Skill: wudi488/context-assembler (version 1.0.0)

--- name: context-assembler description: Dynamic context preprocessor for OpenClaw agents. Selects relevant memory, collapses timelines, detects forbidden patterns, and injects task-specific context before agent reasoning. Reduces context noise by up to 89% while improving output quality. Use when you want smarter, more focused agent behavior without changing any prompts. version: 1.0.0 tags: [context, memory, optimization, agent-infra] --- # Context Assembler **Not a tool for agents — a preprocessor for their context window.** OpenClaw injects ~3700 tokens of static bootstrap context into every session, regardless of the task. Context Assembler reduces that to ~400 tokens of task-relevant information, achieving 89% context compression without quality loss. ## What It Does ``` Task arrives → Classify → Semantic Projection → Timeline Collapse → Forbidden Patterns → Pack → Agent reasons ``` 1. **Classifies** the task type (nas_ops, coding, research, evolution_check, etc.) 2. **Semantically projects** relevant memory from MEMORY.md and daily notes — only what matters 3. **Collapses timelines** — repeated failures become single entries, noise gets filtered 4. **Detects forbidden patterns** — paths that failed ≥2 times get marked "do not retry" 5. **Packs** everything into a compact context block within token budget ## Why This Matters Karpathy's "Context Engineering > Prompt Engineering" principle applied to OpenClaw. The quality bottleneck isn't how you write prompts — it's what the agent sees in its context window before reasoning. ## Quick Start ### Manual invocation (agent calls it) ```bash python3 scripts/assembler.py --task "check NAS disk health" --max-tokens 1500 ``` ### Cron pre-turn hook In your OpenClaw cron job, run assembler first: ```bash # The agent's first action: get optimized context python3 skills/context-assembler/scripts/assembler.py \ --task "daily evolution check" \ --max-tokens 1800 ``` ### Agent-assisted mode Tell your agent: `"optimize my context"` — it will call assembler and use the output. ## Configuration Edit `genome.yml` to customize: - **Source weights**: which memory sources matter most - **Task profiles**: per-task token budgets and source preferences - **Synonym map**: lightweight semantic expansion (e.g., "shrimp" → "aquaculture, water quality") - **Noise patterns**: timeline events to filter out - **Staleness decay**: how fast old information loses relevance The `genome.yml` is the "mutable kernel" — you tune it, the assembler engine stays fixed. ## Requirements - Python 3.8+ - PyYAML (`pip install pyyaml`) - Read access to OpenClaw workspace (`MEMORY.md`, `memory/*.md`) ## Architecture ``` skills/context-assembler/ ├── SKILL.md # This file ├── genome.yml # ★ Mutable kernel (tune this) ├── scripts/ │ └── assembler.py # Fixed engine (~510 lines) ├── index/ # Future: pre-built search indices └── feedback/ # Selection → outcome log ``` ## Design Principles 1. **Offload decisions**: don't teach the agent to judge — encode judgment as a checklist 2. **Compress output space**: templates > free-form writing 3. **Absence as signal**: tell the agent what NOT to include 4. **Embed domain knowledge**: your expertise encoded as correlation rules 5. **Graceful degradation**: missing data is normal, not an error ## Notes - Phase 1 uses keyword matching with synonym expansion (zero-latency, zero-extra-memory) - Phase 2+ will add embedding-based semantic search as memory corpus grows - The genome is designed to be optimizable — feedback logging enables self-tuning - Does NOT modify OpenClaw core — installs as a regular skill - Contains no credentials, tokens, or personal identifiers — safe to publish


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)