#learning #accounting #agent-memory #github-integration

Memory Feedback

Agent memory and learning system. Logs actions/failures, detects patterns, proposes skill improvements via GitHub PRs. Human approval required.

Stems@satoshistackalotto

Install

openclaw skills install @satoshistackalotto/memory-feedback

Memory and Feedback

This skill gives the OpenClaw system a learning loop. All 18 skills log their episodes and failures. This skill reads those logs, detects patterns, and proposes improvements — as GitHub pull requests against the SKILL.md files that govern behaviour. Humans review and merge. The system learns.

This is Phase 4 infrastructure, designed to be activated once Phase 3B has been running long enough to generate meaningful data — typically 2-3 months of real operation. The episode and failure hooks in Skills 14-18 write to /data/memory/ from day one. When this skill is activated, months of learning data are already waiting.

Setup

bash

export OPENCLAW_DATA_DIR="/data"
which jq || sudo apt install jq

# Optional: for GitHub PR workflow
export GITHUB_TOKEN="ghp_your_token"
which gh || echo "gh CLI optional — only needed for PR-based improvement proposals"

GITHUB_TOKEN is optional — required only for the PR workflow that proposes skill improvements. Core memory logging and failure tracking works without it using local files only.

Core Philosophy

Semi-Automatic by Design: The agent owns its memory files — episodes and failures are written without approval. Skill files govern behaviour and require human review before any change takes effect. This boundary is non-negotiable
Pattern Detection, Not Gut Feel: Improvements are proposed only when a pattern appears with sufficient frequency and confidence. A single failure is a data point. Three failures of the same type with the same trigger is a pattern
Human in the Loop for Skill Changes: Every proposed skill improvement becomes a GitHub pull request. The accountant or developer sees exactly what changed, why, and what evidence led to the proposal. Merge = accepted. Close = rejected and never re-proposed
Rate Limited to Protect the Machine: Memory operations are token-budgeted. Pattern scans run overnight. Storage limits are enforced. The system cannot consume itself
Honest About What It Does Not Know: Patterns below the confidence threshold are stored but not proposed. The system does not act on weak evidence

OpenClaw Commands

Episode and Failure Logging (called by other skills)

bash

# Log a successful episode (called internally by all skills)
openclaw memory log-episode --skill greek-financial-statements --type statement_generated --afm EL123456789 --period 2026-01 --session S20260218-001 --tokens 1240 --duration-seconds 31

# Log a failure (called internally by all skills)
openclaw memory log-failure --skill conversational-ai-assistant --type intent_misread --session S20260218-003 --query "send the summary to Alpha" --action-taken "attempted openclaw comms send" --what-went-wrong "Skill 16 not available" --what-should-have-happened "draft text and state manual sending required"

# Log a human correction (called when accountant provides explicit feedback)
openclaw memory log-correction --session S20260218-005 --failure-id FAIL-20260218-003 --correction "Draft the text and tell me to send it manually"

Pattern Scanning and Analysis

bash

# Run pattern scan manually (normally runs nightly)
openclaw memory scan-patterns --since 30-days
openclaw memory scan-patterns --since 30-days --skill conversational-ai-assistant
openclaw memory scan-patterns --failures-only --min-occurrences 3

# View detected patterns
openclaw memory patterns-list --all
openclaw memory patterns-list --above-threshold   # Confidence >= 0.85
openclaw memory patterns-list --below-threshold   # Promising but not yet proposable
openclaw memory pattern-detail --id PAT-20260218-007

# View recent episodes and failures
openclaw memory episodes --last 7-days
openclaw memory episodes --skill greek-financial-statements --last 30-days
openclaw memory failures --last 7-days
openclaw memory failures --type completeness_gate_failed --last 30-days
openclaw memory corrections --last 30-days

Proposal Generation and GitHub Integration

bash

# Generate a proposal from a detected pattern
openclaw memory propose --pattern-id PAT-20260218-007
openclaw memory propose --pattern-id PAT-20260218-007 --dry-run   # Show proposed change without creating PR

# List proposals
openclaw memory proposals-list --all
openclaw memory proposals-list --status pending-pr
openclaw memory proposals-list --status pr-open
openclaw memory proposals-list --status rejected

# Create GitHub PR from a proposal
openclaw memory create-pr --proposal-id PROP-20260218-001
openclaw memory create-pr --proposal-id PROP-20260218-001 --branch-name "memory/skill-improvement/PROP-001"

# Sync PR status (check if merged or closed)
openclaw memory sync-prs
openclaw memory pr-status --proposal-id PROP-20260218-001

