Borrowing it
Nothing to install: this file belongs to Undertone0809/rudder. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Undertone0809/rudder/main/.agents/skills/maintainer/product-acceptance-verifier-maintainer/SKILL.mdgit clone --depth 1 https://github.com/Undertone0809/rudderWrote 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/undertone0809/rudder/product-acceptance-verifier-maintainer)<a href="https://agentmods.dev/skills/undertone0809/rudder/product-acceptance-verifier-maintainer"><img src="https://agentmods.dev/badge/skills/undertone0809/rudder/product-acceptance-verifier-maintainer/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/undertone0809/rudder/product-acceptance-verifier-maintainer"><img src="https://agentmods.dev/badge/skills/undertone0809/rudder/product-acceptance-verifier-maintainer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00071 | $0.01730 |
| Opus 5.5 | $0.00028 | $0.00692 |
| Sonnet 5.5 | $0.00014 | $0.00346 |
| Haiku 4.5 | $0.00007 | $0.00173 |
Grade A, and why
product-acceptance-verifier-maintainer 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 13d 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 — 184 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Acceptance Verifier Maintainer
Act as the independent acceptance boundary between “the implementation checks passed” and “the requested product outcome was observed.”
Exclusive Outcome
End with exactly one verdict:
PASS: the requested terminal behavior was observed in the required environment, including the highest-risk adjacent state.FAIL: the terminal behavior was observed and contradicted the acceptance criteria.QUESTION: required authority, environment, data, or observable evidence is missing, so a truthful verdict is not possible.
Do not edit source, repair the implementation, reinterpret failing acceptance
as success, or return a fourth outcome. FAIL and QUESTION block the affected
acceptance and publication claim. They do not prohibit local checkpoint commits
or independent work. Only the parent or implementer may fix and request a new run.
First-Principles Boundary
Use this skill only when all of these are true:
- There is a concrete terminal behavior to accept.
- Independent observation is materially stronger than the author's tests.
- The environment or state matters to the claim.
Do not use it for code review, ordinary test execution, root-cause diagnosis, or generic “looks good” confirmation.
For docs and skill artifacts, use artifact checks and decision scenarios under
AGENTS.md section 9.1 instead of inventing a Rudder runtime acceptance task.
Verification Lease
A verifier verdict is a lease on one immutable acceptance candidate, not a general endorsement of a branch or feature. Before testing, record:
- commit SHA and branch
- dirty state plus a scoped diff or content fingerprint when the candidate is uncommitted
- build or artifact identifier and the source used to create it
- runtime/process identity, URL, and version or health response
- organization/account and relevant data or fixture identity
- acceptance packet version and verification timestamp
Build or restart the target after the fingerprint is locked when source changes
could affect the running surface. Before returning the verdict, recapture the
same identity. Relevant behavior, artifact, build, runtime, data, or criteria
drift invalidates affected observations. Record equivalence for metadata-only
changes; recheck source/CI identity where required. Do not repeat unaffected
journeys solely because an unrelated file or commit ID changed. Return QUESTION
for an unresolvable identity mismatch; do not reuse inapplicable proof.
What ships with it
1 file 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.
- 13d ago Changed · +13 lines 8fadf250a309
- 1mo ago Changed · +12 lines a377f18f30f9
- 2mo ago First seen · 159 lines · 71 tokens per session scan A a11de6e2ac91
product-acceptance-verifier-maintainer is a skill published in the GitHub repository Undertone0809/rudder (294 stars, last pushed yesterday), licensed Apache-2.0. It adds 71 tokens to every session and 1,730 once invoked, about $0.0003 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 2026-08-30.
Other skills, from other repositories
verify-omnigent
Drive Omnigent the way a user does and prove a behavior with recorded evidence, using an isolated server, runner, and mock model plus a feature map of every user entry point. Load before reproducing a user-facing bug, before claiming a fix works, or when reviewing whether a change covered every surface (session vs.…
test-master
Generates test files, creates mocking strategies, analyzes code coverage, designs test architectures, and produces test plans and defect reports across functional, performance, and security testing disciplines. Use when writing unit tests, integration tests, or E2E tests; creating test strategies or automation…
eval-dataset-design
A guide to creating reliable test sets for AI agents. Each evaluation task includes the request, the simulated user's behaviour, a reset starting state, and a success check that can be independently verified.
dogfood
Systematically explore and test a mobile app on iOS/Android with agent-device to find bugs, UX issues, and other problems. Use when asked to dogfood, QA, exploratory test, find issues, bug hunt, or test this app on mobile.
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.
critique
Adversarial design critique of the current work. Render it, look at it, and argue for rejection. Run after the gates are green, never instead of them.