agent-optimization
Improve an Agent State through versioned scores and score-linked Traces from a frozen Benchmark.
npx skills add Prism-Shadow/penguin-harness --skill agent-optimization --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Agent Optimization Improve one Test Agent through an evidence → hypothesis → Candidate → evaluation → accept or rollback loop. Use public Statements, scores, and Test Traces as black-box feedback. Delegate every evaluation to an `agent-evaluation` subagent; never run or score the Test Agent directly. ## Before you start If the request does not identify the Test Agent, frozen Benchmark, desired target score, positive Run count, and round limit, ask for the missing inputs. When they are already supplied, proceed without asking the user to restate them. ## Goal and contract Require an explicit Test Agent, a frozen Benchmark with a complete valid Formal Baseline, a desired target score, a positive `runs` value, and a positive round limit. `runs` is the number of Runs per Case for every Candidate in this optimization Session. Freeze it for the Session; do not infer it from `benchmark_config.toml` or the Formal Baseline. Read the evaluation `(provider, model_id, thinking_level)` from the complete Evaluation that matches the current Agent State; do not require the user to repeat it. An Evaluation without any part of this runtime is incomplete and cannot be used as a Reference. The top-l
- Before you start
- Goal and contract
- Access and changes
- Optimization loop
- Build and roll back a Candidate
- Delegate evaluation
- Record and report
What does the agent-optimization skill do?
Improve an Agent State through versioned scores and score-linked Traces from a frozen Benchmark.
How do I install it?
Run `npx skills add Prism-Shadow/penguin-harness --skill agent-optimization --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From Prism-Shadow/penguin-harness, a repository with 473 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.