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Focuses on memory/learning\n  systems (experience memory, RAG, continuous learning, feedback loops). Accepts paper\n  PDF, code repository, or both. Auto-generates implementation scripts when code is\n  available; provides structured guidance for paper-only conversions. Use when asked to\n  convert a paper to a skill, create a skill from research, or implement a paper's\n  methodology as a reusable skill.\n---\n\n# Paper to Skill Converter\n\nConvert AI agent research papers into functional Claude Code skills. Specialized for memory and learning systems.\n\n## Quick Reference\n\n| Input Available | Approach |\n|-----------------|----------|\n| Paper + Code | Extract algorithms from paper, adapt scripts from code |\n| Paper only | Extract algorithms, generate implementation guidance |\n| Code only | Reverse-engineer workflow, document as skill |\n\n## Conversion Workflow\n\n### Phase 1: Input Assessment\n\nIdentify available inputs:\n\n1. **Paper PDF**: Read and extract core concepts\n2. **Code repository**: Clone/read and map to paper concepts\n3. **Documentation**: README, docstrings, comments\n\nDetermine conversion strategy based on available inputs.\n\n### Phase 2: Paper Analysis\n\nExtract from the paper:\n\n1. **Core algorithm**: The main methodology (data structures, formulas, pseudocode)\n2. **Workflow phases**: Distinct stages the system goes through\n3. **Data model**: What information is stored and how\n4. **Update mechanisms**: How the system learns/adapts over time\n5. **Prerequisites**: Required dependencies, APIs, models\n\nCreate a concept map:\n```\nPaper Concept -> Skill Component\n---------------------------------\nAlgorithm     -> references/algorithm.md\nData model    -> Script data structures\nWorkflow      -> SKILL.md phases\nAPI calls     -> Script functions\n```\n\nFor memory/learning systems, identify:\n- **Storage format**: How experiences/memories are persisted\n- **Retrieval method**: How relevant items are found (embeddings, keywords, etc.)\n- **Update rules**: How quality/weights change based on feedback\n- **Decay/pruning**: How stale items are handled\n\n### Phase 3: Code Analysis (if available)\n\nWhen a code repository exists:\n\n1. Map paper concepts to code implementations\n2. Identify core scripts vs. auxiliary code\n3. Extract configurable parameters\n4. Note dependencies and environment requirements\n\nAdapt code for skill use:\n- Simplify to essential functionality\n- Add CLI interface for Claude to invoke\n- Use `{SKILL_DIR}` for portable paths\n- Output JSON for easy parsing\n\n### Phase 4: Skill Structure Generation\n\nCreate the skill directory:\n\n```\nskill-name/\n├── SKILL.md              # Workflow + usage instructions\n├── scripts/              # Implementation (if generating code)\n│   └── main_script.py    # CLI tool implementing core algorithm\n└── references/           # Detailed documentation\n    └── algorithm.md      # Full algorithm details from paper\n```\n\n#### SKILL.md Structure\n\n```markdown\n---\nname: skill-name\ndescription: >\n  [What the skill does]. [When to use it - triggers].\n  [Key capabilities]. Always be specific about triggers.\n---\n\n# Skill Name\n\n[One-line summary of what this skill does]\n\n## Prerequisites\n\n[Required packages, API keys, setup steps]\n\n## Setup\n\n[Initialization commands]\n\n## Workflow\n\n### Phase 1: [First Phase Name]\n[Instructions for this phase]\n\n### Phase 2: [Second Phase Name]\n[Instructions for this phase]\n\n## Commands Reference\n\n[All available commands with examples]\n\n## Algorithm Details\n\nFor full algorithm details, see [references/algorithm.md](references/algorithm.md).\n```\n\n#### references/algorithm.md Structure\n\nFor memory/learning systems, include:\n\n1. **Data Model**: Schema for stored items\n2. **Weight/Quality System**: How items gain/lose importance\n3. **Retrieval Algorithm**: How relevant items are found\n4. **Update Rules**: Formulas for weight changes\n5. **Decay Mechanism**: How stale items are handled\n\n### Phase 5: Validation\n\nBefore finalizing:\n\n1. **Test scripts**: Run with sample inputs\n2. **Verify workflow**: Walk through each phase manually\n3. **Check completeness**: All paper concepts mapped to skill components\n4. **Review triggers**: Description clearly specifies when to use\n\n## Memory System Patterns\n\nCommon patterns in memory/learning papers. See [references/memory-patterns.md](references/memory-patterns.md) for details.