RSI Loop - Recursive Self-Improvement

---

name: rsi-loop

description: >

Recursive Self-Improvement (RSI) loop for EvoClaw agents. Provides a structured

observe→analyze→synthesize→deploy pipeline that enables agents to detect their own

failure patterns and generate concrete improvement proposals (new skills, routing fixes,

SOUL.md updates, memory improvements). Use when: (1) logging a task outcome (success/fail/quality),

(2) running periodic self-improvement analysis, (3) reviewing or deploying improvement proposals,

(4) integrating RSI into EvoClaw hub/edge agents via MQTT, (5) checking agent health score,

(6) any mention of "self-improvement", "recursive improvement", "fix my own mistakes",

"improvement loop", or "agent evolution". Core EvoClaw primitive.

---

# RSI Loop - Recursive Self-Improvement

Four-stage pipeline: **Observe → Analyze → Synthesize → Deploy**

Quick Start

# Log an outcome
uv run python skills/rsi-loop/scripts/rsi_cli.py log \
  --task code_generation --success true --quality 4 --model glm-4.7

# Full cycle (detect patterns + generate + deploy quick wins)
uv run python skills/rsi-loop/scripts/rsi_cli.py cycle

# Status dashboard
uv run python skills/rsi-loop/scripts/rsi_cli.py status

Data Layout

skills/rsi-loop/data/
├── outcomes.jsonl       # All logged turn outcomes
├── patterns.json        # Latest analysis output
└── proposals/           # Improvement proposals (one JSON per proposal)
    ├── abc12345.json    # draft/approved/rejected/deployed
    └── ...

Stage 1: Observer — Log Outcomes

Log every significant task at completion. Be honest about quality (1=terrible, 5=perfect).

# Successful task
uv run python skills/rsi-loop/scripts/rsi_cli.py log \
  --task code_generation --success true --quality 4

# Failed task with issues
uv run python skills/rsi-loop/scripts/rsi_cli.py log \
  --task code_debug --success false --quality 2 \
  --issues skill_gap rate_limit \
  --notes "No Rust-specific debug skill, kept hitting context limits"

**Task types:** code_generation, code_debug, code_review, architecture_design, file_ops,

web_search, memory_retrieval, skill_creation, cron_management, api_integration,

data_analysis, message_routing, infrastructure_ops, documentation, general_qa,

trading, monitoring, blockchain, unknown

**Issue types:** rate_limit, model_fallback, tool_error, wrong_output, incomplete_task,

context_loss, memory_miss, skill_gap, bad_routing, slow_response, over_confirmation,

repeated_mistake, missing_tool, wrong_model_tier, compaction_lost_context, other

Stage 2: Analyzer — Detect Patterns

uv run python skills/rsi-loop/scripts/analyzer.py --days 7 --top 5

Outputs ranked patterns by impact score = (frequency/total) × quality_deficit.

Saves to `data/patterns.json`.

Stage 3: Synthesizer — Generate Proposals

# Generate proposals from latest patterns
uv run python skills/rsi-loop/scripts/synthesizer.py generate --top 5

# Review proposals
uv run python skills/rsi-loop/scripts/synthesizer.py list

# Show full proposal detail
uv run python skills/rsi-loop/scripts/synthesizer.py show <proposal_id>

# Approve for deployment
uv run python skills/rsi-loop/scripts/synthesizer.py approve <proposal_id>

Stage 4: Deployer — Apply Improvements

# Dry run (see what would happen)
uv run python skills/rsi-loop/scripts/deployer.py deploy <id> --dry-run

# Deploy a specific proposal
uv run python skills/rsi-loop/scripts/deployer.py deploy <id>

# Deploy all approved proposals
uv run python skills/rsi-loop/scripts/deployer.py deploy-all

**Action types and what they do:**

Full Cycle (Automated)

# Run full cycle, auto-deploy anything estimated < 20 minutes effort
uv run python skills/rsi-loop/scripts/deployer.py full-cycle \
  --days 7 --auto-approve-below-mins 20

# Or use the CLI shortcut
uv run python skills/rsi-loop/scripts/rsi_cli.py cycle

Cron Job (Weekly RSI)

Set up automated weekly analysis:

# Every Sunday at 3 AM AEST
openclaw cron add --name "Weekly RSI Cycle" \
  --cron "0 3 * * 0" \
  --tz "Australia/Sydney" \
  --model "anthropic-proxy-4/glm-4.7" \
  --system-event "Run RSI cycle: uv run python skills/rsi-loop/scripts/rsi_cli.py cycle --days 7"

EvoClaw Integration

For fleet-wide RSI across all hub/edge agents, see:

Proactive Logging Protocol

Log outcomes for every significant task. Rule of thumb:

This builds the dataset that makes RSI work.