Cangjie Skill
D 59 completed
Other
unknown / markdown · tiny
22
Files
963
LOC
0
Frameworks
1
Languages
Pipeline State
completedRun ID
#1536113Phase
doneProgress
0%Started
2026-04-16 17:02:15Finished
2026-04-16 17:02:15LLM tokens
0Pipeline Metadata
Stage
SkippedDecision
skip_scaffold_dupNovelty
6.54Framework unique
—Isolation
—Last stage change
2026-04-16 18:15:42Deduplication group #47247
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AI Prompt
Create a system that can distill high-value books into a set of executable AI skills. The process should follow a multi-stage pipeline called RIA-TV++. First, use an Adler analysis to generate a `BOOK_OVERVIEW.md`. Then, extract candidate methodologies using five specialized extractors (frameworks, principles, cases, counter-examples, and terminology). Implement a three-tier verification process requiring cross-domain evidence, predictive power, and uniqueness. Finally, structure the validated content into six dimensions: R (original citation), I (rephrased), A1 (book case), A2 (future trigger scenario), E (executable steps), and B (boundaries/blind spots). The output should be a structured skill package, including an `INDEX.md` map and test cases.
markdown ai-agent knowledge-distillation nlp information-architecture book-summary skill-engineering structured-data
Generated by gemma4:latest
Catalog Information
Create a system that can distill high-value books into a set of executable AI skills. The process should follow a multi-stage pipeline called RIA-TV++. First, use an Adler analysis to generate a BOOK_OVERVIEW.md. Then, extract candidate methodologies using five specialized extractors (frameworks, principles, cases, counter-examples, and terminology). Implement a three-tier verification process requiring cross-domain evidence, predictive power, and uniqueness. Finally, structure the validated c
Tags
markdown ai-agent knowledge-distillation nlp information-architecture book-summary skill-engineering structured-data
Quality Score
D
58.6/100
Structure
37
Code Quality
100
Documentation
30
Testing
0
Practices
78
Security
100
Dependencies
50
Strengths
- Low average code complexity — well-structured code
- Good security practices — no major issues detected
- Properly licensed project
Weaknesses
- No tests found — high risk of regressions
- No CI/CD configuration — manual testing and deployment
Recommendations
- Add a test suite — start with critical path integration tests
- Set up CI/CD (GitHub Actions recommended) to automate testing and deployment
- Add a linter configuration to enforce code style consistency
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Frameworks
None detected
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