review-prep

review-prep is a skill for Claude Code, Codex from kintecus/build. It costs 83 tokens per session (1,093 once invoked), scanned A, original, MIT.

A short preparation brief for a project review or sync. It reads project records and highlights decisions needed, recently closed decisions, stalled or risky work, and the most useful meeting questions.

In plain words
What is it for?
Use it before a project review, status meeting, or sync to identify what needs attention and which questions are worth asking.
Why use it?
It surfaces relationships and problems that may be missed when reading project documents one at a time. The result is a quick checklist for a busy operator before a review.

Skill for Claude CodeCodex

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.

agentmods
npx agentmods add skills/kintecus/build/review-prep
Any agent
npx skills add kintecus/build --skill review-prep
Clone the repo
git clone --depth 1 https://github.com/kintecus/build

Made for: Claude Code, Codex.

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.

agentmods badge for review-prep

README.md
[![agentmods](https://agentmods.dev/badge/skills/kintecus/build/review-prep.svg)](https://agentmods.dev/skills/kintecus/build/review-prep)
Your own site
<a href="https://agentmods.dev/skills/kintecus/build/review-prep"><img src="https://agentmods.dev/badge/skills/kintecus/build/review-prep.svg" alt="Measured on agentmods" height="20"></a>
Per session 83 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,093 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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.

ModelPer sessionOnce invoked
Fable 5 $0.00083 $0.01093
Opus 5 $0.00042 $0.00547
Sonnet 5 $0.00017 $0.00219
Haiku 4.5 $0.00008 $0.00109

Measured 4d ago against content hash 433564854e72, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

review-prep 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 4d 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.

.claude/skills/review-prep/SKILL.md · 56 lines

How it starts

The opening of the file, as written. The whole thing — 56 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Review Prep

Produce a 2-minute punch list that an operator opens before a project review. The job is not to summarize the docs - it is to derive meaning across them: surface what needs a human decision, what quietly stalled, and what no single doc says on its own.

Which project

If the user named a project, use it. If they ran this from inside a project context, use that. If ambiguous, ask which project (one short question, do not guess).

How to run it - use a fresh-context agent

Dispatch a fresh general-purpose agent to read the artifacts and produce the doc. Do NOT do the reading-and-synthesis yourself in the main conversation - if you have been working on the project this session, your context is polluted by what you already know, and you will miss exactly the cross-artifact findings that make this valuable (the whole point is catching what the operator's own working memory misses). Hand the agent only the project root and these instructions.

The agent reads, in order:

  1. STATUS.md - current state, active threads, open questions, next actions
  2. docs/INDEX.md - the one-line index of every doc; read this to know what exists before opening anything, then open only the live threads (judge from dates - what is active now)
  3. The most recent / most active docs/ files - not all of them, just the ones the index shows are live
  4. decisions/*.md - the decision log

Pass the agent today's date explicitly.

Output format - exactly these five sections

1. Project pulse - one line per active thread (skip dormant ones). Format: thread - RED/AMBER/GREEN - the single most important fact about its current state (source: filename). Judge the color: green = moving, amber = waiting/at-risk, red = blocked or overdue.

2. Decisions waiting on the operator - the 3-7 things that genuinely need the operator's judgment now. Each phrased as a crisp question, with a source. Do NOT pad - if it's already decided, it doesn't belong here. Quality over quantity.

Read the full file on GitHub · 56 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.

  1. 4d ago First seen · 56 lines · 83 tokens per session scan A 433564854e72

Subscribe to this mod's changes

review-prep is a skill published in the GitHub repository kintecus/build (2 stars, last pushed 1mo ago), licensed MIT. It adds 83 tokens to every session and 1,093 once invoked, about $0.0004 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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