Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add jeremylongworth-source/AgentSkills --skill skill-evaluation-iterationgit clone --depth 1 https://github.com/jeremylongworth-source/AgentSkillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/jeremylongworth-source/agentskills/skill-evaluation-iteration)<a href="https://agentmods.dev/skills/jeremylongworth-source/agentskills/skill-evaluation-iteration"><img src="https://agentmods.dev/badge/skills/jeremylongworth-source/agentskills/skill-evaluation-iteration/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/jeremylongworth-source/agentskills/skill-evaluation-iteration"><img src="https://agentmods.dev/badge/skills/jeremylongworth-source/agentskills/skill-evaluation-iteration.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00064 | $0.00520 |
| Opus 5 | $0.00032 | $0.00260 |
| Sonnet 5 | $0.00013 | $0.00104 |
| Haiku 4.5 | $0.00006 | $0.00052 |
Grade A, and why
skill-evaluation-iteration scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 54 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill Evaluation Iteration
Core Workflow
- Define the target behavior and realistic user scenarios before editing the skill.
- Run or simulate a baseline without the skill when practical.
- Run or simulate the same scenario with the skill.
- Compare outputs by decision quality, missing steps, correctness, concision, safety, tool use, validation, and deliverable usefulness.
- Patch the skill to close observed gaps. Keep changes small and domain-specific.
- Validate the skill folder with the official validator.
- Repeat only when the new test reveals a material gap.
Output Contract
For skillset improvement work, return:
- Scenarios evaluated
- Rubric used
- Gaps found
- Skills changed
- Validation run
- Remaining risks or deferred improvements
Patch Decision Rule
Patch a skill only when the test reveals a concrete trigger, workflow, output, validation, freshness, or routing gap. Do not edit skills just to make them longer.
Evaluation Criteria
- Trigger accuracy: skill activates for the right requests and avoids unrelated ones.
- Metadata sync:
agents/openai.yamlstill reflects the currentSKILL.mdpurpose, especially after major edits. - Account routing: account-level instructions mention the skill when it should be globally discoverable.
- Context efficiency: SKILL.md is concise and references are loaded only when needed.
- Procedural value: skill changes the agent's workflow, not just wording.
- Quality bar: output includes acceptance criteria, validation, and domain-specific checks.
- Robustness: skill handles edge cases, constraints, and missing context.
- Maintainability: skill avoids stale facts unless it includes a freshness rule.
- Hygiene: generated scaffold placeholders, TODOs, stale examples, and irrelevant boilerplate are removed.
When To Forward-Test
Forward-test when the skill is complex, high-impact, or repeatedly used. Use realistic prompts and raw artifacts. Avoid leaking expected answers into the test prompt.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago First seen · 54 lines · 64 tokens per session scan A d649a2c06d63
skill-evaluation-iteration is a skill published in the GitHub repository jeremylongworth-source/AgentSkills (1 stars, last pushed 9d ago), licensed MIT. It adds 64 tokens to every session and 520 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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