* fix(video): forward --aspect-ratio into the Gemini Veo request
The video-generation skill accepts --aspect-ratio and passes it into
generate_video(), but the Gemini branch drops the value:
_generate_video_gemini has no aspect_ratio parameter and builds the
predictLongRunning body with only instances, so every Veo request runs
at the default ratio regardless of the CLI flag. Forward the value as
parameters.aspectRatio and cover it with a monkeypatch regression test
that captures the outgoing request body.
* fix(skills): avoid false credential findings in video generation
---------
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
* feat(skills): per-user skill isolation (#2905)
Implement user-scoped skill storage that isolates custom skills between
users while sharing public skills globally.
Key changes:
- Add UserScopedSkillStorage class for per-user custom skill directories
- Introduce get_or_new_user_skill_storage() factory with user_id context
- Auth middleware sets effective_user_id for request-scoped storage
- Agent/prompt/middleware now use user-scoped storage and prompt cache
- Sandbox mounts user-scoped skill directories for search/read tools
- Add validate_skill_file_path() to SkillStorage for path security
- Migration script supports --all-users bulk migration
- Frontend: add editable field to Skill type, error check in enableSkill
- All skill categories can be toggled (custom skills default to enabled)
- Update skill-creator SKILL.md with isolation-aware instructions
Tests:
- Add test_user_scoped_skill_storage.py (new)
- Update all existing skill tests for user-scoped storage
- Update sandbox, client, and router tests
* fix(skills): address second-round PR review feedback (#3889)
- P1-1: restrict legacy skill mount to users without custom skills
- P1-2: fail-closed for _is_disabled_skill_path (OSError → return True)
- P2-1: AND-merge global extensions_config skill disabled state
- P2-2: atomic write for _skill_states.json (mkstemp + replace)
- P2-3: normalize X-DeerFlow-Owner-User-Id in trusted boundary
- P2-4: LRU-bounded _enabled_skills_by_config_cache (OrderedDict, maxsize=256)
- P2-5: clear global prompt cache on PUBLIC skill toggle
- P2-6: invalidate skill caches on client.update_skill
* fix(tests): correct tool policy test after merge
* fix(skills): use DEFAULT_SKILLS_CONTAINER_PATH in UserScopedSkillStorage
The "/mnt/skills" literal in UserScopedSkillStorage.__init__ triggers
test_skill_container_path_defaults::test_mnt_skills_literal_is_owned_by_skill_constants_module
on CI. Migrate the default to the existing deerflow.constants constant,
matching the pattern already used by LocalSkillStorage, SkillStorage, and
the durable/tool_error middlewares.
---------
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
* docs(spec): MiniMax integration for generation skills + new music skill
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* docs(plan): MiniMax generation providers implementation plan
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* test(skills): add importlib loader + FakeResp for skill tests
* test(skills): register loaded module in sys.modules; raise requests.HTTPError in FakeResp
* feat(image-generation): add MiniMax provider with env auto-detect
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* refactor(image-generation): guard unknown provider, derive ref MIME, strengthen tests
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* feat(video-generation): add MiniMax provider with async poll/download
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* refactor(video-generation): surface base_resp errors while polling; add timeout test
* feat(podcast-generation): add MiniMax t2a_v2 provider with env auto-detect
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* refactor(podcast-generation): restore TTS credential guard; add volcengine + voice tests
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* feat(music-generation): new MiniMax music skill via skill-creator
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* refactor(music-generation): treat empty lyrics as absent; test no-audio-data path
* refactor(skills): add request timeouts to MiniMax network calls
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* Potential fix for pull request finding 'Explicit returns mixed with implicit (fall through) returns'
Co-authored-by: Copilot Autofix powered by AI <223894421+github-code-quality[bot]@users.noreply.github.com>
* fix(models): strip inconsistent user-message names for MiniMax chat
