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Fresh export of all 20 diagrams as PNG and full HTML documentation
site after the Grand Unification UML sync.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
New BrowserRenderer class uses Playwright to render JavaScript-heavy
documentation sites (React, Vue SPAs) that return empty HTML shells
with requests.get(). Activated via --browser flag on web scraping.
- browser_renderer.py: Playwright wrapper with lazy browser launch,
auto-install Chromium on first use, context manager support
- doc_scraper.py: browser_mode config, _render_with_browser() helper,
integrated into scrape_page() and scrape_page_async()
- SPA detection warnings now suggest --browser flag
- Optional dep: pip install "skill-seekers[browser]"
- 14 real e2e tests (actual Chromium, no mocks)
- UML updated: Scrapers class diagram (BrowserRenderer + dependency),
Parsers (DoctorParser), Utilities (Doctor), Components, and new
Browser Rendering sequence diagram (#20)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
3 sequence diagrams (create command dispatch, GitHub+C3.x pipeline with
all 5 stages, MCP dual-path invocation), 2 activity diagrams (source
detection in correct code order, enhancement level flag mapping), and
1 component diagram with corrected runtime dependency arrows.
All diagrams cross-referenced against source code for accuracy.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Move UML/ directory and Architecture.md from Docs/ to docs/.
Rename Architecture.md to UML_ARCHITECTURE.md to avoid collision
with existing docs/ARCHITECTURE.md (docs organization file).
Update all references in README.md, CONTRIBUTING.md, CLAUDE.md,
and the architecture file itself.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
The parsers architecture is now fully documented in the StarUML project
(Docs/UML/skill_seekers.mdj) with the Parsers class diagram showing all
28 SubcommandParser subclasses.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* feat: add MiniMax AI as LLM platform adaptor
Original implementation by octo-patch in PR #318.
This commit includes comprehensive improvements and documentation.
Code Improvements:
- Fix API key validation to properly check JWT format (eyJ prefix)
- Add specific exception handling for timeout and connection errors
- Remove unused variable in upload method
Dependencies:
- Add MiniMax to [all-llms] extra group in pyproject.toml
Tests:
- Remove duplicate setUp method in integration test class
- Add 4 new test methods:
* test_package_excludes_backup_files
* test_upload_success_mocked (with OpenAI mocking)
* test_upload_network_error
* test_upload_connection_error
* test_validate_api_key_jwt_format
- Update test_validate_api_key_valid to use JWT format keys
- Fix test assertions for error message matching
Documentation:
- Create comprehensive MINIMAX_INTEGRATION.md guide (380+ lines)
- Update MULTI_LLM_SUPPORT.md with MiniMax platform entry
- Update 01-installation.md extras table
- Update INTEGRATIONS.md AI platforms table
- Update AGENTS.md adaptor import pattern example
- Fix README.md platform count from 4 to 5
All tests pass (33 passed, 3 skipped)
Lint checks pass
Co-authored-by: octo-patch <octo-patch@users.noreply.github.com>
* fix: improve MiniMax adaptor — typed exceptions, key validation, tests, docs
- Remove invalid "minimax" self-reference from all-llms dependency group
- Use typed OpenAI exceptions (APITimeoutError, APIConnectionError)
instead of string-matching on generic Exception
- Replace incorrect JWT assumption in validate_api_key with length check
- Use DEFAULT_API_ENDPOINT constant instead of hardcoded URLs (3 sites)
- Add Path() cast for output_path before .is_dir() call
- Add sys.modules mock to test_enhance_missing_library
- Add mocked test_enhance_success with backup/content verification
- Update test assertions for new exception types and key validation
- Add MiniMax to __init__.py docstrings (module, get_adaptor, list_platforms)
- Add MiniMax sections to MULTI_LLM_SUPPORT.md (install, format, API key,
workflow example, export-to-all)
Follows up on PR #318 by @octo-patch (feat: add MiniMax AI as LLM platform adaptor).
Co-Authored-By: Octopus <octo-patch@users.noreply.github.com>
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: octo-patch <octo-patch@users.noreply.github.com>
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Adds docs/BEST_PRACTICES.md — a comprehensive guide for creating
high-quality Claude skills. Covers SKILL.md structure, code examples,
prerequisites, troubleshooting, quality targets, and a real-world
before/after example (Grade F to Grade A). Addresses roadmap item I2.2.
Based on PR #206 by @jmagly from the AI Writing Guide project.
Fixes applied: updated outdated CLI command, fixed broken doc links.
Co-authored-by: jmagly <jmagly@users.noreply.github.com>
- Add docs/VIDEO_GUIDE.md (483 lines) — comprehensive guide covering
Quick Start, CLI reference, visual pipeline, AI enhancement, output
structure, time clipping, and troubleshooting
- Update README.md video section with new CLI examples (enhance,
clipping, vision OCR, re-build from JSON) and link to full guide
- Sync README.zh-CN.md with all video feature additions:
- Quick Start section: video commands
- Core Features: new video extraction feature list
- Installation table: video/video-full packages + GPU note
- Usage Examples: full video extraction subsection
- Documentation links: VIDEO_GUIDE.md reference
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Auto-detects NVIDIA (CUDA), AMD (ROCm), or CPU-only GPU and installs the
correct PyTorch variant + easyocr + all visual extraction dependencies.
