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 product-on-purpose/agent-skills-toolkit --skill askit-evaluategit clone --depth 1 https://github.com/product-on-purpose/agent-skills-toolkitWrote 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/product-on-purpose/agent-skills-toolkit/askit-evaluate)<a href="https://agentmods.dev/skills/product-on-purpose/agent-skills-toolkit/askit-evaluate"><img src="https://agentmods.dev/badge/skills/product-on-purpose/agent-skills-toolkit/askit-evaluate/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/product-on-purpose/agent-skills-toolkit/askit-evaluate"><img src="https://agentmods.dev/badge/skills/product-on-purpose/agent-skills-toolkit/askit-evaluate.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 | $0.00072 | $0.00933 |
| Opus 5 | $0.00036 | $0.00466 |
| Sonnet 5 | $0.00014 | $0.00187 |
| Haiku 4.5 | $0.00007 | $0.00093 |
Grade A, and why
askit-evaluate 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 9d 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 — 37 lines — stays where its author put it; the contents beside it link to each section on GitHub.
askit-evaluate
Purpose
Assess a known, local component or plugin against STANDARD.md. Three modes. conformance (the default) runs the deterministic portable scripts and returns a per-rule report (pass / warn / error), the satisfied tier, and concrete remediation. behavioral runs a skill against its eval-set and judges whether it triggers and behaves as expected, delegating to askit-quality-grader. review forms a qualitative judgment (correctness, altitude, naming, whether a component is warranted), delegating to askit-reviewer. Only conformance is deterministic and gate-safe; behavioral and review are opt-in LLM-judged passes that produce evidence, never a CI gate result (Design Principle 3, ADR 0023).
When to use
When the user asks to evaluate, audit, or check a skill or plugin, asks "what tier is this" or "what is blocking the next tier" (conformance), asks whether a skill actually triggers and behaves correctly (behavioral), or wants a qualitative review (review).
conformance mode (default, deterministic)
- Determine the target path (a plugin root with
library.json, or a single skill directory withSKILL.md). - Run:
node scripts/evaluate.mjs <path> --json. - Present the findings grouped by rule, the tier (for a plugin), and the remediation. Lead with errors, then warnings.
- For a shareable, designed report, render the same object:
node scripts/evaluate.mjs <path> --format=html --out report.html(a self-contained page for a non-engineer) or--format=md(the Markdown twin for PR review and agents). It renders the same deterministic object the terminal shows, adds no judgment, and does not change the verdict. See references/report-format.md. - If there are warnings or errors, point the user at
askit-build-skillinimprovemode to fix them.
behavioral mode (opt-in, LLM-judged)
- Locate the target's eval-set under
evals/(triggering{query, should_trigger}cases and{given, expect}behavior cases). Theevals/convention is forward-looking: most real targets do not ship one. If it is absent, the grader DERIVES a case set instead - should-fire queries and adversarial near-misses from the target's description (read sibling skills' descriptions to make the no-fire cases genuinely competitive), plus behavior cases from the documented workflow - judges by static analysis of the artifact, and says so in the evidence. - Delegate to
askit-quality-grader: it runs the skill against the cases (on-disk or derived) and judges fire / no-fire and output quality. - Report the verdict per case with evidence. This is evidence, not a gate result; it never fails CI.
What ships with it
4 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.
- 9d ago First seen · 37 lines · 72 tokens per session scan A b60c465168ed
askit-evaluate is a skill published in the GitHub repository product-on-purpose/agent-skills-toolkit (2 stars, last pushed today), licensed Apache-2.0. It adds 72 tokens to every session and 933 once invoked, about $0.0004 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 2026-08-31.
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