Skill: gandli-2025/claw-compactor (version 1.0.0)

--- name: claw-compactor version: "1.0.0" description: > Claw Compactor - 6-layer token compression skill for OpenClaw agents. Cuts workspace token spend by 50–97% using deterministic rule-engines plus Engram: a real-time, LLM-driven Observational Memory system. Run at session start for automatic savings reporting. triggers: - "compress memory", "compress workspace", "save tokens", "token savings", "compress context", "run engram", "engram observe", "engram reflect", "memory compression", "benchmark compression" # Claw Compactor - OpenClaw Skill Reference ## Overview Claw Compactor reduces token usage across the full OpenClaw workspace using 6 compression layers: 1, Name=Rule Engine, Cost=Free, Notes=Dedup, strip filler, merge sections 2, Name=Dictionary Encoding, Cost=Free, Notes=Auto-codebook, `$XX` substitution 3, Name=Observation Compression, Cost=Free, Notes=Session JSONL → structured summaries 4, Name=RLE Patterns, Cost=Free, Notes=Path/IP/enum shorthand 5, Name=Compressed Context Protocol, Cost=Free, Notes=Format abbreviations **6**, Name=**Engram**, Cost=LLM API, Notes=Real-time Observational Memory **Skill location:** `skills/claw-compactor/` **Entry point:** `scripts/mem_compress.py` **Engram CLI:** `scripts/engram_cli.py` ## Auto Mode (Recommended - Run at Session Start) ```bash python3 skills/claw-compactor/scripts/mem_compress.py <workspace> auto ``` Automatically compresses all workspace files, tracks token counts between runs, and reports savings. Run this at the start of every session. ## Core Commands ### Full Pipeline (All Layers) python3 scripts/mem_compress.py <workspace> full Runs all 5 deterministic layers in optimal order. Typical: 50%+ combined savings. ### Benchmark (Non-Destructive) python3 scripts/mem_compress.py <workspace> benchmark # JSON output: python3 scripts/mem_compress.py <workspace> benchmark --json Dry-run report showing potential savings without writing any files. # Layer 1: Rule-based compression python3 scripts/mem_compress.py <workspace> compress # Layer 2: Dictionary encoding python3 scripts/mem_compress.py <workspace> dict # Layer 3: Observation compression (session JSONL → summaries) python3 scripts/mem_compress.py <workspace> observe # Layer 5: Tokenizer optimization python3 scripts/mem_compress.py <workspace> optimize # Tiered summaries (L0/L1/L2) python3 scripts/mem_compress.py <workspace> tiers # Cross-file deduplication python3 scripts/mem_compress.py <workspace> dedup # Token count report python3 scripts/mem_compress.py <workspace> estimate # Workspace health check python3 scripts/mem_compress.py <workspace> audit ### Global Options --json Machine-readable JSON output --dry-run Preview without writing files --since DATE Filter sessions by date (YYYY-MM-DD) --auto-merge Auto-merge duplicates (dedup command) ## Engram - Layer 6: Real-Time Observational Memory Engram is the flagship layer. It operates as a live engine alongside conversations, automatically compressing messages into structured, priority-annotated knowledge. ### Prerequisites Configure via `engram.yaml` (recommended) or environment variables: ```yaml # engram.yaml - place in claw-compactor root llm: provider: openai-compatible base_url: http://localhost:8403 model: claude-code/sonnet max_tokens: 4096 threads: default: observer_threshold: 30000 # pending tokens before Observer fires reflector_threshold: 40000 # observation tokens before Reflector fires concurrency: max_workers: 4 # parallel thread workers # Alternative: environment variables export ANTHROPIC_API_KEY=sk-ant-... # Preferred # or export OPENAI_API_KEY=sk-... # OpenAI-compatible fallback export OPENAI_BASE_URL=https://... # Optional: custom endpoint (local LLM, etc.) ### Engram Auto-Mode (Recommended for Production) Auto-detects all active threads and processes them concurrently (4 workers): # Single run - auto-detects all threads python3 scripts/engram_auto.py --workspace ~/.openclaw/workspace # Via shell wrapper bash scripts/engram-auto.sh # Via CLI python3 scripts/engram_cli.py <workspace> auto --config engram.yaml python3 scripts/engram_cli.py <workspace> status --thread openclaw-main python3 scripts/engram_cli.py <workspace> observe --thread openclaw-main python3 