Removes easyocr from video-full pip extras to avoid pulling ~2GB of wrong
CUDA packages on non-NVIDIA systems.
New files:
- video_setup.py (835 lines): GPU detection, PyTorch install, ROCm config,
venv checks, system dep validation, module selection, verification
- test_video_setup.py (60 tests): Full coverage of detection, install, verify
Updated docs: CHANGELOG, AGENTS.md, CLAUDE.md, README.md, CLI_REFERENCE,
FAQ, TROUBLESHOOTING, installation guide, video dependency plan
All 2523 tests passing (15 skipped).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Sync with latest development changes including ruff formatting,
bug fixes, and pinecone adaptor additions.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Add FALLBACK_MAIN_SELECTORS constant and _find_main_content() helper to
eliminate 3 duplicated fallback loops in doc_scraper.py
- Move link extraction before early return in extract_content() so links
are always discovered from the full page, not just main content
- Fix single-threaded dry-run to extract links from soup (full page)
instead of main element only — fixes reactflow.dev finding only 1 page
- Add link extraction to async dry-run path (was completely missing)
- Remove main_content from get_configuration() defaults so fallback logic
kicks in instead of a broad CSS comma selector matching body
- Smart create --config routing: peek at JSON to determine unified
(sources array → unified_scraper) vs simple (base_url → doc_scraper)
- Update docs/user-guide/02-scraping.md and docs/reference/CONFIG_FORMAT.md
to use unified config format (legacy format rejected since v2.11.0)
- Fix test_auto_fetch_enabled and test_mcp_validate_legacy_config
Closes#300
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Stage 1 quality improvements from the Arbitrary Limits & Dead Code audit:
Reference file truncation removed:
- codebase_scraper.py: remove code[:500] truncation at 5 locations — reference
files now contain complete code blocks for copy-paste usability
- unified_skill_builder.py: remove issues[:20], releases[:10], body[:500],
and code_snippet[:300] caps in reference files — full content preserved
Enhancement summarizer rewrite:
- enhance_skill_local.py: replace arbitrary [:5] code block cap with
character-budget approach using target_ratio * content_chars
- Fix intro boundary bug: track code block state so intro never ends
inside a code block, which was desynchronizing the parser
- Remove dead _target_lines variable (assigned but never used)
- Heading chunks now also respect the character budget
Hardcoded language fixes:
- unified_skill_builder.py: test examples use ex["language"] instead of
always "python" for syntax highlighting
- how_to_guide_builder.py: add language field to HowToGuide dataclass,
set from workflow at creation, used in AI enhancement prompt
Test fixes:
- test_enhance_skill_local.py: rename test to test_code_blocks_not_arbitrarily_capped,
fix assertion to count actual blocks (```count // 2), use target_ratio=0.9
Documentation:
- Add Stage 1 plan, implementation summary, review, and corrected docs
- Update CHANGELOG.md with all changes
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
All scrapers (scrape, github, analyze, pdf) now share a common argument
contract via add_all_standard_arguments() in arguments/common.py.
Universal flags (--dry-run, --verbose, --quiet, --name, --description,
workflow args) work consistently across all source types.
Previously, `create <url> --dry-run`, `create owner/repo --dry-run`,
and `create ./path --dry-run` would crash because sub-scrapers didn't
accept those flags. Also fixes main.py _handle_analyze_command() not
forwarding --dry-run, --preset, --quiet, --name, --description to
codebase_scraper.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
These files were incorrectly deleted — they have distinct content from
the *_DEPLOYMENT.md files (different structure, different focus, different
examples) and are not duplicates.
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
- Add YAML-based enhancement workflow presets shipped inside the package
(default, minimal, security-focus, architecture-comprehensive, api-documentation)
- Add `skill-seekers workflows` subcommand: list, show, copy, add, remove, validate
- copy/add/remove all accept multiple names/files in one invocation with partial-failure behaviour
- `add --name` override restricted to single-file operations
- Add 5 MCP tools: list_workflows, get_workflow, create_workflow, update_workflow, delete_workflow
- Fix: create command _add_common_args() now correctly forwards each --enhance-workflow
as a separate flag instead of passing the whole list as a single argument
- Update README: reposition as "data layer for AI systems" with AI Skills front and centre
- Update CHANGELOG, QUICK_REFERENCE, CLAUDE.md with workflow preset details
- 1,880+ tests passing
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Refactored all RAG adaptors (LangChain, LlamaIndex, Haystack, Weaviate, Chroma,
FAISS, Qdrant) to use existing helper methods from base.py, removing ~215 lines
of duplicate code (26% reduction).
Key improvements:
- All adaptors now use _format_output_path() for consistent path handling
- All adaptors now use _iterate_references() for reference file iteration
- Added _generate_deterministic_id() helper with 3 formats (hex, uuid, uuid5)
- 5 adaptors refactored to use unified ID generation
- Removed 6 unused imports (hashlib, uuid)
Benefits:
- DRY principles enforced across all RAG adaptors
- Single source of truth for common logic
- Easier maintenance and testing
- Consistent behavior across platforms
All 159 adaptor tests passing. Zero regressions.
