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-build-samplesgit 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-build-samples)<a href="https://agentmods.dev/skills/product-on-purpose/agent-skills-toolkit/askit-build-samples"><img src="https://agentmods.dev/badge/skills/product-on-purpose/agent-skills-toolkit/askit-build-samples/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-build-samples"><img src="https://agentmods.dev/badge/skills/product-on-purpose/agent-skills-toolkit/askit-build-samples.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 | $0.00056 | $0.00620 |
| Opus 5 | $0.00028 | $0.00310 |
| Sonnet 5 | $0.00011 | $0.00124 |
| Haiku 4.5 | $0.00006 | $0.00062 |
Grade A, and why
askit-build-samples 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 10d 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 — 29 lines — stays where its author put it; the contents beside it link to each section on GitHub.
askit-build-samples
Purpose
Author and validate the evidence a skill carries, following the builder pattern (../../docs/reference/builder-pattern.md). create generates a skill's samples (at least 3 golden examples plus at least 1 anti-example, Standard sec 7.2) under examples/, and its triggering eval set (at least 20 {query, should_trigger} cases, sec 8.3) plus any chain/hook behavior cases under evals/ in the eval-set format the G3 library-regression check and the askit-evaluate behavioral mode consume. validate detects drift: a sample or eval that no longer matches the skill's current behavior is an error, not a silent staleness. Format and the example-threads convention are in references/samples-format.md.
When to use
When generating samples or an eval set for a skill, or checking existing samples and evals for drift after a behavior change.
create mode
- Read the target skill (its description, triggers, and behavior).
- Generate
examples/golden samples (>= 3 realistic input/output pairs) and an anti-example (>= 1 case the skill should NOT handle), per sec 7.2. - Generate
evals/<name>.eval.json: a triggering set of >= 20{query, should_trigger}cases (fires when it should, silent when it should not, sec 8.3), plus{given, expect}behavior cases for any chain the skill participates in (so the G3 check finds coverage).
validate mode
- Re-run the samples and evals against the skill's current behavior.
- Flag drift: a golden sample whose output changed, an anti-example that now triggers, or an eval whose expectation no longer holds. Drift is an error so samples stay honest (sec 7.2, 8.3).
Scope
Samples and eval sets are the evidence layer. The deterministic G3 baseline (presence + the regression signal) is enforced by library-regression; behavioral judging of the cases is the opt-in askit-evaluate behavioral mode (delegated to askit-quality-grader), never the CI gate (Design Principle 3). Example-threads (the bounded validation triad of ADR 0021: a greenfield Bronze plugin, the pm-skills adopt-and-grade thread, and the toolkit itself as Gold) anchor samples to real end-to-end arcs rather than isolated snippets.
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.
- 10d ago First seen · 29 lines · 56 tokens per session scan A ef1726886e25
askit-build-samples 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 56 tokens to every session and 620 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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