor(skills): dedupe TypeVar and reuse cached enabled skills - Drop redundant module-level TypeVar in tool_policy; rely on PEP 695 syntax. - Expose get_cached_enabled_skills() and have the lead agent reuse it instead of synchronously rescanning skills on every request. * fix(agent): expose config-scoped skill cache * fix(subagents): pass filtered tools explicitly * fix(skills): clean allowed-tools policy feedback |
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Xun
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1ad1420e31
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refactor(skills): Unified skill storage capability (#2613) | ||
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Ansel
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6dce26a52e
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fix: resolve tool duplication and skill parser YAML inconsistencies (#1803) (#2107)
* Refactor tests for SKILL.md parser Updated tests for SKILL.md parser to handle quoted names and descriptions correctly. Added new tests for parsing plain and single-quoted names, and ensured multi-line descriptions are processed properly. * Implement tool name validation and deduplication Add tool name mismatch warning and deduplication logic * Refactor skill file parsing and error handling * Add tests for tool name deduplication Added tests for tool name deduplication in get_available_tools(). Ensured that duplicates are not returned, the first occurrence is kept, and warnings are logged for skipped duplicates. * Apply suggestions from code review Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com> * Update minimal config to include tools list * Update test for nonexistent skill file Ensure the test for nonexistent files checks for None. * Refactor tool loading and add skill management support Refactor tool loading logic to include skill management tools based on configuration and clean up comments. * Enhance code comments for tool loading logic Added comments to clarify the purpose of various code sections related to tool loading and configuration. * Fix assertion for duplicate tool name warning * Fix indentation issues in tools.py * Fix the lint error of test_tool_deduplication * Fix the lint error of tools.py * Fix the lint error * Fix the lint error * make format --------- Co-authored-by: Willem Jiang <willem.jiang@gmail.com> Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com> |
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Alian
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e97c8c9943
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fix(skills): support parsing multiline YAML strings in SKILL.md frontmatter (#1703)
* fix(skills): support parsing multiline YAML strings in SKILL.md frontmatter * test(skills): add tests for multiline YAML descriptions |
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moose-lab
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03b144f9c9
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fix: replace print() with logging across harness package (#1282)
Replace all bare print() calls with proper logging using Python's standard logging module across the deerflow harness package. Changes across 8 files (16 print statements replaced): - agents/middlewares/clarification_middleware.py: use logger.info/debug - agents/middlewares/memory_middleware.py: use logger.debug - agents/middlewares/thread_data_middleware.py: use logger.debug - agents/middlewares/view_image_middleware.py: use logger.debug - agents/memory/queue.py: use logger.info/debug/warning/error - agents/lead_agent/prompt.py: use logger.error - skills/loader.py: use logger.warning - skills/parser.py: use logger.error Each file follows the established codebase convention: import logging logger = logging.getLogger(__name__) Log levels chosen based on message semantics: - debug: routine operational details (directory creation, timer resets) - info: significant state changes (memory queued, updates processed) - warning: recoverable issues (config load failures, skipped updates) - error: unexpected failures (parsing errors, memory update errors) Note: client.py is intentionally excluded as it uses print() for CLI output, which is the correct behavior for a command-line client. Co-authored-by: moose-lab <moose-lab@users.noreply.github.com> |
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DanielWalnut
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76803b826f
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refactor: split backend into harness (deerflow.*) and app (app.*) (#1131)
* refactor: extract shared utils to break harness→app cross-layer imports Move _validate_skill_frontmatter to src/skills/validation.py and CONVERTIBLE_EXTENSIONS + convert_file_to_markdown to src/utils/file_conversion.py. This eliminates the two reverse dependencies from client.py (harness layer) into gateway/routers/ (app layer), preparing for the harness/app package split. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * refactor: split backend/src into harness (deerflow.*) and app (app.*) Physically split the monolithic backend/src/ package into two layers: - **Harness** (`packages/harness/deerflow/`): publishable agent framework package with import prefix `deerflow.*`. Contains agents, sandbox, tools, models, MCP, skills, config, and all core infrastructure. - **App** (`app/`): unpublished application code with import prefix `app.*`. Contains gateway (FastAPI REST API) and channels (IM integrations). Key changes: - Move 13 harness modules to packages/harness/deerflow/ via git mv - Move gateway + channels to app/ via git mv - Rename all imports: src.* → deerflow.* (harness) / app.* (app layer) - Set up uv workspace with deerflow-harness as workspace member - Update langgraph.json, config.example.yaml, all scripts, Docker files - Add build-system (hatchling) to harness pyproject.toml - Add PYTHONPATH=. to gateway startup commands for app.* resolution - Update ruff.toml with known-first-party for import sorting - Update all documentation to reflect new directory structure Boundary rule enforced: harness code never imports from app. All 429 tests pass. Lint clean. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * chore: add harness→app boundary check test and update docs Add test_harness_boundary.py that scans all Python files in packages/harness/deerflow/ and fails if any `from app.*` or `import app.*` statement is found. This enforces the architectural rule that the harness layer never depends on the app layer. Update CLAUDE.md to document the harness/app split architecture, import conventions, and the boundary enforcement test. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * feat: add config versioning with auto-upgrade on startup When config.example.yaml schema changes, developers' local config.yaml files can silently become outdated. This adds a config_version field and auto-upgrade mechanism so breaking changes (like src.* → deerflow.* renames) are applied automatically before services start. - Add config_version: 1 to config.example.yaml - Add startup version check warning in AppConfig.from_file() - Add scripts/config-upgrade.sh with migration registry for value replacements - Add `make config-upgrade` target - Auto-run config-upgrade in serve.sh and start-daemon.sh before starting services - Add config error hints in service failure messages Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix comments * fix: update src.* import in test_sandbox_tools_security to deerflow.* Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix: handle empty config and search parent dirs for config.example.yaml Address Copilot review comments on PR #1131: - Guard against yaml.safe_load() returning None for empty config files - Search parent directories for config.example.yaml instead of only looking next to config.yaml, fixing detection in common setups Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix: correct skills root path depth and config_version type coercion - loader.py: fix get_skills_root_path() to use 5 parent levels (was 3) after harness split, file lives at packages/harness/deerflow/skills/ so parent×3 resolved to backend/packages/harness/ instead of backend/ - app_config.py: coerce config_version to int() before comparison in _check_config_version() to prevent TypeError when YAML stores value as string (e.g. config_version: "1") - tests: add regression tests for both fixes Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix: update test imports from src.* to deerflow.*/app.* after harness refactor Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com> |