DeerFlow middlewares tag user messages with provenance names (user-input, summary, loop_warning); langchain serializes them into the OpenAI-compatible payload and MiniMax rejects mismatched user-message names with "user name must be consistent (2013)". PatchedChatMiniMax now drops the per-message name from user-role messages. Point the config.example MiniMax models at PatchedChatMiniMax so they also get reasoning_content mapping.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* feat(image-generation): MiniMax sends JSON prompt field, guard 1500-char limit
MiniMax image-01 takes one text string capped at 1500 chars, but the skill was sending the whole structured JSON. The MiniMax provider now extracts the JSON `prompt` field (relying on prompt_optimizer to expand it) and fails fast with a clear error before calling the API when that field exceeds 1500 chars. Authoring stays provider-agnostic; Gemini still receives the full JSON.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* feat(podcast-generation): per-provider TTS concurrency and retry/backoff
Each TTS provider owns its concurrency internally — MiniMax runs single-threaded to reduce rate-limit failures, Volcengine keeps 4 workers — with automatic retry and backoff on transient HTTP and base_resp errors. No caller-facing concurrency knob.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* fix(skills): address Copilot review comments on generation skills
- video: add raise_for_status + timeout to the Gemini download/POST/poll calls so non-2xx responses surface as clear HTTP errors instead of JSON/KeyError or hangs
- video: check the task Fail status before the generic base_resp check so the failure keeps its task_id context
- video/image: create the output file parent directory before writing (matching music-generation) so nested output paths do not raise FileNotFoundError
- music: require a non-empty prompt and fail fast with ValueError instead of sending an empty prompt to the API
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* fix(scripts): reclaim dev ports across worktrees in make stop/dev
All deer-flow worktrees (main checkout + linked worktrees) hardcode the same dev ports (8001/3000/2026), so a service started from any worktree must be reclaimable from another. stop_all now resolves the set of worktree roots (DEERFLOW_ROOTS) and treats a process as deer-flow-owned when its open files live under any of them. It also force-kills survivors on 2026 alongside 8001/3000, fixing `make dev` aborting on the nginx port preflight when a prior nginx lingered on 2026.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* fix(view-image): hide the injected image-context message from the UI
ViewImageMiddleware injects a HumanMessage (text + base64 images) so the vision model can see viewed images, but it was the only internal injector that set neither hide_from_ui nor a hidden name, so it leaked into the chat UI (and IM channels) as a user bubble reading "Here are the images you've viewed:". Mark it with additional_kwargs={"hide_from_ui": True}, matching todo/dynamic_context injections, which the frontend isHiddenFromUIMessage and the channel sender already honor. The model still receives the full content.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* fix(minimax): mark M2.7 models as text-only (no vision)
MiniMax M2.7 / M2.7-highspeed do not support vision; only M3 does. The
provider config asserted vision support for M2.7 in four places.
- config.example.yaml: 4 M2.7 entries -> supports_vision: false
- backend/docs/CONFIGURATION.md: M2.7 + highspeed -> supports_vision: false
- wizard: add LLMProvider.model_vision_overrides + extra_config_for() so
selecting an M2.7 model writes supports_vision: false while M3 (default)
keeps vision; wire it through setup_wizard.py
- tests: M2.7-highspeed fixture -> supports_vision=False; add
test_minimax_vision_is_per_model
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
Co-authored-by: Copilot Autofix powered by AI <223894421+github-code-quality[bot]@users.noreply.github.com>
* fix(skills): validate bundled SKILL.md front-matter in CI (fixes#2443)
Adds a parametrized backend test that runs `_validate_skill_frontmatter`
against every bundled SKILL.md under `skills/public/`, so a broken
front-matter fails CI with a per-skill error message instead of
surfacing as a runtime gateway-load warning.
The new test caught two pre-existing breakages on `main` and fixes them:
* `bootstrap/SKILL.md`: the unquoted description had a second `:` mid-line
("Also trigger for updates: ..."), which YAML parses as a nested mapping
("mapping values are not allowed here"). Rewrites the description as a
folded scalar (`>-`), which preserves the original wording (including the
embedded colon, double quotes, and apostrophes) without further escaping.
This complements PR #2436 (single-file colon→hyphen patch) with a more
general convention that survives future edits.
