Procedural Memory — Auto-Generated Skills from Learned Patterns

Issue: #23

Procedural memory extends the hybrid memory system with “what have I learned to do”: it extracts successful (and failed) multi-step tool-call patterns from session logs and turns them into reusable procedures and, when validated enough, into auto-generated skills that any session or sub-agent can discover.

Overview

Layer What it does
1. Procedure tagging During session processing, multi-step tool sequences are extracted from JSONL logs; successful runs → positive procedures, failures → negative procedures. Stored in the procedures table and optionally as procedure-tagged facts.
2. Procedure-aware recall memory_recall_procedures(taskDescription) and auto-recall inject “Last time this worked” and “Known issue: avoid …” so the agent reuses proven flows and avoids known failures.
3. Skill generation After a procedure is validated N times (default 3), the plugin can auto-generate skills/auto/{slug}/SKILL.md and recipe.json, discoverable by the standard skill system.

Configuration

All under plugins.entries["openclaw-hybrid-memory"].config.procedures:

Option Default Description
enabled true Enable procedure extraction, recall injection, and skill generation.
sessionsDir ~/.openclaw/agents/main/sessions Directory containing session .jsonl files.
minSteps 2 Minimum tool-call steps to consider a sequence a procedure.
validationThreshold 3 Success count required before auto-generating a skill.
skillTTLDays 30 TTL (days) for procedure confidence / revalidation.
skillsAutoPath skills/auto Path (relative to workspace or absolute) for auto-generated skills.
requireApprovalForPromote true When true, human should move skills out of auto/ to promote to permanent.

CLI Commands

Extract procedures from session logs 

# Default: use config sessionsDir, all files
openclaw hybrid-mem extract-procedures

# Only sessions modified in last 7 days
openclaw hybrid-mem extract-procedures --days 7

# Custom directory
openclaw hybrid-mem extract-procedures --dir /path/to/sessions

# Preview without writing
openclaw hybrid-mem extract-procedures --dry-run

Use this in your nightly pipeline together with (or after) session distillation: same session JSONL can be used for fact extraction and procedure extraction.

Generate auto-skills 

# Generate SKILL.md + recipe.json for procedures that reached validationThreshold
openclaw hybrid-mem generate-auto-skills

# Preview only
openclaw hybrid-mem generate-auto-skills --dry-run

Generated skills live under skills/auto/ (or your procedures.skillsAutoPath). To promote one to a permanent skill, move the folder out of auto/ (e.g. to skills/ or a custom path).

Tools

memory_recall_procedures(taskDescription, limit?)

Searches stored procedures by task description (FTS on task_pattern). Returns:

  • Last time this worked: positive procedures with recipe steps.
  • Known issues (avoid): negative procedures (e.g. dead endpoints, failing flows).

Example: when the user says “check Moltbook”, the agent can call memory_recall_procedures("check Moltbook") and get back working steps and warnings like “don’t use /api/v1/agents/notifications (returns HTML 404)”.

Auto-recall injection

When auto-recall is enabled and procedures are enabled, each turn the plugin:

  1. Searches procedures matching the current prompt.
  2. If any match, prepends a <relevant-procedures> block to the injected context with:
    • Short “Last time this worked” lines (task + steps).
    • “Known issue (avoid)” lines for negative procedures.

So the model sees procedure hints without having to call the tool first.

Schema (SQLite)

Facts table (additions)

  • procedure_type — 'positive' | 'negative' | NULL
  • success_count — integer, default 0
  • last_validated — epoch seconds or NULL
  • source_sessions — JSON array of session IDs (text)

Procedures table

  • id, task_pattern, recipe_json, procedure_type (positive negative)
  • success_count, failure_count, last_validated, last_failed
  • confidence, ttl_days, promoted_to_skill, skill_path
  • created_at, updated_at

Full-text search: procedures_fts on task_pattern for searchProcedures and getNegativeProceduresMatching.

Security and safety

  • Secrets: Procedure recipes never store API keys, passwords, or tokens; the extractor redacts known secret keys from step args.
  • Sandbox: Auto-generated skills are written only under skills/auto/ (or your configured path), separate from human-authored skills.
  • Rate limiting: Skill generation is capped per run (default 10) to avoid runaway self-modification.
  • Audit: Each generated skill file includes the source procedure id and metadata (confidence, last validated).

Example end-to-end

  1. Day 1: User asks to “check Moltbook”. Agent calls /api/v1/agents/notifications, gets HTML 404. Session ends in failure.
  2. Nightly: openclaw hybrid-mem extract-procedures --days 1 runs. Parser sees tool sequence + error content → stores a negative procedure: “Check Moltbook …” with recipe and procedure_type: negative.
  3. Day 2: User asks again to “check Moltbook”. Auto-recall injects: “Known issue (avoid): … /notifications …”. Agent uses a different endpoint and succeeds.
  4. Nightly: Extract-procedures runs again; this time the session is successful → positive procedure stored or existing one’s success_count incremented.
  5. Day 7: After several successful runs, success_count reaches 3. You run openclaw hybrid-mem generate-auto-skills → skills/auto/moltbook-check/SKILL.md and recipe.json are created.
  6. Later: Any session or sub-agent that loads skills can use skills/auto/moltbook-check until you move it out of auto/ to promote it.

Back to top

OpenClaw Hybrid Memory — durable agent memory

This site uses Just the Docs, a documentation theme for Jekyll.