scripts/engram_cli.py <workspace> reflect --thread openclaw-main **Retry:** LLM calls retry on 429/5xx with exponential backoff (2s→4s→8s, max 3 attempts). No retry on 400/401/403 (fail fast on config errors). # Check all thread statuses python3 scripts/mem_compress.py <workspace> engram status # Force Observer for a thread python3 scripts/mem_compress.py <workspace> engram observe --thread <thread-id> # Force Reflector for a thread python3 scripts/mem_compress.py <workspace> engram reflect --thread <thread-id> # Print injectable context python3 scripts/mem_compress.py <workspace> engram context --thread <thread-id> # Status: all threads python3 scripts/engram_cli.py <workspace> status # Status: single thread python3 scripts/engram_cli.py <workspace> status --thread <thread-id> # Force observe python3 scripts/engram_cli.py <workspace> observe --thread <thread-id> # Force reflect python3 scripts/engram_cli.py <workspace> reflect --thread <thread-id> # Import conversation from file (JSON array or JSONL) python3 scripts/engram_cli.py <workspace> ingest \ --thread <thread-id> --input /path/to/conversation.jsonl # Get injectable context string (ready for system prompt) python3 scripts/engram_cli.py <workspace> context --thread <thread-id> # JSON output for any command python3 scripts/engram_cli.py <workspace> status --json python3 scripts/engram_cli.py <workspace> context --thread <id> --json # Start daemon, pipe JSONL messages via stdin python3 scripts/engram_cli.py <workspace> daemon --thread <thread-id> # Pipe a message: echo '{"role":"user","content":"Hello!","timestamp":"12:00"}' | \ # Control commands (send as JSONL): echo '{"__cmd":"observe"}' # force observe now echo '{"__cmd":"reflect"}' # force reflect now echo '{"__cmd":"status"}' # print thread status JSON echo '{"__cmd":"quit"}' # exit daemon # Quiet mode (suppress startup messages on stderr) python3 scripts/engram_cli.py <workspace> daemon --thread <id> --quiet ### Engram Python API ```python from scripts.lib.engram import EngramEngine engine = EngramEngine( workspace_path="/path/to/workspace", observer_threshold=30_000, # tokens before auto-observe reflector_threshold=40_000, # tokens before auto-reflect anthropic_api_key="sk-ant-...", # or set ANTHROPIC_API_KEY env ) # Add a message - auto-triggers observe/reflect when thresholds exceeded status = engine.add_message("thread-id", role="user", content="Hello!") # Manual trigger regardless of thresholds obs_text = engine.observe("thread-id") # returns None if no pending msgs ref_text = engine.reflect("thread-id") # returns None if no observations # Get full context dict ctx = engine.get_context("thread-id") # Build injectable system context string ctx_str = engine.build_system_context("thread-id") # Ready to prepend to system prompt ### Engram Configuration Variables `ANTHROPIC_API_KEY`, Default=-, Description=Anthropic API key (preferred) `OPENAI_API_KEY`, Default=-, Description=OpenAI-compatible API key `OPENAI_BASE_URL`, Default=`https://api.openai.com`, Description=Custom endpoint for local LLMs `OM_OBSERVER_THRESHOLD`, Default=`30000`, Description=Pending tokens before auto-observe `OM_REFLECTOR_THRESHOLD`, Default=`40000`, Description=Observation tokens before auto-reflect `OM_MODEL`, Default=`claude-opus-4-5`, Description=LLM model override ### Threshold Tuning Quick Reference Each Observer call ≈ 2K output tokens (Sonnet). Daily volume at default 30K threshold: #aimm, Daily Tokens=~149K, @30K threshold=~5×/day, @10K threshold=~15×/day openclaw-main, Daily Tokens=~138K, @30K threshold=~4.5×/day, @10K threshold=~14×/day #open-compress, Daily Tokens=~68K, @30K threshold=~2.3×/day, @10K threshold=~7×/day #general, Daily Tokens=~62K, @30K threshold=~2×/day, @10K threshold=~6×/day subagent, Daily Tokens=~43K, @30K threshold=~1.4×/day, @10K threshold=~4×/day cron, Daily Tokens=~9K, @30K threshold=~0.3×/day, @10K threshold=~1×/day **Total**, Daily Tokens=**~470K/day**, @30K threshold=**~16×/day (~32K output tokens)**, @10K threshold=**~47×/day (~94K output tokens)** Start at `observer_threshold: 30000`. Tune down for fresher context; tune up to reduce cost. ### Engram Benchmark Summary | **Engram (L6)** | **87.5%** | 0.038 | 0.414 | ~35s | 2 | | RuleCompressor (L1–5) | 9.0% | 0.923 | 