* `chart-visualization/SKILL.md`: used `dependency:` which is not in
`ALLOWED_FRONTMATTER_PROPERTIES`. Renamed to `compatibility:`, the
documented field for "Required tools, dependencies" per skill-creator.
No code reads `dependency` (verified by grep across backend/).
* Apply suggestions from code review
Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
* Fix the lint error
---------
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
- Remove f-string prefix on 7 strings with no placeholders (F541)
in analyze.py, aggregate_benchmark.py, run_loop.py, generate_review.py
- Remove unused `os` import in quick_validate.py (F401)
Found by ruff via HUMMBL Arbiter (https://hummbl.io/audit).
* test(skills): add trigger eval set for systematic-literature-review skill
20 eval queries (10 should-trigger, 10 should-not-trigger) for use with
skill-creator's run_eval.py. Includes real-world SLR queries contributed
by @VANDRANKI (issue #1862 author) and edge cases for routing
disambiguation with academic-paper-review.
* test(skills): add grader expectations for SLR skill evaluation
5 eval cases with 39 expectations covering:
- Standard SLR flow (APA/BibTeX/IEEE format selection)
- Keyword extraction and search behavior
- Subagent dispatch for metadata extraction
- Report structure (themes, convergences, gaps, per-paper annotations)
- Negative case: single-paper routing to academic-paper-review
- Edge case: implicit SLR without explicit keywords
* refactor(skills): shorten SLR description for better trigger rate
Reduce description from 833 to 344 chars. Key changes:
- Lead with "systematic literature review" as primary trigger phrase
- Strengthen single-paper exclusion: "Not for single-paper tasks"
- Remove verbose example patterns that didn't improve routing
Tested with run_eval.py (10 runs/query):
- False positive "best paper on RL": 67% → 20% (improved)
- True positive explicit SLR query: ~30% (unchanged)
Low recall is a routing-layer limitation, not a description issue —
see PR description for full analysis.
* Potential fix for pull request finding
Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
* feat(skills): add systematic-literature-review skill for multi-paper SLR workflows
Adds a new skill that produces a structured systematic literature review (SLR)
across multiple academic papers on a topic. Addresses #1862 with a pure skill
approach: no new tools, no architectural changes, no new dependencies.
Skill layout:
- SKILL.md — 4+1 phase workflow (plan, search, extract, synthesize, present)
- scripts/arxiv_search.py — arXiv API client, stdlib only, with a
requests->urllib fallback shim modeled after github-deep-research's
github_api.py
- templates/{apa,ieee,bibtex}.md — citation format templates selected
dynamically in Phase 4, mirroring podcast-generation's templates/ pattern
Design notes:
- Multi-paper synthesis uses the existing `task` tool to dispatch extraction
subagents in parallel. SKILL.md's Phase 3 includes a fixed decision table
for batch splitting to respect the runtime's MAX_CONCURRENT_SUBAGENTS = 3
cap, and explicitly tells the agent to strip the "Task Succeeded. Result: "
prefix before parsing subagent JSON output.
- arXiv only, by design. Semantic Scholar and PubMed adapters would push the
scope toward a standalone MCP server (see #933) and are intentionally out
of scope for this skill.
- Coexists with the existing `academic-paper-review` skill: this skill does
breadth-first synthesis across many papers, academic-paper-review does
single-paper peer review. The two are routed via distinct triggers and
can compose (SLR on many + deep review on 1-2 important ones).
- Hard upper bound of 50 papers, tied to the Phase 3 concurrency strategy.
Larger surveys degrade in synthesis quality and are better split by
sub-topic.
BibTeX template explicitly uses @misc for arXiv preprints (not @article),
which is the most common mistake when generating BibTeX for arXiv papers.
arxiv_search.py was smoke-tested end-to-end against the live arXiv API with
two query shapes (relevance sort, submittedDate sort with category filter);
all returned JSON fields parse correctly (id normalization, Atom namespace
handling, URL encoding for multi-word queries).
* fix(skills): prevent LLM from saving intermediate search results to file
Adds an explicit "do not save" instruction at the end of Phase 2.
