autoforge
AutoForge is a production-grade autonomous optimization framework for AI agents.
Skill by LeoYeAI
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/leoyeai-autoforge
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
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About
Use cases: documents, testing, security, agent-building
Reviews
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Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
- Runs as
- Python package
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/autoforge
What's inside
- Skill name
autoforge- Description
- AutoForge is a production-grade autonomous optimization framework for AI agents. It replaces subjective "reflection" with mathematically rigorous convergence loops — tracking every iteration in TSV, cross-validating with multiple models, and stopping only when pass rates confirm real improvement. Four specialized modes: prompt (skill & doc optimization via scenario simulation), code (sandboxed test execution with measurable criteria), audit (CLI verification against live tool behavior), and project (whole-repo cross-file consistency analysis). Battle-tested across 50+ iterations on production skills. Use when: user says "autoforge", "forge", "optimize skill", "improve", "run autoforge", "optimize code", "improve script", "optimize repo", "forge project", "check project", "repo audit".
- License
- MIT
- Instructions
- 484 lines in SKILL.md · view on GitHub
Before installing
- Ships executable scripts
Files (9)
- README.md
- SKILL.md
- _meta.json
- example-config.json · examples/
- eval-examples.md · references/
- ml-mode.md · references/
- email-prompt-proposed.md · results/
- report.sh · scripts/
- visualize.py · scripts/
SKILL.md (first lines)
# AutoForge — Autonomous Optimization Framework > Stop reflecting. Start converging. Every iteration is measured, logged, and validated — not vibed. AutoForge replaces ad-hoc "improve this" prompts with a rigorous optimization loop: define evals, run iterations, track pass rates in TSV, report live to your channel, and stop only when math says you're done. Multi-model cross-validation prevents the "same model grades its own homework" blind spot. **Four modes. One convergence standard.** | Mode | What it does | Best for | |------|-------------|----------| | `prompt` | Simulate 5 scenarios/iter, evaluate Yes/No | SKILL.md, prompts, doc templates | | `code` | Sandboxed test execution, measure exit/stdout/stderr | Shell scripts, Python tools, pipelines | | `audit` | Test CLI commands live, verify SKILL.md matches reality | CLI skill documentation | | `project` | Scan whole repo, cross-file consistency analysis | README↔CLI drift, Dockerfile↔deps, CI gaps | --- # AutoForge — Top-Agent Architecture ## Overview ``` Agent (you) ├── State: results.tsv, current target file state, iteration counter ├── Iteration 1: evaluate → improve → write TSV → report ├── Iteration 2: evaluate → improve → write TSV → report ├── ... └── Finish: report.sh --final → configured channel ``` ### Sub-Agent = You "Sub-Agent" is a **conceptual role**, not a separate process. You (the top-agent) execute each iteration yourself: simulate/execute → evaluate → write TSV → call report.sh. The templates below describe what you do PER ITERATION — not what you send to another agent. For code mode, run tests using the `exec` tool. ### Multi-Model Setup (recommended for Deep Audits) For complex audits, you can **split two roles across different models**: | Role | Model | Task | |------|-------|------| | **Optimizer** | Opus / GPT-4.1 | Analyzes, finds issues, writes fixes | | **Validator** | GPT-5 / Gemini (different model) | Checks against ground truth, provides pass rate | **Flow:** Optimizer and Validator alternate. Optimizer iterations have status `improved`/`retained`/`discard`. Validator iterations confirm or refute the pass rate. Spawn validators as sub-agents with `sessions_spawn` and explicit `model`. **When to use Multi-Model:** Deep Audits (>5 iterations expected), complex ground truth, or when a single model is blind to its own errors. **When Single-Model suffices:** Simple CLI audits, prompt optimization, code with clear tests. --- ## Configuration AutoForge uses environment variables for reporting. All are optional — without them, output goes to stdout. | Variable | Default | Description | |----------|---------|-------------| | `AF_CHANNEL` | `telegram` | Messaging channel for reports | | `AF_CHAT_ID` | _(none)_ | Chat/group ID for report delivery | | `AF_TOPIC_ID` | _(none)_ | Thread/topic ID within the chat | --- ## Hard Invariants These rules apply **always**, regardless of mode: 1. **TSV is mandatory.** Every iteration writes exactly one row to `results/[target]-results.tsv`. 2. **Reporting is mandatory.** Call `report.sh` immediately after every TSV row. 3. **--dry-run never overwrites the target.** Only TSV, `*-proposed.md`, and reports are written. 4. **Mode isolation is strict.** Only execute steps for the assigned mode. 5. **Iteration 1 = Baseline.** Evaluate the original version unchanged, status `baseline`. --- ## Modes — Read ONLY Your Mode! You are assigned ONE mode. **Ignore all sections for other modes.** | Mode | What happens | Output | |------|-------------|--------| | `prompt` | Mentally simulate skill/prompt, evaluate against evals | Improved prompt text | | `code` | Run tests in sandbox, measure results | Improved code | | `audit` | Test CLI commands (read-only only!) + verify SKILL.md against reality | Improved SKILL.md | | `project` | Scan whole repo, cross-file analysis, fix multiple files per iteration | Improved repository | **Your mode is in the task prompt.** Everything else is irrelevant to you. --- ## TSV Format (same for ALL modes) ### Header (once at loop start): ```bash printf '%s\t%s\t%s\t%s\t%s\n' "iteration" "prompt_version_summary" "pass_rate" "change_description" "status" > results/[target]-results.tsv ``` ### Row per iteration: ```bash printf '%s\t%s\t%s\t%s\t%s\n' "1" "Baseline" "58%" "Original version" "baseline" >> results/[target]-results.tsv ``` > **Use `printf` not `echo -e`!** `echo -e` interprets backslashes in field values. `printf '%s'` outputs strings literally. ### 5 columns, TAB-separated, EXACTLY this order: | # | Column | Type | Rules | |---|--------|------|-------| | 1 | `iteration` | Integer | 1, 2, 3, ... | | 2 | `prompt_version_summary` | String | Max 50 Unicode chars. No tabs, no newlines. | | 3 | `pass_rate` | String | Number + `%`: `58%`, `92%`, `100%`. Always integer. | | 4 | `change_description` | String | Max 100 Unicode chars. No tabs, no newlines. | | 5 | `status` | Enum | Exactly one of: `baseline` · `improved` · `retained` · `discard` | ### Escaping rules: - **Tabs** in text fields → replace with spaces - **Newlines** in text fields → replace with ` | ` - **Empty fields** → use hyphen `-` (never leave empty) - **`$` and backticks** → use `printf '%s'` or escape with `\$` (prevents unintended variable interpolation)
From LeoYeAI/openclaw-master-skills, MIT.
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Signals
- Stars
- 2,168
- Forks
- 325
- Open issues
- 13
- Last commit (this folder)
- May 11, 2026 (5 months ago)
- Last release
- Jul 20, 2026 (3 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Website
- myclaw.ai
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Cite this page
autoforge skill. Agent Mart. https://agentmart.dev/skills/leoyeai-autoforge (last updated 2026-10-24)