Rate Limit Management

bash

# View current consumption
openclaw memory rate-limit-status
openclaw memory rate-limit-status --verbose   # Show all counters and limits

# View config
openclaw memory rate-limit-config

# Reset daily counter (use only if counter stuck after verified issue)
openclaw memory rate-limit-reset --counter daily-tokens --confirm

# View storage usage
openclaw memory storage-usage
openclaw memory storage-usage --by-directory

Memory Maintenance

bash

# Archive old episodes (auto-runs, but can be triggered manually)
openclaw memory archive --episodes --older-than 90-days
openclaw memory archive --failures --older-than 90-days

# View memory health
openclaw memory health
openclaw memory health --verbose

# Generate memory report (monthly summary of learning activity)
openclaw memory report --period 2026-01
openclaw memory report --period 2026-01 --format pdf --output /data/reports/system/

Episode Log Structure

json

{
  "episode_id": "EP-20260218-001",
  "session_id": "S20260218-001",
  "user": "maria.g",
  "timestamp_utc": "2026-02-18T09:14:32Z",
  "skill": "greek-financial-statements",
  "action_type": "statement_generated",
  "client_afm": "EL123456789",
  "period": "2026-01",
  "commands_called": [
    "openclaw statements check-ready --afm EL123456789 --period 2026-01",
    "openclaw statements generate --afm EL123456789 --period 2026-01 --type all"
  ],
  "human_confirmation_required": false,
  "outcome": "success",
  "result_summary": "Full statement pack generated. P&L, balance sheet, cash flow, VAT summary. v1 issued.",
  "tokens_used": 1240,
  "duration_seconds": 31
}

Failure Log Structure

json

{
  "failure_id": "FAIL-20260218-003",
  "episode_id": "EP-20260218-003",
  "session_id": "S20260218-001",
  "user": "maria.g",
  "timestamp_utc": "2026-02-18T11:22:14Z",
  "skill": "conversational-ai-assistant",
  "failure_type": "intent_misread",
  "user_query": "Send the January summary to Alpha Trading",
  "agent_action_taken": "Attempted openclaw comms send — skill not yet available",
  "what_went_wrong": "Agent tried to send client communication directly. Skill 16 not deployed at time of query.",
  "what_should_have_happened": "Recognise outgoing communication unavailable. Draft the summary text. Inform user it needs manual sending.",
  "human_correction_provided": true,
  "human_correction_text": "Just write the summary text and tell me to send it manually",
  "pattern_candidate": true,
  "skill_improvement_candidate": "14-conversational-ai-assistant",
  "proposed_change_summary": "Add scope boundary: if comms send requested and Skill 16 unavailable, draft content and state manual sending required"
}

Pattern Detection

Detection Algorithm

yaml

Pattern_Detection:
  runs: "Nightly at 02:00 Athens time"
  lookback_window: "30 days rolling"
  minimum_occurrences: 3
  confidence_threshold_for_proposal: 0.85

  grouping_keys:
    - skill + failure_type                     # Same skill, same failure type
    - user_query_intent + agent_action_taken   # Same intent, same wrong action
    - skill + missing_data_source              # Same skill, same upstream data missing

  confidence_calculation:
    factors:
      - occurrence_count (weight: 40%)
      - recency_bias (more recent = higher weight) (weight: 30%)
      - human_correction_present (weight: 20%)
      - consistent_what_should_have_happened across occurrences (weight: 10%)

  output:
    above_threshold: "Write pattern to /data/memory/patterns/failures/{id}.json — eligible for proposal"
    below_threshold: "Write pattern to /data/memory/patterns/failures/{id}.json — marked WATCH, not proposable yet"

Pattern Record Structure

json

{
  "pattern_id": "PAT-20260218-007",
  "detected_date": "2026-02-18",
  "pattern_type": "recurring_failure",
  "skill": "14-conversational-ai-assistant",
  "failure_type": "intent_misread",
  "occurrences": 4,
  "failure_ids": ["FAIL-20260212-001", "FAIL-20260214-003", "FAIL-20260217-002", "FAIL-20260218-003"],
  "confidence": 0.91,
  "common_trigger": "User requests outgoing client communication",
  "common_failure": "Agent attempts Skill 16 which is not deployed",
  "what_should_have_happened": "Draft content and state manual sending required until Skill 16 deployed",
  "proposed_skill_change": {
    "skill_file": "14-conversational-ai-assistant-SKILL.md",
    "section": "Scope Boundaries / Out_Of_Scope__Redirect",
    "change_description": "Add explicit handling for comms send requests when Skill 16 unavailable",
    "current_text_excerpt": "Draft document request text (for human review before sending)",
    "proposed_addition": "If user requests outgoing communication and Skill 16 (client-communication-engine) is not deployed: draft the full text content, clearly state it requires manual sending, and note that Skill 16 will handle this automatically when deployed."
  },
  "proposal_status": "pending",
  "github_pr_status": null,
  "rejected": false
}