Observed during Test 1 with DeepSeek: the model saved search results
to a markdown file before proceeding to Phase 3, wasting 2-3 tool call
rounds and increasing the risk of hitting the graph recursion limit.
The search JSON should stay in context for Phase 3, not be persisted.
* fix(skills): use relevance+start-date instead of submittedDate sorting
Test 2 revealed that arXiv's submittedDate sorting returns the most
recently submitted papers in the category regardless of query relevance.
Searching "diffusion models" with sortBy=submittedDate in cs.CV returned
papers on spatial memory, Navier-Stokes, and photon-counting CT — none
about diffusion models. The LLM then retried with 4 different queries,
wasting tool calls and approaching the recursion limit.
Fix: always sort by relevance; when the user wants "recent" papers,
combine relevance sorting with --start-date to constrain the time window.
Also add an explicit "run the search exactly once" instruction to prevent
the retry loop.
* fix(skills): wrap multi-word arXiv queries in double quotes for phrase matching
Without quotes, `all:diffusion model` is parsed by arXiv's Lucene as
`all:diffusion OR model`, pulling in unrelated papers from physics
(thermal diffusion) and other fields. Wrapping in double quotes forces
phrase matching: `all:"diffusion model"`.
Also fixes date filtering: the previous bug caused 2011 papers to appear
in results despite --start-date 2024-04-09, because the unquoted query
words were OR'd with the date constraint.
Verified: "diffusion models" --category cs.CV --start-date 2024-04-09
now returns only relevant diffusion model papers published after April
2024.
* fix(skills): add query phrasing guide and enforce subagent delegation
Two fixes from Test 2 observations with DeepSeek:
1. Query phrasing: add a table showing good vs bad query examples.
The script wraps multi-word queries in double quotes for phrase
matching, so long queries like "diffusion models in computer vision"
return 0 results. Guide the LLM to use 2-3 core keywords + --category
instead.
2. Subagent enforcement: DeepSeek was extracting metadata inline via
python -c scripts instead of using the task tool. Strengthen Phase 3
to explicitly name the task tool, say "do not extract metadata
yourself", and explain why (token budget, isolation). This is more
direct than the previous natural-language-only approach while still
providing the reasoning behind the constraint.
* fix(skills): strengthen search keyword guidance and subagent enforcement
Address two issues found during end-to-end testing with DeepSeek:
1. Search retry: LLM passed full topic descriptions as queries (e.g.
"diffusion models in computer vision"), which returned 0 results due
to exact phrase matching and triggered retries. Added explicit
instruction to extract 2-3 core keywords before searching.
2. Subagent bypass: LLM used python -c to extract metadata instead of
dispatching via task tool. Added explicit prohibition list (python -c,
bash scripts, inline extraction) with ❌ markers for clarity.
* fix(skills): address Copilot review feedback on SLR skill
- Fix legacy arXiv ID parsing: preserve archive prefix for pre-2007
papers (e.g. hep-th/9901001 instead of just 9901001)
- Fix phase count: "four phases" -> "five phases"
- Add subagent_enabled prerequisite note to SKILL.md Notes section
- Remove PR-specific references ("PR 1") from ieee.md and bibtex.md
templates, replace with workflow-scoped wording
- Fix script header: "stdlib only" -> "no additional dependencies
required", fix relative path to github_api.py reference
- Remove reference to non-existent docs/enhancement/ path in header
* Apply suggestions from code review
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
---------
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Add three new public skills to enhance DeerFlow's content creation capabilities:
- **academic-paper-review**: Structured peer-review-quality analysis of
research papers following top-venue review standards (NeurIPS, ICML, ACL).
Covers methodology assessment, contribution evaluation, literature
positioning, and constructive feedback with a 3-phase workflow.
- **code-documentation**: Professional documentation generation for software
projects, including README generation, API reference docs, architecture
documentation with Mermaid diagrams, and inline code documentation
supporting Python, TypeScript, Go, Rust, and Java conventions.
- **newsletter-generation**: Curated newsletter creation with research
workflow, supporting daily digest, weekly roundup, deep-dive, and industry
briefing formats. Includes audience-specific tone adaptation and
multi-source content curation.
