Loading skill
Install any skill in seconds. Free to start, no credit card required.
Get Started Free →autoforge
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 proj
Passed 5 of 14 test cases with the skill, and 1 of 14 without it.
7 fixed1 passed either way14 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
The instructions your model reads when the skill loads.
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 |
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" 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.
For complex audits, you can split two roles across different models:
| Role | Model | Task |
test cases passed, vs 1 / 14 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.