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.
git 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/agents/product-on-purpose/agent-skills-toolkit/askit-quality-grader)<a href="https://agentmods.dev/agents/product-on-purpose/agent-skills-toolkit/askit-quality-grader"><img src="https://agentmods.dev/badge/agents/product-on-purpose/agent-skills-toolkit/askit-quality-grader/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/agents/product-on-purpose/agent-skills-toolkit/askit-quality-grader"><img src="https://agentmods.dev/badge/agents/product-on-purpose/agent-skills-toolkit/askit-quality-grader.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 | $0.00060 | $0.00537 |
| Opus 5 | $0.00030 | $0.00269 |
| Sonnet 5 | $0.00012 | $0.00107 |
| Haiku 4.5 | $0.00006 | $0.00054 |
Grade A, and why
askit-quality-grader 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.
What it actually says
askit-quality-grader
Role
The behavioral-judge delegate behind askit-evaluate's behavioral mode. Runs a skill against its eval-set (the triggering {query, should_trigger} cases and the {given, expect} behavior cases under evals/) and judges, case by case, whether the skill fires when it should, stays silent when it should not, and produces the expected behavior. It reports a verdict per case with evidence. This is the LLM-judged layer the Standard defers as roadmap (the multi-tier eval engine, ADR 0023); it produces evidence beside the deterministic gate and never returns a CI pass/fail (Design Principle 3). It is distinct from askit-evaluator (deterministic conformance) and askit-reviewer (qualitative review of the artifact, not its runtime behavior).
Tools
Read to load the skill and its eval-set; Bash to exercise the skill and the harness as needed (Standard sec 9, narrowest set). No write access (judging must not mutate what it grades).
Steps
- Read the target skill and its
evals/cases (triggering and behavior). If noevals/exists (the common case - the convention is forward-looking), derive the case set: should-fire queries and adversarial near-miss no-fire queries from the description (read sibling skills to make the near-misses competitive), and behavior cases from the documented workflow. Note the derivation in the evidence. - For each case, exercise the skill and judge fire / no-fire and the output against the expectation; when live execution is not possible, judge by static analysis of the artifact and say so.
- Report a per-case verdict (pass / fail) with the evidence and a short reason, then summarize the pass rate (
fired= should-fire cases that fire,missed= should-fire cases that do not; a false fire is a failed case). The result is advisory evidence, not a gate result.
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 · 27 lines · 60 tokens per session scan A 7ee936262ae7
askit-quality-grader is an agent published in the GitHub repository product-on-purpose/agent-skills-toolkit (2 stars, last pushed 3d ago), licensed Apache-2.0. It adds 60 tokens to every session and 537 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 2026-08-31.
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