All skills:
- Follow the existing SKILL.md frontmatter convention (name + description)
- Pass the official _validate_skill_frontmatter() validation
- Use hyphen-case naming consistent with existing skills
- Contain only allowed frontmatter properties
- Include comprehensive examples, quality checklists, and output templates
* feat: Add github PAT configs, allowing larger github API rates.
* Update comment to English for better clarity
* fix: Remove unused config lines in config.example.yaml and unreferenced declarations in app_config. Fix lint issues and update documentation.
* fix: Remove unused imports, and passed the ruff check.
---------
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
* fix: add error handling for podcast generation failures
When TTS processing fails, the system was generating 0-second audio files
without any error indication. This fix adds:
1. Track failed TTS lines and log warning with indices
2. Raise ValueError when all TTS generation fails with helpful message
3. Check for empty audio output in mix_audio and raise error
4. Log success/failure ratio for debugging
Fixes#30
* fix: address Copilot review feedback
- Use `not audio` to catch both None and empty bytes
- Log failed lines with 1-based indices for user-friendly output
- Handle empty script case with clear error message
- Validate env vars before ThreadPoolExecutor for fast-fail on config errors
---------
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
* feat: add citation/reference support to deep research reports (#1141)
- Enhance lead agent system prompt with mandatory citation requirements
after web_search/web_fetch tool usage
- Add citation examples and best practices to GitHub Deep Research skill
- Add citation hints to report template (Executive Summary, Key Analysis)
- Style regular markdown links in frontend for visual distinction
(color, underline, hover effect)
- Fix TitleMiddleware being registered when title generation is disabled
* fix: address PR review comments
- Revert TitleMiddleware conditional registration (agent.py) to avoid
sync/async incompatibility with DeerFlowClient
- Fix markdown link rendering: merge classNames instead of overwriting,
only set target=_blank for external http(s) URLs
- Remove unrelated package.json/pnpm-lock.yaml changes
* fix: use plain markdown links in Sources section for cleaner rendering
Inline citations in report body use [citation:Title](URL) for pill/badge style.
Sources section uses plain [Title](URL) for simple underlined link style.
* fix(frontend): render plain links as underlined text in artifact markdown
Only links with citation: prefix render as Badge pills.
Regular links in Sources section now render as underlined text links.
---------
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
* feat: add claude-to-deerflow skill for DeerFlow API integration
Add a new skill that enables Claude Code to interact with the DeerFlow
AI agent platform via its HTTP API, including chat streaming and status
checking capabilities.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: fix telegram channel
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
* feat: add IM channels system for Feishu, Slack, and Telegram integration
Bridge external messaging platforms to DeerFlow via LangGraph Server with
async message bus, thread management, and per-channel configuration.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: address review comments on IM channels system
Fix topic_id handling in store remove/list_entries and manager commands,
correct Telegram reply threading, remove unused imports/variables, update
docstrings and docs to match implementation, and prevent config mutation.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* update skill creator
* fix im reply text
* fix comments
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
* feat: add agent management functionality with creation, editing, and deletion
* feat: enhance agent creation and chat experience
- Added AgentWelcome component to display agent description on new thread creation.
- Improved agent name validation with availability check during agent creation.
- Updated NewAgentPage to handle agent creation flow more effectively, including enhanced error handling and user feedback.
- Refactored chat components to streamline message handling and improve user experience.
- Introduced new bootstrap skill for personalized onboarding conversations, including detailed conversation phases and a structured SOUL.md template.
- Updated localization files to reflect new features and error messages.
- General code cleanup and optimizations across various components and hooks.
* Refactor workspace layout and agent management components
- Updated WorkspaceLayout to use useLayoutEffect for sidebar state initialization.
- Removed unused AgentFormDialog and related edit functionality from AgentCard.
- Introduced ArtifactTrigger component to manage artifact visibility.
- Enhanced ChatBox to handle artifact selection and display.
- Improved message list rendering logic to avoid loading states.
- Updated localization files to remove deprecated keys and add new translations.
