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 kellykampen/agent-skills --skill evidence-driven-testinggit clone --depth 1 https://github.com/kellykampen/agent-skillsWrote 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/kellykampen/agent-skills/evidence-driven-testing)<a href="https://agentmods.dev/skills/kellykampen/agent-skills/evidence-driven-testing"><img src="https://agentmods.dev/badge/skills/kellykampen/agent-skills/evidence-driven-testing/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/kellykampen/agent-skills/evidence-driven-testing"><img src="https://agentmods.dev/badge/skills/kellykampen/agent-skills/evidence-driven-testing.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 | $0.00055 | $0.00582 |
| Opus 5 | $0.00028 | $0.00291 |
| Sonnet 5 | $0.00011 | $0.00116 |
| Haiku 4.5 | $0.00006 | $0.00058 |
Grade A, and why
evidence-driven-testing 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 — 61 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Evidence-Driven Testing
Record annotated screen-recording proof of UI behavior, then attach it to the PR and tracker issue.
Inputs
- Test targets (required): The behaviors/flows to verify, phrased as testable statements.
- PR / issue (optional): Where to post the evidence. If omitted, deliver to the requester only.
Instructions
1. Prepare the screen
- Maximize the browser/app window; close popups, notifications, and extra panels.
- Navigate to the starting state (logged in, correct page) BEFORE recording, unless setup itself is under test.
2. Start recording
- Begin the screen recording before the first meaningful action.
- Add a
setupannotation describing the starting context, e.g. "Logged in, navigating to connectors page".
3. Annotate as you test
- At each named test's start, add a
test_startannotation in Jest style:It should execute the tool directly when permission is 'always'. - After each check, add an
assertionannotation with resultpassed,failed, oruntested. - Rules for assertions:
- One assertion per meaningful state change — consolidate, don't annotate per UI label.
- Use "Precondition: ..." assertions to establish starting state.
- Keep under ~80 characters, high-signal.
- If a test cannot run (missing prerequisite, expired auth window), mark it
untestedwith the reason — never skip silently.
4. Stop and review
- Stop recording after the final assertion.
- Confirm the recording captured the key moments before sharing.
5. Post the evidence
- Write a short report: what was tested, environment + exact commit, pass/fail per test, caveats.
- Post the video + summary as a PR comment (embed in the PR description if it's your PR).
- Attach the same video to the tracker issue (Linear/Jira) with a one-line result.
- Send the report + recording to the requester.
Guardrails
- Never record a half-covered or tiled window — maximize first.
- When verifying a fix, show or reference the old failure alongside the new success.
- Always state the exact commit/branch/deployment tested against.
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 · 61 lines · 55 tokens per session scan A 2f61da4fe875
evidence-driven-testing is a skill published in the GitHub repository kellykampen/agent-skills (2 stars, last pushed 1mo ago), licensed MIT. It adds 55 tokens to every session and 582 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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