0.958 | ~6ms | 0 | | RandomDrop | 21.5% | 0.852 | 0.911 | ~0ms | 0 | - Engram low ROUGE-L = semantic restructuring, not verbatim copy - intent is preserved - Use RuleCompressor for instant prompt compression; Engram for long-term memory - Full results → `benchmark/RESULTS.md` ### Observation Format Engram produces structured, bilingual (EN/中文) priority-annotated logs: Date: 2026-03-05 - 12:10 User building OpenCompress; deadline one week / 用户在构建 OpenCompress,deadline 一周内 - 12:10 Using ModernBERT-large / 使用 ModernBERT-large - 🟡 12:12 Discussed annotation strategy / 讨论了标注策略 - 🟡 12:30 Deployment pipeline discussion on M3 Ultra - 🟢 12:45 User prefers concise replies - **Critical** - goals, deadlines, blockers, key decisions (never dropped) - 🟡 **Important** - technical details, ongoing work, preferences - 🟢 **Useful** - background, mentions, soft context ### Memory Storage Layout memory/engram/{thread_id}/ ├── pending.jsonl # Unobserved message buffer (auto-cleared after observe) ├── observations.md # Observer output - append-only structured log ├── reflections.md # Reflector output - compressed long-term memory (overwrites) └── meta.json # Timestamps and token counts ## Integration with OpenClaw Memory System ### System Prompt Injection Inject Engram context at the start of each session: engine = EngramEngine(workspace_path) ctx_str = engine.build_system_context("my-session") if ctx_str: system_prompt = ctx_str + "\n\n" + base_system_prompt The `build_system_context()` output structure: ## Long-Term Memory (Reflections) <Reflector output - long-term compressed context> ## Recent Observations <Last 200 lines of Observer output> <!-- engram_tokens: 1234 --> ### Combining Engram with Deterministic Layers After an Engram session, run the deterministic pipeline on the output files: # Then apply deterministic compression to further reduce those: ### Recommended Workflow for Long-Running Agent Sessions 1. **Session start:** inject `build_system_context()` into system prompt 2. **Each message:** call `engine.add_message()` - auto-triggers observe/reflect 3. **Session end / weekly cron:** run `full` pipeline on workspace 4. **Multi-session continuity:** context persists in `memory/engram/{thread}/` ## OpenClaw Skill Installation To install as an OpenClaw skill, ensure the skill directory is available at: ~/.openclaw/workspace/skills/claw-compactor/ or configure the path in your OpenClaw skill registry. SKILL.md is read by the OpenClaw agent dispatcher. The `description` and `triggers` fields above control when this skill is automatically activated. ## Heartbeat / Cron Automation ```markdown ## Memory Maintenance (weekly) - python3 skills/claw-compactor/scripts/mem_compress.py <workspace> benchmark - If savings > 5%: run full pipeline - If pending Engram messages: run engram observe --thread <id> Cron (Sunday 3am): 0 3 * * 0 cd /path/to/skills/claw-compactor && \ python3 scripts/mem_compress.py /path/to/workspace full ## Output Artifacts Reference Dictionary codebook, Location=`memory/.codebook.json`, Description=Must travel with memory files Observed session log, Location=`memory/.observed-sessions.json`, Description=Tracks processed transcripts Layer 3 summaries, Location=`memory/observations/`, Description=Observation compression output Engram observations, Location=`memory/engram/{thread}/observations.md`, Description=Live Observer log Engram reflections, Location=`memory/engram/{thread}/reflections.md`, Description=Distilled long-term memory Level 0 summary, Location=`memory/MEMORY-L0.md`, Description=~200 token ultra-compressed summary Level 1 summary, Location=`memory/MEMORY-L1.md`, Description=~500 token compressed summary ## Troubleshooting - `FileNotFoundError` on workspace: Point path to workspace root containing `memory/` - Dictionary decompression fails: Check `memory/.codebook.json` is valid JSON - Zero savings on `benchmark`: Workspace already optimized - `observe` finds no transcripts: Check `sessions/` for `.jsonl` files - Engram: "no API key configured": Set `ANTHROPIC_API_KEY` or `OPENAI_API_KEY` - Engram Observer returns `None`: No pending messages for that thread - Token counts seem wrong: Install tiktoken: `pip3 install tiktoken`


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