analyzing-release-readiness

analyzing-release-readiness is a skill for Claude Code from aws/agent-toolkit-for-aws. It costs 96 tokens per session (4,958 once invoked), scanned A, original, Apache-2.0.

A pre-merge review of a GitHub pull request, GitLab merge request, or local branch. It checks whether the code change is correct, risky, and possible to roll back safely.

In plain words
What is it for?
Use it to review a pull request or merge request, assess a local branch before merging, and produce a structured list of findings.
Why use it?
It helps identify release risks before changes are merged, when fixes are usually easier and safer to make.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the aws-agents-for-devsecops plugin — 13 skills, 9 commands, 1 MCP server shipped together

Good fit Use it to review a pull request or merge request, assess a local branch before merging, and produce a structured list of findings.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/aws/agent-toolkit-for-aws/analyzing-release-readiness
About the project

Agent Toolkit for AWS is a collection of AWS-supported MCP servers, skills, plugins, commands, and hooks that help AI coding agents build, deploy, and manage applications on AWS. It is used by developers working with AWS services through agents such as Claude Code, Codex, Cursor, and Kiro. The catalogue entries are the toolkit's own agent extensions for AWS development and operations.

aws/agent-toolkit-for-aws · 2,550 stars · on GitHub

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 aws/agent-toolkit-for-aws --skill analyzing-release-readiness
Clone the repo
git clone --depth 1 https://github.com/aws/agent-toolkit-for-aws

Made for: Claude Code.

Or install aws-agents-for-devsecops, the plugin that ships this one along with the rest of its 13 skills, 9 commands, 1 MCP server.

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 analyzing-release-readiness

README.md
[![agentmods](https://agentmods.dev/badge/skills/aws/agent-toolkit-for-aws/analyzing-release-readiness.svg)](https://agentmods.dev/skills/aws/agent-toolkit-for-aws/analyzing-release-readiness)
Your own site
<a href="https://agentmods.dev/skills/aws/agent-toolkit-for-aws/analyzing-release-readiness"><img src="https://agentmods.dev/badge/skills/aws/agent-toolkit-for-aws/analyzing-release-readiness.svg" alt="Measured on agentmods" height="20"></a>
Per session 96 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,958 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. Third-party audits
  • Socket pass 18 Jun 2026
  • Snyk warn 18 Jun 2026
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 $0.00096 $0.04958
Opus 5 $0.00048 $0.02479
Sonnet 5 $0.00019 $0.00992
Haiku 4.5 $0.00010 $0.00496

Measured 8d ago against content hash 60daab48edba, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

analyzing-release-readiness 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 8d 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.

plugins/aws-agents-for-devsecops/skills/analyzing-release-readiness/SKILL.md · 410 lines

How it starts

The opening of the file, as written. The whole thing — 410 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Release Readiness Review

AgentSpace routing (SigV4 only): If list_agent_spaces is available in your tool list and the multi-space orchestration skill has NOT been invoked yet this session, invoke it first to determine which agent_space_id to use. Then pass agent_space_id on all tool calls below. For bearer token auth this is unnecessary — the token is already scoped to one space.

Run a release readiness review via the AWS DevOps Agent. Analyzes a code change for risk, correctness, and potential rollback issues. Returns a structured report with actionable findings.

Rules:

  • If a PR/MR URL is provided: Extract ALL fields from the URL. Do NOT inspect the local workspace or git state.
  • NEVER use gh CLI, glab CLI, or any external tool to fetch PR/MR details. All required fields (repository, prNumber/mergeRequestIid, hostname) MUST be parsed directly from the URL or user input. The DevOps Agent fetches the content itself — you only need to pass identifiers.
  • Only use the local workspace flows when the user references a repository or package without a PR/MR link.

Gathering execution parameters

Infer everything automatically from the user's request — do not ask for parameters that can be derived.

Input source decision tree:

Has the user provided a pull request/merge request link or ID?
├── Yes: github.com PR URL               → use "GitHub PR" flow below
├── Yes: gitlab.com MR URL               → use "GitLab MR" flow below
└── No link provided — repo name only    → use "Local GitHub/GitLab repo" flow below

GitHub PR (github.com URL or PR reference)

  • Parse the input to extract fields — do NOT attempt a web fetch unless fields cannot be determined from the input.
  • repository (required): owner/repo from the PR URL
  • At least one of the following is required: headSha (commit SHA), headBranch (branch name), prNumber (PR number as a string, e.g. "8" not 8)
  • hostname: Extract from the URL (e.g., github.com or a self-hosted hostname)
  • Pass these fields to create_release_readiness_review under content.githubPrContent as an array of objects (even for a single PR).

Read the full file on GitHub · 410 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. 8d ago First seen · 410 lines · 96 tokens per session scan A 60daab48edba

Subscribe to this mod's changes

analyzing-release-readiness is a skill published in the GitHub repository aws/agent-toolkit-for-aws (2,550 stars, last pushed 3d ago), licensed Apache-2.0. It adds 96 tokens to every session and 4,958 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 2026-08-30.

Related

Other skills, from other repositories

review-implement-phase

Implements triaged review actions, commits focused fixes, and posts Done plus resolves threads. Use when the user wants only the implementation phase of the review-framework workflow.

prisma/orm · 38 tokens

engram-branch-pr

PR creation workflow for Engram following the issue-first enforcement system. Trigger: When creating a pull request, opening a PR, or preparing changes for review.

Gentleman-Programming/engram · 37 tokens

verify-behavior

Verify or reproduce visible product behavior by driving the real UI with pi-computer-use's checked tools, requiring verified expect postconditions and durable state evidence for meaningful UI flows. Use when triage needs visual reproduction, implementation needs behavioral proof, review needs interactive confirmation…

nicknisi/dotfiles · 68 tokens

github-contributor

End-to-end playbook for shipping high-quality pull requests to open-source projects you don't maintain — discovery, CONTRIBUTING compliance, PR-size check, minimal-diff implementation, PR description with AI-assisted disclosure, conflict resolution, and post-submission maintainer interaction. Use whenever creating…

daymade/claude-code-skills · 133 tokens

revdiff

Review diffs, files, and documents with inline annotations in a TUI overlay, or answer questions about revdiff usage, configuration, themes, and keybindings. Opens revdiff in agterm/tmux/zellij/herdr/kitty/wezterm/cmux/ghostty/iterm2/emacs-vterm, captures annotations, and addresses them. Works in git, hg, and jj repos…

umputun/revdiff · 248 tokens

write-pr

Reference standards for writing pull request titles and descriptions in the tldraw repository, plus the pre-flight comment sweep over the diff. Use as supporting guidance when another skill or workflow needs PR content standards, not as the user-facing create/update PR workflow.

tldraw/tldraw · 53 tokens