AutoSkillit: Skill for Claude Code

.claude/skills/review-promotion/SKILL.md

review-promotion is a skill for Claude Code from TalonT-Org/AutoSkillit. It costs 52 tokens per session (2,950 once invoked), scanned A, original, MIT.

A reviewer-focused skill for assessing changes being promoted from one development branch to another. It examines risk, breaking changes, regressions, test coverage, and dependencies across affected areas.

In plain words
What is it for?
Reviewing integration-to-main promotions, scoring domain risks, auditing breaking changes, comparing test coverage, checking cross-domain dependencies, and optionally posting a report to a pull request.
Why use it?
It gives reviewers a structured view of whether a promotion is safe to approve and what might break.

Skill for Claude Code

Written for Claude Code: installed under .claude/. Also seen: mentions subagents; mentions Claude Code.

This is TalonT-Org/AutoSkillit's own configuration. It tells Claude Code how to work on AutoSkillit itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything AutoSkillit configures →

Reuse

Borrowing it

Nothing to install: this file belongs to TalonT-Org/AutoSkillit. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/TalonT-Org/AutoSkillit/main/.claude/skills/review-promotion/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/TalonT-Org/AutoSkillit

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.

agentmods badge for review-promotion

README.md
[![agentmods](https://agentmods.dev/badge/skills/talont-org/autoskillit/review-promotion/github.svg)](https://agentmods.dev/skills/talont-org/autoskillit/review-promotion)
Your own site
<a href="https://agentmods.dev/skills/talont-org/autoskillit/review-promotion"><img src="https://agentmods.dev/badge/skills/talont-org/autoskillit/review-promotion/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.

agentmods 80×15 button for review-promotion

Your own site · 80×15
<a href="https://agentmods.dev/skills/talont-org/autoskillit/review-promotion"><img src="https://agentmods.dev/badge/skills/talont-org/autoskillit/review-promotion.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 52 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,950 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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00052 $0.02950
Opus 5 $0.00026 $0.01475
Sonnet 5 $0.00010 $0.00590
Haiku 4.5 $0.00005 $0.00295

Measured 10d ago against content hash 4bc9946946fb, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

review-promotion 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 10d 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-promotion/SKILL.md · 406 lines

How it starts

The opening of the file, as written. The whole thing — 406 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Review Promotion

Perform deep reviewer-facing analysis of an integration-to-main promotion. This skill partitions all changed files by domain, runs parallel domain risk analysis, assesses test coverage and breaking changes, synthesizes a reviewer verdict, and optionally posts the review report as a PR comment.

Arguments

/autoskillit:review-promotion [batch_branch] [base_branch] [--post-to-pr]
  • batch_branch (optional) — source branch to analyze. Defaults to develop.
  • base_branch (optional) — target branch. Defaults to main.
  • --post-to-pr — if present, post the review report as a comment on the open promotion PR.

When to Use

  • Before approving a promotion PR
  • When you need a structured risk assessment across all changed domains
  • When you want an automated reviewer's guide with a go/no-go verdict

Critical Constraints

NEVER:

  • Create files outside .autoskillit/temp/review-promotion/
  • Modify any source code — this skill is read-only analysis
  • Use gh pr comment --body inline — always use --body-file
  • Fail silently if gh is unavailable when --post-to-pr — output verdict = review_ready and exit 0

ALWAYS:

  • Output report_path = <absolute path> as a structured token (absolute path, prepend CWD)
  • Output verdict = <value> as a structured token

Workflow

Phase 0: Setup

Step 0.1: Parse Arguments

Parse optional positional arguments and flags:

  • batch_branch — default "develop" if absent or empty
  • base_branch — default "main" if absent or empty
  • post_to_prtrue if --post-to-pr present in ARGUMENTS
Step 0.2: Compute Divergence Point
git merge-base {base_branch} {batch_branch}
git diff --name-only {base_branch}..{batch_branch}
git diff --name-only --diff-filter=A {base_branch}..{batch_branch}
git diff --name-only --diff-filter=M {base_branch}..{batch_branch}

Store as merge_base_sha, changed_files, new_files (added files), and modified_files (modified files).

Read the full file on GitHub · 406 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. 10d ago First seen · 406 lines · 52 tokens per session scan A 4bc9946946fb

Subscribe to this mod's changes

review-promotion is a skill published in the GitHub repository TalonT-Org/AutoSkillit (5 stars, last pushed today), licensed MIT. It adds 52 tokens to every session and 2,950 once invoked, about $0.0003 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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