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 caoyuan-fire/engi-foundry-skill --skill engifoundry-reviewgit clone --depth 1 https://github.com/caoyuan-fire/engi-foundry-skillWrote 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/caoyuan-fire/engi-foundry-skill/engifoundry-review)<a href="https://agentmods.dev/skills/caoyuan-fire/engi-foundry-skill/engifoundry-review"><img src="https://agentmods.dev/badge/skills/caoyuan-fire/engi-foundry-skill/engifoundry-review/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/caoyuan-fire/engi-foundry-skill/engifoundry-review"><img src="https://agentmods.dev/badge/skills/caoyuan-fire/engi-foundry-skill/engifoundry-review.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 | $0.00048 | $0.02095 |
| Opus 5 | $0.00024 | $0.01047 |
| Sonnet 5 | $0.00010 | $0.00419 |
| Haiku 4.5 | $0.00005 | $0.00210 |
Grade A, and why
engifoundry-review 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 11d 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 — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
EngiFoundry Review
Reviewer Selection And PAK Review Chain
This section applies only to the controlling Agent arranging Review. A session that received engifoundry.reviewer-task/v1 is already the Reviewer Worker; it skips selection and must not invoke or select another Reviewer.
Read the complete project-owned Executor configuration and the complete schema referenced by its schemaRef. Use the configured reviewer; never replace it with a host-native subagent or another available CLI merely because that mechanism has lower overhead.
For a package PAK, the first Planning, Job, or rework Review establishes one PAK Review chain by invoking the configured Reviewer in a genuinely fresh context through its verified CLI usage. Later Reviews for the same PAK default to continuing the same Reviewer session, across Planning, different Jobs, and rework. This continuity lets the Reviewer retain its prior findings and observe whether corrections converge; it does not authorize implementation or controlling work.
The chain is eligible only while the Reviewer can inspect the complete primary subject and evidence, perform every required check with verified available tools, did not perform or repair the reviewed work, and has not been contaminated by implementation discussion or an intended conclusion. The same model as the producer is acceptable because independence comes from the clean chain boundary and separation from implementation, not a different model.
Start a new genuinely fresh Reviewer context when the PAK's authorized contract, design, scope, acceptance boundary, or authority changes materially; the Reviewer performed or repaired implementation; continuation is unavailable or cannot be trusted; or the user explicitly requests an independent opinion. The new context receives the prior immutable Review records for that PAK as evidence so it can recover the finding history without receiving implementation discussion. A continuation or session handle is transient host state: never write it to project records, task envelopes, or handbacks.
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.
- 11d ago First seen · 82 lines · 48 tokens per session scan A f1537dd33662
engifoundry-review is a skill published in the GitHub repository caoyuan-fire/engi-foundry-skill (2 stars, last pushed 17d ago), licensed Apache-2.0. It adds 48 tokens to every session and 2,095 once invoked, about $0.0002 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.
Other skills, from other repositories
vibe-review
Review a proposed change against requirements, regressions, verification evidence, and relevant security risks.
kn-review
Use when reviewing implemented code before committing — multi-perspective review with severity-based findings.
kn-doc
Use when working with Knowns documentation - viewing, searching, creating, or updating docs.
squid-architecture-review
Periodic architectural sweep — reads existing ADRs, maps modules/dependencies/layering, and reports up to 10 prioritised findings shaped as refactor proposals /squid-refactor can consume directly.
squid-implement-night
Run the full agent-team pipeline end-to-end for one feature whose Tasks Plan is already approved by /squid-plan, handing the human a validated, ready-to-squash-merge PR. Trigger after /squid-plan.
squid-review
Push the committed feature branch, create or update its PR, then run Product Architect acceptance and PR-Reviewer on it. Output: a clean PR with no blockers, or ONE rollup task. Trigger after a feature's tasks are implemented and committed.