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-benchmark-designgit 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-benchmark-design)<a href="https://agentmods.dev/skills/jeremylongworth-source/agentskills/skill-benchmark-design"><img src="https://agentmods.dev/badge/skills/jeremylongworth-source/agentskills/skill-benchmark-design/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-benchmark-design"><img src="https://agentmods.dev/badge/skills/jeremylongworth-source/agentskills/skill-benchmark-design.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 | $0.00047 | $0.00520 |
| Opus 5 | $0.00023 | $0.00260 |
| Sonnet 5 | $0.00009 | $0.00104 |
| Haiku 4.5 | $0.00005 | $0.00052 |
Grade A, and why
skill-benchmark-design 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 8d 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 — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill Benchmark Design
Core Workflow
- Name the skill or bundle under test, target user, target artifact, and decision the benchmark must support.
- Select realistic scenarios that exercise normal, edge, and risky requests without leaking expected answers into the prompt.
- Define baseline and skill-enabled runs using the same prompt, source material, and acceptance criteria.
- Choose scoring criteria from
docs/evaluation/skill-quality-rubric.mdand add task-specific pass/fail checks. - Specify evidence capture: prompt, inputs, outputs, reviewer notes, validation output, loaded context, tools/scripts, and overhead when available.
- Set the decision rule: keep, revise, split, merge, defer, or retire.
Overhead And Composition Checks
- Include one repeated-run or batch scenario when the skill may be used many times in one thread.
- Compare monolithic guidance against composed skills or skillsets when the workflow has separable stages.
- Capture activated skills, loaded references, command/script output, MCP tool use, artifacts, and validation results.
- Keep host optimizations optional. Benchmark Claude Code
context: fork, dynamic!commandinjection, or MCP Tool Search separately from the portable AgentSkills baseline. - Do not report exact context or token savings unless the host exposes reliable measurements.
Safety Rules
- Do not use private customer, employee, security, or financial data in public benchmark artifacts.
- Do not optimize prompts by exposing the expected answer.
- Do not treat an automated judge as sufficient for legal, financial, security, employment, medical, or production-impacting workflows.
- Keep tracing, scoring, and observability tools optional and vendor-neutral.
Deliverable Shape
For benchmark plans, provide:
- Skill or bundle under test
- Target artifact and target user
- Scenarios and source material
- Baseline and skill-enabled run plan
- Acceptance criteria and scoring rubric
- Evidence capture plan
- Overhead and composition checks
- Safety boundaries
- Promotion decision rule
What ships with it
2 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.
- 8d ago First seen · 65 lines · 47 tokens per session scan A 5a4099bb5627
skill-benchmark-design is a skill published in the GitHub repository jeremylongworth-source/AgentSkills (1 stars, last pushed 9d ago), licensed MIT. It adds 47 tokens to every session and 520 once invoked, about $0.0002 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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