GitHub Integration Flow

yaml

GitHub_PR_Flow:

  prerequisites:
    - GitHub repo URL configured in /data/memory/rate-limits/config.json
    - GitHub token stored in /data/auth/github-token.enc
    - Agent has push access to create branches only (not merge to main)

  step_1_branch_creation:
    command: "git checkout -b memory/skill-improvement/{proposal-id}"
    naming: "memory/skill-improvement/PROP-{YYYYMMDD}-{3digits}"

  step_2_file_edit:
    target: "The specific SKILL.md file identified in the pattern"
    change: "Exactly the text change described in proposed_skill_change"
    commit_message: |
      "Memory system: {brief description}
      
      Pattern: {pattern_id} — {confidence} confidence
      Occurrences: {N} over {lookback_window}
      Failure type: {failure_type}
      
      See /data/memory/patterns/failures/{pattern_id}.json for full evidence."

  step_3_pull_request:
    title: "[Memory] {skill_name}: {brief description of change}"
    body: |
      ## What This Changes
      {plain English description of the specific text change}
      
      ## Why This Is Proposed
      Pattern {pattern_id} detected {N} occurrences of {failure_type} 
      in {skill_name} over the last {days} days.
      
      Confidence score: {confidence} (threshold: 0.85)
      
      ## Evidence
      Failure IDs: {list}
      Common trigger: {common_trigger}
      Common failure: {common_failure}
      What should have happened: {what_should_have_happened}
      
      ## To Accept
      Review the specific text change in the diff and merge if correct.
      
      ## To Reject
      Close this PR. The pattern will be marked as reviewed/rejected 
      and this specific change will not be re-proposed.
    labels: ["memory-proposal", "skill-improvement"]

  step_4_post_pr:
    on_merge:
      - Mark proposal status as "merged"
      - Log episode: proposal_accepted
      - Pattern marked as resolved
    on_close:
      - Mark proposal status as "rejected"  
      - Pattern marked as reviewed/rejected
      - Same change never re-proposed for this pattern
      - Agent learns: log rejection reason for future calibration

  hard_limits:
    max_prs_per_day: 2
    max_proposals_per_day: 3
    agent_never_pushes_to: "main, master, or any protected branch"
    agent_never_merges: "Any PR — merge requires human action only"

Rate Limiting

yaml

Rate_Limit_Config:
  location: "/data/memory/rate-limits/config.json"

  token_budgets:
    memory_logging: 500     # Per-operation logging (episodes and failures)
    pattern_scan: 1500      # Nightly scan of last 30 days
    proposal_generation: 1000  # Per proposal — includes SKILL.md diff computation
    daily_total: 5000       # Hard ceiling across all memory operations
    note: "Token budget counts only memory/reflection tokens, not accounting operations"

  operation_frequency:
    episode_logging: "Every qualifying action — no rate limit (lightweight)"
    failure_logging: "Every failure — no rate limit (lightweight)"
    pattern_scan: "Once daily at 02:00 Athens — never during business hours"
    proposal_generation: "Maximum 3 per day"
    github_pr_creation: "Maximum 2 per day"

  storage_limits:
    episodes_max_mb: 500
    failures_max_mb: 200
    patterns_max_mb: 50
    proposals_max_mb: 50
    total_memory_max_gb: 2
    auto_archive_after_days: 90
    halt_at_percent: 90    # Halt memory writes at 90% of total_memory_max_gb

  circuit_breakers:
    halt_if_daily_token_budget_exceeded: true
    halt_if_storage_exceeds_90_percent: true
    alert_at_80_percent_storage: true
    never_run_pattern_scan_during_business_hours: true
    never_run_more_than_one_scan_per_day: true

  hard_floors:
    note: "These values cannot be set lower by the agent — they are code-enforced minimums"
    min_confidence_for_proposal: 0.85
    min_occurrences_for_proposal: 3
    min_lookback_days: 7

