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Fanta Autoresearch

by @fantaclaw-ai

Autonomous goal-directed iteration for optimization and improvement tasks. Use when you need to systematically improve a metric, optimize a system, or iterat...

Versionv1.0.0
Downloads261
TERMINAL
clawhub install fanta-autoresearch

πŸ“– About This Skill


name: autoresearch description: "Autonomous goal-directed iteration for optimization and improvement tasks. Use when you need to systematically improve a metric, optimize a system, or iteratively refine something. Triggers on phrases like 'autoresearch', 'autonomous loop', 'iterate until', 'improve X', 'optimize Y', or when user wants to run multiple iterations of make-change β†’ verify β†’ keep/revert cycles."

Autoresearch Skill

Run autonomous iteration loops: Goal β†’ Metric β†’ Loop (make change β†’ verify β†’ keep/revert β†’ repeat).

Core Protocol

SETUP:
1. Define GOAL (what to improve)
2. Define METRIC (how to measure success)
3. Define SCOPE (what can be modified)
4. Establish BASELINE (current metric value)

LOOP (forever or N iterations): 1. Review current state + history + results log 2. Pick next change (based on what worked, what failed, what's untried) 3. Make ONE focused change 4. Commit change (for rollback) 5. Run mechanical verification (tests, benchmarks, scores) 6. If improved β†’ keep. If worse β†’ revert. If error β†’ fix or skip. 7. Log the result 8. Repeat until goal reached or max iterations

Principles

1. One change per iteration β€” Atomic changes. If it breaks, you know why. 2. Mechanical verification only β€” No subjective "looks good." Use metrics. 3. Automatic rollback β€” Failed changes revert instantly. 4. Git is memory β€” Each experiment is committed. Git revert preserves history. 5. Simplicity wins β€” Equal results + less code = KEEP

Quick Start

Goal: Improve memory search Top-1 hit rate from 65% to 75%
Metric: Benchmark score (openclaw cron runs --id  --limit 1)
Scope: ~/.openclaw/workspace/MEMORY.md, ~/.openclaw/openclaw.json
Max Iterations: 5

Then run the loop manually or spawn a subagent to execute it.

Usage Patterns

Pattern 1: Manual Loop (Interactive)

For simple tasks, run the loop yourself:

Iteration 1:
  - Change: [describe what you'll change]
  - Verify: [run verification]
  - Result: [keep/revert + reason]
  - Log entry

Pattern 2: Spawn Subagent (Autonomous)

For longer tasks, spawn a subagent with the loop instructions:

sessions_spawn with:
  - task: Full autoresearch loop specification
  - timeoutSeconds: 600 (10 min per iteration)
  - mode: run (one-shot) or session (persistent)

Pattern 3: Background Process

For very long loops, use exec with background continuation:

exec with:
  - command: The optimization script
  - background: true
  - yieldMs: 60000 (check every minute)

Verification Commands

| Domain | Verify Command | |--------|----------------| | Memory search | openclaw cron runs --id --limit 1 | | Tests | npm test, pytest, cargo test | | Build | npm run build, cargo build | | Lint | eslint ., ruff check . | | Benchmarks | npm run bench, custom benchmark script | | Coverage | npm test -- --coverage |

Logging Format

Track iterations in TSV format:

iteration	change	metric_before	metric_after	delta	status	description
0	baseline	65.0	65.0	0.0	baseline	initial state
1	lowered minScore	65.0	70.0	+5.0	keep	improved retrieval
2	tried larger model	70.0	68.0	-2.0	revert	worse, reverted
3	added corpus entry	70.0	72.0	+2.0	keep	filled gap

Subagent Template

When spawning a subagent for autoresearch, use this template:

GOAL: [what to improve]
METRIC: [how to measure]
VERIFICATION: [command to run]
SCOPE: [files that can be modified]
MAX_ITERATIONS: [number]

CONSTRAINTS:

  • [resource limits]
  • [safety rules]
  • [reversibility requirements]
  • APPROACH: 1. Establish baseline 2. For each iteration: a. Identify next change b. Make ONE atomic change c. Run verification d. Compare to baseline e. Keep if improved, revert if worse f. Log result 3. Report final results

    Common Patterns

    Improving Benchmark Scores

    Goal: Improve benchmark score
    Metric: Benchmark output
    Changes: Config tweaks, corpus improvements, model changes
    Iterations: 5-10
    

    Fixing Tests

    Goal: All tests passing
    Metric: Test count failing
    Changes: Fix one test at a time
    Iterations: Until zero failures
    

    Reducing Bundle Size

    Goal: Bundle < 100KB
    Metric: Build output size
    Changes: Remove dependencies, tree-shake, minify
    Iterations: Until target met
    

    Increasing Coverage

    Goal: Coverage > 80%
    Metric: Coverage percentage
    Changes: Add tests for uncovered lines
    Iterations: Until target met
    

    Failure Handling

    | Failure | Response | |---------|----------| | Syntax error | Fix immediately, don't count as iteration | | Runtime error | Attempt fix (max 3 tries), then move on | | Resource exhaustion | Revert, try smaller variant | | Timeout | Revert, simplify approach | | External dependency failed | Skip, log, try different approach |

    Stopping Conditions

  • Goal metric reached
  • Max iterations hit
  • No improvement for 3 consecutive iterations
  • User interrupt (Ctrl+C or /stop)
  • References

    For advanced patterns, see:

  • references/workflows.md β€” Multi-step workflows
  • references/metrics.md β€” Common metric patterns
  • πŸ’‘ Examples

    Goal: Improve memory search Top-1 hit rate from 65% to 75%
    Metric: Benchmark score (openclaw cron runs --id  --limit 1)
    Scope: ~/.openclaw/workspace/MEMORY.md, ~/.openclaw/openclaw.json
    Max Iterations: 5
    

    Then run the loop manually or spawn a subagent to execute it.