← Back to Skills Marketplace
fantaclaw-ai

Fanta Autoresearch

by fantaclaw-ai · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ⚠ suspicious
95
Downloads
0
Stars
0
Active Installs
1
Versions
Install in OpenClaw
/install fanta-autoresearch
Description
Autonomous goal-directed iteration for optimization and improvement tasks. Use when you need to systematically improve a metric, optimize a system, or iterat...
README (SKILL.md)

Autoresearch Skill

Run autonomous iteration loops: Goal → Metric → Loop (make change → verify → keep/revert → 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 → keep. If worse → revert. If error → fix or skip.
7. Log the result
8. Repeat until goal reached or max iterations

Principles

  1. One change per iteration — Atomic changes. If it breaks, you know why.
  2. Mechanical verification only — No subjective "looks good." Use metrics.
  3. Automatic rollback — Failed changes revert instantly.
  4. Git is memory — Each experiment is committed. Git revert preserves history.
  5. Simplicity wins — 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 \x3Cjob-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 \x3Cjob-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 \x3C 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:

Usage Guidance
This skill appears to do what it says: automate iterative experiments. It does, however, assume it can run arbitrary verification and background commands. Before installing/using it: - Only supply trusted verification commands (the verify argument is executed via the shell). Avoid passing untrusted strings that could execute unintended shell actions. - If you intend to run autonomous subagents or background execs, constrain their scope (limit modified file paths, use a sandbox or ephemeral environment, set resource/time limits). - Review and, if needed, modify scripts to avoid shell=True style execution or to use safer argument lists to reduce injection risk. - Check where logs are written (default: autoresearch-log.tsv) and ensure sensitive data is not captured there. - Don't grant additional credentials or environment variables to this skill unless you understand and approve each use case (the skill itself does not request any). If you want extra assurance, run the Python script in a controlled environment first and/or have a developer replace subprocess.run(..., shell=True) with a safer invocation that passes args without shell expansion.
Capability Analysis
Type: OpenClaw Skill Name: fanta-autoresearch Version: 1.0.0 The skill bundle provides a framework for autonomous optimization loops but contains a shell injection vulnerability in 'scripts/autoresearch-loop.py' via the use of 'subprocess.run(shell=True)' on unsanitized command strings. Additionally, 'SKILL.md' encourages the agent to employ high-risk capabilities such as spawning subagents ('sessions_spawn') and running background processes ('exec') to achieve its goals. While these features are aligned with the stated purpose of 'autoresearch', the combination of autonomous execution and lack of input validation poses a significant security risk.
Capability Assessment
Purpose & Capability
Name/description (autonomous iterative optimization) match the provided SKILL.md, reference docs, and the included Python helper. Requested resources (none) are consistent with an instruction-only/tooling skill for running iterations and verification commands.
Instruction Scope
SKILL.md instructs agents to run verification commands, spawn subagents (sessions_spawn), and use exec with background continuation. Those are coherent for long-running optimization loops, but they grant the skill the ability to run arbitrary shell commands and persistent tasks — the instructions also reference modifying files (e.g., ~/.openclaw/workspace/MEMORY.md, openclaw.json) which fits the stated scope but should be explicitly limited by the user when running autonomously.
Install Mechanism
No install spec; skill is instruction + a small Python script. Nothing is downloaded or written by an installer during install, which reduces supply-chain risk.
Credentials
Skill declares no environment variables, no credentials, and no unusual config paths. The files and commands referenced (openclaw cron, local workspace files, common test/build tools) are appropriate for the described purpose.
Persistence & Privilege
always:false and default autonomous invocation are set. The skill does not request permanent inclusion or elevated platform privileges. It can spawn background processes per SKILL.md, which is expected for long-running loops but increases runtime blast radius if misused.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install fanta-autoresearch
  3. After installation, invoke the skill by name or use /fanta-autoresearch
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Initial release. Provides autonomous iteration loops for optimization tasks: Goal → Metric → Loop (make change → verify → keep/revert → repeat). Includes workflow patterns, metric references, and interactive loop executor script.
Metadata
Slug fanta-autoresearch
Version 1.0.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 1
Frequently Asked Questions

What is Fanta Autoresearch?

Autonomous goal-directed iteration for optimization and improvement tasks. Use when you need to systematically improve a metric, optimize a system, or iterat... It is an AI Agent Skill for Claude Code / OpenClaw, with 95 downloads so far.

How do I install Fanta Autoresearch?

Run "/install fanta-autoresearch" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Fanta Autoresearch free?

Yes, Fanta Autoresearch is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Fanta Autoresearch support?

Fanta Autoresearch is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Fanta Autoresearch?

It is built and maintained by fantaclaw-ai (@fantaclaw-ai); the current version is v1.0.0.

💬 Comments