File System

yaml

Memory_File_Structure:
  owns: "/data/memory/"

  episodes:
    path: "/data/memory/episodes/{YYYY-MM-DD}/{session-id}_{action-type}.json"
    written_by: "All 18 skills via openclaw memory log-episode"
    read_by: "memory-feedback only"
    auto_archived_after: "90 days"

  failures:
    path: "/data/memory/failures/{YYYY-MM-DD}/{session-id}_{failure-type}.json"
    written_by: "All 18 skills via openclaw memory log-failure"
    read_by: "memory-feedback only"
    auto_archived_after: "90 days"

  patterns:
    path: "/data/memory/patterns/failures/{pattern-id}.json"
         "/data/memory/patterns/successes/{pattern-id}.json"
    written_by: "memory-feedback (nightly scan)"
    retained: "Indefinitely — patterns are system learning assets"

  corrections:
    path: "/data/memory/corrections/{YYYY-MM-DD}_{correction-id}.json"
    written_by: "conversational-ai-assistant (when human provides correction)"
    read_by: "memory-feedback"
    retained: "Indefinitely"

  proposals:
    path: "/data/memory/proposals/{YYYY-MM-DD}_{skill-name}_{id}.md"
    written_by: "memory-feedback"
    retained: "Indefinitely (audit record of what was proposed and outcome)"

  rate_limits:
    path: "/data/memory/rate-limits/"
    files:
      - "current-state.json"   # Live counters — updated per operation
      - "daily-log.json"       # Historical daily consumption
      - "config.json"          # Configurable limits

Activation Checklist

Skill 19 should be activated when:

yaml

Activation_Prerequisites:
  data_maturity:
    - "Phase 3B has been running for minimum 60 days"
    - "At least 100 episodes logged across all skills"
    - "At least 20 failures logged with what_should_have_happened populated"
    - "At least 3 human corrections on record"

  infrastructure:
    - "GitHub repo URL and token configured"
    - "Agent has branch creation access (not merge)"
    - "/data/memory/ directories exist and are writable"
    - "Storage monitoring confirms adequate space (min 5 GB free)"

  team_readiness:
    - "Senior accountant / developer briefed on PR review process"
    - "GitHub notifications configured for the memory/skill-improvement/* branch pattern"
    - "Team understands: close PR = rejected, merge PR = accepted change goes live"

  first_run:
    - "Run openclaw memory scan-patterns --since 60-days --dry-run first"
    - "Review all patterns detected before enabling live proposal creation"
    - "Confirm rate limit config matches team preferences"
    - "Enable with: openclaw memory activate --confirm"

Integration Points

yaml

Written_By_All_Skills:
  hook: "Every skill calls openclaw memory log-episode or openclaw memory log-failure as final step"
  episode_triggers: "Decision made, output produced, government system interaction"
  failure_triggers: "Any error, intent misread, missing data, human correction"

Read_By_Memory_Feedback_Only:
  note: "No other skill reads from /data/memory/ for decision-making"
  reason: "Memory data is raw and unverified until pattern-processed. Direct reads would propagate noise."

Downstream_Of_Memory_System:
  github: "Proposed skill improvements as pull requests"
  dashboard: "Memory health metrics (token usage, storage, pattern counts)"
  reports: "/data/reports/system/ — monthly memory activity report"

Success Metrics

A successful deployment of this skill should achieve:

✅ 100% of qualifying agent actions generate an episode log within 5 seconds
✅ 100% of failures generate a failure log with what_should_have_happened populated
✅ Nightly pattern scan completes within the daily token budget
✅ At least one high-confidence pattern detected per month after 90 days of operation
✅ PRs opened for all proposals above the confidence threshold — zero silently dropped
✅ Rejected patterns never re-proposed — the rejection is respected
✅ Storage consumption stays within configured limits — no unchecked growth
✅ Zero PR merges without human action — agent never self-merges

Remember: This skill is how the system gets better over time without requiring manual review of every interaction. The value compounds. Month 3 of operation is more valuable than month 1. The team should expect to see the quality of the conversational assistant and financial statement generation improve measurably within 6 months of activation.

Memory Feedback

Install

Memory and Feedback

Setup

Core Philosophy

OpenClaw Commands

Episode and Failure Logging (called by other skills)

Pattern Scanning and Analysis

Proposal Generation and GitHub Integration

Rate Limit Management

Memory Maintenance

Episode Log Structure

Failure Log Structure

Pattern Detection

Detection Algorithm

Pattern Record Structure

GitHub Integration Flow

Rate Limiting

File System

Activation Checklist

Integration Points

Success Metrics

Related skills