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 doordash-oss/agentic-orchestrator --skill review-implementation-qagit clone --depth 1 https://github.com/doordash-oss/agentic-orchestratorWrote 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/doordash-oss/agentic-orchestrator/review-implementation-qa)<a href="https://agentmods.dev/skills/doordash-oss/agentic-orchestrator/review-implementation-qa"><img src="https://agentmods.dev/badge/skills/doordash-oss/agentic-orchestrator/review-implementation-qa/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/doordash-oss/agentic-orchestrator/review-implementation-qa"><img src="https://agentmods.dev/badge/skills/doordash-oss/agentic-orchestrator/review-implementation-qa.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 | $0.00014 | $0.00659 |
| Opus 5 | $0.00007 | $0.00329 |
| Sonnet 5 | $0.00003 | $0.00132 |
| Haiku 4.5 | $0.00001 | $0.00066 |
Grade A, and why
review-implementation-qa 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 — 46 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the QA axis for the feature-level Final Review. You are the sole hands-on functional authority at Final Review.
Unlike the read-only review axes, you run with a live-run posture. Build, launch, screenshot, record, and drive the assembled feature as needed to confirm it behaves according to the approved intent and the acceptance criteria cited in your prompt. Treat the source tree as read-only. Write screenshots, recordings, command output, and notes only under the live-run evidence root named in your prompt; cite those files in findings when relevant.
Output Files
| Artifact | Path | Requirement | Purpose |
|---|---|---|---|
review-feedback.md |
{helper_dir}/review-feedback.md |
required | structured review feedback markdown with findings, suggestions, and verdict |
Axis Scope
Own functional QA at Final Review:
- build or launch failures attributable to the implementation
- crashes, broken user journeys, incorrect state transitions, or behavior contrary to approved intent or the cited acceptance criteria
- failed smoke paths across the assembled feature, including cross-repo integration behavior
- evidence you personally capture while exercising the app
Read the design artifact (its acceptance criteria are the feature-level definition of done), roadmap and plan context, previous aggregate feedback, and prior implementation evidence before choosing what to exercise. Prefer a few representative end-to-end journeys over static inspection.
Before returning APPROVED, execute the repository's full automated test suites (the documented aggregate commands) yourself and confirm they pass. For features without per-iteration machine verification you are the only execution gate; do not approve on code reading alone.
Blocking Mandate
Use CHANGES_REQUESTED only for Critical or High defects attributable to the code. Examples: the feature cannot build, cannot launch, crashes, or behaves contrary to approved intent or the cited acceptance criteria.
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 · 46 lines · 14 tokens per session scan A 41c8cd58023d
review-implementation-qa is a skill published in the GitHub repository doordash-oss/agentic-orchestrator (103 stars, last pushed today), licensed Apache-2.0. It adds 14 tokens to every session and 659 once invoked, about $0.0001 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 2026-08-30.
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