Phase 1 of optional enhancements (Phases 2-5 pending).
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Complete summary of all critical and high priority fixes:
- Phase 1 (P0): Test coverage + CLI integration
- Phase 2 (P1): Code quality improvements
- Full verification and validation results
- Release readiness checklist for v2.10.0
Ready for production release.
- Filter out chunks smaller than min_chunk_size (default 100 tokens)
- Exception: Keep all chunks if entire document is smaller than target size
- All 15 tests passing (100% pass rate)
Fixes edge case where very small chunks (e.g., 'Short.' = 6 chars) were
being created despite min_chunk_size=100 setting.
Test: pytest tests/test_rag_chunker.py -v
Add comprehensive integration guides for 4 AI coding assistants:
## New Integration Guides (98KB total)
- docs/integrations/WINDSURF.md (20KB) - Windsurf IDE with .windsurfrules
- docs/integrations/CLINE.md (25KB) - Cline VS Code extension with MCP
- docs/integrations/CONTINUE_DEV.md (28KB) - Continue.dev for any IDE
- docs/integrations/INTEGRATIONS.md (25KB) - Comprehensive hub with decision tree
## Working Examples (3 directories, 11 files)
- examples/windsurf-fastapi-context/ - FastAPI + Windsurf automation
- examples/cline-django-assistant/ - Django + Cline with MCP server
- examples/continue-dev-universal/ - HTTP context server for all IDEs
## README.md Updates
- Updated tagline: Universal preprocessor for 10+ AI systems
- Expanded Supported Integrations table (7 → 10 platforms)
- Added 'AI Coding Assistant Integrations' section (60+ lines)
- Cross-links to all new guides and examples
## Impact
- Week 2 of ACTION_PLAN.md: 4/4 tasks complete (100%) ✅
- Total new documentation: ~3,000 lines
- Total new code: ~1,000 lines (automation scripts, servers)
- Integration coverage: LangChain, LlamaIndex, Pinecone, Cursor, Windsurf,
Cline, Continue.dev, Claude, Gemini, ChatGPT
## Key Features
- All guides follow proven 11-section pattern from CURSOR.md
- Real-world examples with automation scripts
- Multi-IDE consistency (Continue.dev works in VS Code, JetBrains, Vim)
- MCP integration for dynamic documentation access
- Complete troubleshooting sections with solutions
Positions Skill Seekers as universal preprocessor for ANY AI system.
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
- Remove SPYKE-related client documentation files
- Fix critical ruff linter errors:
- Remove unused 'os' import in test_analyze_e2e.py
- Remove unused 'setups' variable in test_test_example_extractor.py
- Prefix unused output_dir parameter in codebase_scraper.py
- Fix import sorting in test_integration.py
- Update CHANGELOG.md with comprehensive PR #272 feature documentation
These changes were part of PR #272 cleanup but didn't make it into the squash merge.
Complete implementation of C3.9, granular AI enhancement control, performance optimizations, and bug fixes.
Features:
- C3.9 Project Documentation Extraction (markdown files)
- Granular AI enhancement control (--enhance-level 0-3)
- C# test extraction support
- 6-12x faster LOCAL mode with parallel execution
- Auto-enhancement UX improvements
- LOCAL mode fallback for all AI enhancements
Bug Fixes:
- C# language support
- Config type field compatibility
- LocalSkillEnhancer import
Documentation:
- Updated CHANGELOG.md
- Updated CLAUDE.md
- Removed client-specific files
Tests: All 1,257 tests passing
Critical linter errors: Fixed
- Update Chinese README (README.zh-CN.md) with new preset flags
- Update docs/features/*.md (PATTERN_DETECTION, HOW_TO_GUIDES, BOOTSTRAP_SKILL_TECHNICAL)
- Update scripts/bootstrap_skill.sh to use 'skill-seekers analyze'
- Update scripts/skill_header.md command examples
- Update tests/test_bootstrap_skill.py assertions
- Fix CHANGELOG.md historical entry with correct command name
All references to 'skill-seekers-codebase' updated to 'skill-seekers analyze'
except where needed for backward compatibility (pyproject.toml, E2E tests).
Related to Phase 1 implementation from previous commits.
Merging with admin override due to known issues:
✅ **What Works**:
- GLM-4.7 Claude-compatible API support (correctly implemented)
- PDF scraper improvements (content truncation fixed, page traceability added)
- Documentation updates comprehensive
⚠️ **Known Issues (will be fixed in next commit)**:
1. Import bugs in 3 files causing UnboundLocalError (30 tests failing)
2. PDF scraper test expectations need updating for new behavior (5 tests failing)
3. test_godot_config failure (pre-existing, not caused by this PR - 1 test failing)
**Action Plan**:
Fixes for issues #1 and #2 are ready and will be committed immediately after merge.
Issue #3 requires separate investigation as it's a pre-existing problem.
Total: 36 failing tests, 35 will be fixed in next commit.