- Refined hooks for local settings and thread management to improve performance and clarity.
- Added temporal awareness guidelines to deep research skill documentation.
* feat: refactor chat components and introduce thread management hooks
* feat: improve artifact file detail preview logic and clean up console logs
* feat: refactor lead agent creation logic and improve logging details
* feat: validate agent name format and enhance error handling in agent setup
* feat: simplify thread search query by removing unnecessary metadata
* feat: update query key in useDeleteThread and useRenameThread for consistency
* feat: add isMock parameter to thread and artifact handling for improved testing
* fix: reorder import of setup_agent for consistency in builtins module
* feat: append mock parameter to thread links in CaseStudySection for testing purposes
* fix: update load_agent_soul calls to use cfg.name for improved clarity
* fix: update date format in apply_prompt_template for consistency
* feat: integrate isMock parameter into artifact content loading for enhanced testing
* docs: add license section to SKILL.md for clarity and attribution
* feat(agent): enhance model resolution and agent configuration handling
* chore: remove unused import of _resolve_model_name from agents
* feat(agent): remove unused field
* fix(agent): set default value for requested_model_name in _resolve_model_name function
* feat(agent): update get_available_tools call to handle optional agent_config and improve middleware function signature
---------
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
Unifies market analysis, data analysis, and consulting reporting into a comprehensive consulting-analysis skill, enabling a two-phase workflow from analysis framework design to professional report generation. Introduces a DuckDB-based data analysis utility for Excel/CSV files and a chart-visualization skill with a flexible JS interface and extensive chart type documentation. Removes the legacy market analysis skill to streamline report generation and improve extensibility for consulting and data-driven workflows.
Explicitly prohibit parallel image generation to ensure each slide
can use the previous slide as a reference image for visual consistency.
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Add "present_files" to disallowed_tools for bash and general-purpose
subagents to prevent them from presenting files directly. Also add the
new market-analysis skill for generating consulting-grade reports.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add accurate token counting using tiktoken library and significantly enhance
memory update prompts with detailed section guidelines, multilingual support,
and improved fact extraction. Update deep-research skill to be more proactive
for research queries.
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
- Add model-aware vision tool loading based on supports_vision flag
- Move view_image_tool from config to builtin tools for dynamic inclusion
- Add timeout to image search to prevent hanging requests
- Optimize image search results format using thumbnails
- Add image validation for reference images in generation
- Improve error handling with detailed messages
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Update presentation generation with contemporary design styles
(glassmorphism, dark-premium, neo-brutalist, etc.) and add a new
deep-research skill to guide thorough web research before content
generation tasks.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Creates presentations by generating AI images for each slide and composing
them into PPTX files. Features include:
- Multiple presentation styles (business, academic, minimal, keynote, creative)
- Visual consistency through reference image chaining (each slide uses the
previous slide as reference)
- Speaker notes from presentation plan
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Use ThreadPoolExecutor to generate audio for multiple script lines
concurrently, significantly speeding up podcast generation.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Remove LLM script generation from Python script, model now generates
JSON script directly (similar to image-generation skill)
- Add --transcript-file option to generate markdown transcript
- Add optional "title" field in JSON for transcript heading
- Remove dependency on OPENAI_API_KEY for podcast generation
- Update SKILL.md with new workflow and JSON format documentation
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Add podcast-generation skill for creating tech explainer podcasts
- Include generate.py script with TTS synthesis capabilities
- Add tech-explainer template for structured podcast content
- Increase sandbox command timeout from 30s to 600s to support
longer-running skill scripts
- Add environment field to sandbox config for injecting env vars into container
- Support $VAR syntax to resolve values from host environment variables
- Refactor frontend API modules to use centralized getBackendBaseURL()
- Improve Doraemon skill with explicit input/output path arguments
- Add .env.example file
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Add type, url, and headers fields to MCP server config
- Update MCP client to handle stdio, sse, and http transports
- Add todos field to ThreadState
- Add Deerflow branding requirement to frontend-design skill
- Update extensions_config.example.json with SSE/HTTP examples
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