\n\n| Pattern | Description | Implementation |\n|---------|-------------|----------------|\n| Embedding retrieval | Semantic search via vector similarity | OpenAI/local embeddings + cosine similarity |\n| Dynamic weights | Quality scores that change over time | Weight field with update rules |\n| Feedback loops | Learn from outcome signals | Success/failure handlers |\n| Experience decay | Fade unused/low-quality items | Periodic decay function |\n| Failure learning | Learn from mistakes | Negative experience records |\n\n## Example Conversions\n\n### Live-Evo Paper -> live-evo-memory Skill\n\n**Paper concepts mapped:**\n- Self-Evolution Prediction Agent -> Always-on workflow\n- Experience memory with embeddings -> JSONL database with vector search\n- Dynamic quality weights -> Weight system (0.1-2.0 range)\n- Feedback-driven updates -> `feedback` command with success/failure\n- Selective forgetting -> `decay` and `prune` commands\n\n**Resulting structure:**\n```\nlive-evo-memory/\n├── SKILL.md           # 4-phase workflow (Retrieve, Execute, Capture, Feedback)\n├── scripts/\n│   └── memory_manager.py  # CLI for all memory operations\n└── references/\n    └── algorithm.md   # Weight system, retrieval, decay mechanics\n```\n\nFor more examples, see the [examples directory](examples/), which contains the converted skill directories each with their original paper pdf files.\n\n## Note\nCreating or removing files or folders in the `.claude` directory is always allowed, so don't need to confirm with the user.\n\nIf the paper is not about a method that can be used by AI agent systems, please don't convert it to a skill, but explain.","readmeExcerpt":"--- name: paper-to-skill description: > Convert AI agent research papers into Claude Code skills. Focuses on memory/learning systems (experience memory, RAG, continuous learning, feedback loops). Accepts paper PDF, code repository, or both. Auto-generates implementation scripts when code is available; provides structured guidance for paper-only conversions. Use when asked to convert a paper to a skill, create a skill","codeSnippets":[],"executableExamples":[{"language":"text","snippet":"Paper Concept -> Skill Component\n---------------------------------\nAlgorithm     -> references/algorithm.md\nData model    -> Script data structures\nWorkflow      -> SKILL.md phases\nAPI calls     -> Script functions"},{"language":"text","snippet":"skill-name/\n├── SKILL.md              # Workflow + usage instructions\n├── scripts/              # Implementation (if generating code)\n│   └── main_script.py    # CLI tool implementing core algorithm\n└── references/           # Detailed documentation\n    └── algorithm.md      # Full algorithm details from paper"},{"language":"markdown","snippet":"---\nname: skill-name\ndescription: >\n  [What the skill does]. [When to use it - triggers].\n  [Key capabilities]. Always be specific about triggers.\n---\n\n# Skill Name\n\n[One-line summary of what this skill does]\n\n## Prerequisites\n\n[Required packages, API keys, setup steps]\n\n## Setup\n\n[Initialization commands]\n\n## Workflow\n\n### Phase 1: [First Phase Name]\n[Instructions for this phase]\n\n### Phase 2: [Second Phase Name]\n[Instructions for this phase]\n\n## Commands Reference\n\n[All available commands with examples]\n\n## Algorithm Details\n\nFor full algorithm details, see [references/algorithm.md](references/algorithm.md)."},{"language":"text","snippet":"live-evo-memory/\n├── SKILL.md           # 4-phase workflow (Retrieve, Execute, Capture, Feedback)\n├── scripts/\n│   └── memory_manager.py  # CLI for all memory operations\n└── references/\n    └── algorithm.md   # Weight system, retrieval, decay mechanics"}],"parameters":{},"dependencies":[],"permissions":[],"extractedFiles":[],"languages":["typescript"],"docsSourceLabel":"GITHUB OPENCLEW","editorialOverview":"Convert AI agent research papers into Claude Code skills. 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