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 megamen32/LastHumanCommit --skill user-testinggit clone --depth 1 https://github.com/megamen32/LastHumanCommitWrote 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/megamen32/lasthumancommit/user-testing)<a href="https://agentmods.dev/skills/megamen32/lasthumancommit/user-testing"><img src="https://agentmods.dev/badge/skills/megamen32/lasthumancommit/user-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/megamen32/lasthumancommit/user-testing"><img src="https://agentmods.dev/badge/skills/megamen32/lasthumancommit/user-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 | $0.00031 | $0.00763 |
| Opus 5.5 | $0.00012 | $0.00305 |
| Sonnet 5.5 | $0.00006 | $0.00153 |
| Haiku 4.5 | $0.00003 | $0.00076 |
Grade A, and why
user-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 yesterday.
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
Real-use testing
Run at a major milestone and its final acceptance, roughly 3 hours of coherent work, not every hour, tool call or small edit. The boundary is a usable outcome, not an arbitrary elapsed deadline; do not wait three hours for a ready milestone or split a substantial outcome into fragments to evade independent testing. Technical tests continue throughout development.
Before code, define a tangible outcome: who is solving which real task, the before/after difference and a real entry point. For a bug start TDD immediately: reproduce the failing behavior RED, repair it GREEN, then check the original scenario. An internal refactor, dependency or SHA is a means toward an observable user/consumer outcome, not a substitute for it. API and CLI consumers count too.
This is user testing, not source review. Receive a plausible persona, a realistic goal and relevant conditions, the real surface and allowed test data. Receive no implementation history and not the expected verdict, changed control names, a success click recipe or other testers' findings. Source-informed regression replay is a different, explicitly labelled check.
Start fresh, without source code, implementation chats or internal explanations. In normal use, public help available to a real user is allowed. In a separately labelled first-contact/discoverability experiment, begin without hints. Do not confuse these experiments or pretend an experienced agent has no prior knowledge.
Actually perform actions with the working surface tool: browser automation or computer use for web, Agent Device where available for mobile/native devices, the real application for desktop, or a real shell invocation for CLI. Check actual resulting state. A screenshot, successful tool invocation or green process health is not alone proof of the user's goal. Record a tool/environment failure separately.
Use designated test identities and data. Exercise meaningful writes when the task and environment allow them. Messaging/payment/invitation flows should use the established test destination or sandbox, not unrelated real people or accounts. A missing login/surface is BLOCKED_REAL_SURFACE, not simulated success.
On a browser error, timeout or ambiguous state, capture and inspect a secret-safe screenshot in the owning session before retry, navigation or cleanup. If capture is unavailable, record the missing evidence and tool limitation.
Capture goal, actions tried, expected versus observed result, failure point, recovery attempt and useful evidence. Explain what helped, what confused you, what you liked/disliked, where you hesitated and whether the result solves your actual task. Preserve detailed candid feedback rather than compressing it into PASS/FAIL or only critical bugs. An honest "nothing notable" is better than invented praise or criticism; useful detail has no arbitrary short word quota. Classify a proven defect, usability obstacle, preference or unverified concern; blunt criticism needs an observed episode. Do not turn every preference into a mandatory feature, and do not throw preferences away before Lead has read them.
When both before/after versions are safe and available, a neutral comparison can show whether the tester independently notices the practical difference. Do not reveal which version is expected to win or replay harmful production defects. Otherwise prove the new behavior and retain the failing baseline/TDD separately; a tester who never saw the old version must not claim an observed comparison.
Return PASS, CHANGES_REQUIRED or BLOCKED_REAL_SURFACE. For repairs, replay the original failure and make a fresh uninstructed attempt when discoverability matters. Source-informed diagnosis and implementation belong to Worker, not the Tester.
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.
- yesterday Changed · +23 lines 202b63307a95
- 1mo ago First seen · 42 lines · 31 tokens per session scan A 6d20eff91948
user-testing is a skill published in the GitHub repository megamen32/LastHumanCommit (2 stars, last pushed yesterday), licensed MIT. It adds 31 tokens to every session and 763 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-05.
Other skills, from other repositories
browse
Drive a real browser through Aside: open a page, read it, click through a flow, take screenshots, check console errors. (gstack).
cua-driver
Use Cua Driver for desktop or browser tasks that are awkward or unavailable through Bash/APIs, or when the user explicitly wants GUI interaction: app testing, visual bug reproduction, form filling, calendar entry, screenshots, and demo recording. Also covers Cua setup; not OpenAI Codex Computer Use or web research.
create-verification-skill
Create a repo-local verification skill and exhaustive feature map for driving a real app through its UI, CLI, or API. Use for "create a verification skill", "make a verify skill for this repo", or "document how agents can verify this app".
browser-qa
A browser-based quality check for deployed web pages and user flows. It uses browser automation to test rendering, navigation, forms, interactions, responsive behaviour, and accessibility-related issues.
webapp-testing
Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.
test-electron-app
Drive the real running PostHog Electron app (live tRPC, workspace-server, real data) over CDP with agent-browser. Connect to the running app on port 9222, test desktop changes against a local Django stack, snapshot the accessibility tree, inspect network requests, and screenshot only when explicitly asked. Use when…