An automated review coordinator for repositories containing AI agents or applications built with large language models. It detects which specialist reviews apply, combines their findings, and produces one overall grade and scorecard.

Use it to review applicable areas such as product value, agent reliability, token costs, and user experience, then get scores and a cross-cutting list of fixes.

It avoids running separate reviews by hand and removes duplicate findings from overlapping checks. The result gives one prioritized view of whether the project is ready to ship.

report-card — Skill for Claude Code by vikast908

Install

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.

Any agent

npx skills add vikast908/agent-repo-card --skill report-card

Clone the repo

git clone --depth 1 https://github.com/vikast908/agent-repo-card

Made for: Claude Code.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/vikast908/agent-repo-card/report-card/github.svg)](https://agentmods.dev/skills/vikast908/agent-repo-card/report-card)

Your own site

<a href="https://agentmods.dev/skills/vikast908/agent-repo-card/report-card"><img src="https://agentmods.dev/badge/skills/vikast908/agent-repo-card/report-card/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/vikast908/agent-repo-card/report-card"><img src="https://agentmods.dev/badge/skills/vikast908/agent-repo-card/report-card.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 99 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,204 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

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	$0.00099	$0.01204
Opus 5	$0.00049	$0.00602
Sonnet 5	$0.00020	$0.00241
Haiku 4.5	$0.00010	$0.00120

Measured 11d ago against content hash 5729b784b465, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

report-card 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.

skills/report-card/SKILL.md · 72 lines

How it starts

The opening of the file, as written. The whole thing — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Repo report card (orchestrator)

You are a lead reviewer running a quality gate on an AI-agent repo. You don't re-derive every review yourself — you run the specialist checks that apply, then synthesize their results into one honest, evidence-backed verdict: should this ship?

Protocol (shared across all checks)

Plan first (default). Present a short plan: which reviews you'll run (after applicability detection), how you'll run them, and the combined output. Ask "Proceed with the full report card, or adjust scope?" and wait. Skip if invoked with auto / "just do it".
Evidence rule. Every finding keeps its file:line from the sub-review. Never invent or inflate. If a sub-review was skipped, say why.
Severity: Critical / High / Medium / Low.
Score: combine sub-scores into a weighted overall 0–100 → grade (90+ A, 75+ B, 60+ C, 40+ D, else F).
Output inline, then offer to save to agent-review/report-card.md.

Step 1 — Detect what applies

Scan the repo and decide which reviews are relevant. Don't run reviews that don't apply.

Signal (how to detect)	Reviews it turns on
Calls an LLM — provider SDKs (`anthropic`, `openai`, `@google/genai`, `cohere`, `ollama`…), model IDs, prompt strings	`token-efficiency`, `prompt-quality`, `agent-eval-coverage`
Has an agent / tool loop — a model→tool→model loop, `tool_call`/`function_call`, tool dispatch	`agent-reliability`
Has tools, secrets, or untrusted input — `exec`/`subprocess`/`eval`, HTTP/file/DB tools, `.env`, RAG/scraping	`agent-security`
Has a UI — `*/.{tsx,jsx,vue,svelte,astro}`, HTML/CSS, component dirs	`ux-audit`, `accessibility-audit`
Always (any product)	`product-review`

Report which reviews you turned on and which you skipped, with the reason.

Step 2 — Run the applicable reviews

Preferred (fast): dispatch each applicable review as a parallel subagent (Agent tool), each instructed to run the corresponding skill in auto mode and return its scorecard + Critical/High findings as compact structured data. Run them concurrently, then collect.

Read the full file on GitHub · 72 lines

Changes

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 · 72 lines · 99 tokens per session scan A 5729b784b465

Subscribe to this mod's changes

report-card is a skill published in the GitHub repository vikast908/agent-repo-card (1 stars, last pushed 2mo ago), licensed MIT. It adds 99 tokens to every session and 1,204 once invoked, about $0.0005 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.

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Use when auditing a diff or repository against the plumb-line principles — finds laundered uncertainty, boundary leaks, hardcoded priors, overstated maturity, outputs lacking recorded lineage, and baseline drift with no explanation. Read-only: it reports, never auto-fixes.

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report-card