Borrowing it
Nothing to install: this file belongs to vladm3105/aidoc-flow-framework. 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/vladm3105/aidoc-flow-framework/dev/.agents/skills/preprod-review/SKILL.mdgit clone --depth 1 https://github.com/vladm3105/aidoc-flow-frameworkWrote 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/vladm3105/aidoc-flow-framework/preprod-review)<a href="https://agentmods.dev/skills/vladm3105/aidoc-flow-framework/preprod-review"><img src="https://agentmods.dev/badge/skills/vladm3105/aidoc-flow-framework/preprod-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/vladm3105/aidoc-flow-framework/preprod-review"><img src="https://agentmods.dev/badge/skills/vladm3105/aidoc-flow-framework/preprod-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 | $0.00054 | $0.02294 |
| Opus 5.5 | $0.00022 | $0.00918 |
| Sonnet 5.5 | $0.00011 | $0.00459 |
| Haiku 4.5 | $0.00005 | $0.00229 |
Grade A, and why
preprod-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 1mo 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 — 189 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pre-production review
Muse project-skill instance of the canon
preprod-reviewskill. Source:aidoc-flow-claude-agents-config/skills/preprod-review/SKILL.md. Engine-neutral wording mirrorsframework/skills/preprod-review/. Skill path in this repo:.agents/skills/preprod-review/. Where the canon names a Claude-specific agent type, use this harness's focused read-only subagent equivalent and keep the independence contract.
Reviewing something well before it ships is not one read — it is several independent reads from angles that a single reviewer conflates and therefore misses. A security lens and a docs lens hunting the same diff surface different truths; fused into one pass they blur. This skill fixes the lenses, runs them in parallel against real source, and forces every surviving finding through a verification step so the verdict is evidence, not vibes.
Use it before anything that widens blast radius:
- cutting a release or version tag
- promoting a library, template or workflow to a team/company default
- rolling a change out to N consumer repos
- a deploy or infrastructure change that is hard to reverse
- handing an artifact to adopters who cannot see its internals
Not for ordinary in-flight review of a PR or a work-in-progress branch —
that is /code-review. This is for the ship/elevate/rollout decision, where the
cost of being wrong is paid by people who were not in the room.
The shape
-
Scope the surface (you, inline — a few minutes). List every file the review must cover, then read the 2–3 most privileged or most load-bearing ones yourself, so you can spot-check findings later. What counts varies by artifact, but the shape is always entry points, whatever they trust, and the surfaces adopters read:
Artifact Typically CI / workflows .github/workflows/*.yml, install or distribution templates, the scripts they invoke, adopter docslibrary / service public API surface, config and defaults, migration steps, the release or deploy script infrastructure the changed resources, IAM/permission boundaries, the state and rollback path any of them the governance surfaces — CHANGELOG / ROADMAP / decisions log / handoff -
Fan out five lenses in parallel (one dispatch, five agents — see below). Each gets a scoped brief and returns a ranked report. Running them in one turn is the point: they finish together and don't inherit each other's framing.
-
Synthesize + verify (you, inline). Dedup across the five reports. For every BLOCKER and every HIGH, open the cited
file:lineand confirm the defect is real before it survives into your output. A plausible-sounding finding that doesn't reproduce against source is noise — cut it or mark it PLAUSIBLE with what would confirm it. -
Deliver one ranked verdict:
BLOCKER(do not ship),SHIP-WITH-FIXES(fixable, enumerated), orREADY. Lead with the verdict, then the ranked findings, then strong positives to not regress. -
Promote surviving findings (you, inline — the orchestrator owns this). Lenses must not file; their reports are raw synthesis input. Check every surviving BLOCKER and HIGH against the
submit-feedbackpromotion bar and file the ones that clear it viasubmit-feedback(search-first, body contract, safe publication). Link the issue numbers from the verdict so no finding dies at the transcript boundary.
What ships with it
2 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.
- 1mo ago First seen · 189 lines · 54 tokens per session scan A debd0799bf39
preprod-review is a skill published in the GitHub repository vladm3105/aidoc-flow-framework (17 stars, last pushed yesterday), licensed MIT. It adds 54 tokens to every session and 2,294 once invoked, about $0.0002 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 2026-09-28.
Other skills, from other repositories
author-labels
Decide which author label, if any, a pull request gets. Load before labeling a pull request.
yfm
YAML front matter (YFM) for any markdown article. x-cmd's default is three top-level fields: tags, description, memo. Default borrows the Agent Skills frontmatter shape — see . The yfm module is a helper: x yfm ls / init / lint.
check
Comprehensive rule-based check. Triggered by "/check [target]" to evaluate all source files against project rules and report every violation.
review
Review pull requests or code changes. Triggered by "/review [prlink]" or when user asks to review pending changes.
simplify
Review code for readability, reuse, and simplicity. Triggered by "/simplify" when user wants to refactor complex code, remove duplication, or improve clarity.
git
Git and code hosting platform management — GitHub, GitLab, Codeberg, Forgejo. Zero barrier — install x-cmd and manage repos from the terminal. Use for "git", "github", "gitlab", "repo", "pull request", "code hosting".