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Prepare implementation review comments on a pull request

Prepare implementation review comments on a pull request is a skill for Claude Code from Intai/story-flow. It costs 42 tokens per session (2,791 once invoked), scanned A, original, MIT.

A pull-request review guide explains how to post one review containing evidence, risks, requirement links, screenshots, API calls, test results, and coverage gaps. A pull request is a proposed code change awaiting review.

In plain words
What is it for?
It helps inspect a pull request and publish summary and inline review comments, including BDD results and unit-test coverage.
Why use it?
It gives reviewers relevant context in one place and points comments to the code lines they concern.

Skill for Claude Code

Written for Claude Code: user-invocable in frontmatter.

Part of the story-flow plugin — 7 skills, 8 commands, 5 agents shipped together

not rated 16repo +2 changed today A scan 42 tokens original MIT

Good fit It helps inspect a pull request and publish summary and inline review comments, including BDD results and unit-test coverage.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/intai/story-flow/prepare-implementation-review-comments
View source ↗ Intai/story-flow
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 Intai/story-flow --skill prepare-implementation-review-comments
Clone the repo
git clone --depth 1 https://github.com/Intai/story-flow

Made for: Claude Code.

Or install story-flow, the plugin that ships this one along with the rest of its 7 skills, 8 commands, 5 agents.

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 Prepare implementation review comments on a pull request

README.md
[![agentmods](https://agentmods.dev/badge/skills/intai/story-flow/prepare-implementation-review-comments/github.svg)](https://agentmods.dev/skills/intai/story-flow/prepare-implementation-review-comments)
Your own site
<a href="https://agentmods.dev/skills/intai/story-flow/prepare-implementation-review-comments"><img src="https://agentmods.dev/badge/skills/intai/story-flow/prepare-implementation-review-comments/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 Prepare implementation review comments on a pull request

Your own site · 80×15
<a href="https://agentmods.dev/skills/intai/story-flow/prepare-implementation-review-comments"><img src="https://agentmods.dev/badge/skills/intai/story-flow/prepare-implementation-review-comments.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 42 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,791 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. 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 $0.00042 $0.02791
Opus 5.5 $0.00017 $0.01116
Sonnet 5.5 $0.00008 $0.00558
Haiku 4.5 $0.00004 $0.00279

Measured today against content hash 90b593c3e9f6, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

Prepare implementation review comments on a pull request scanned grade A with 1 finding 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 today.

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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

- For each operation below, use the host's MCP server tools if the project has one, else the host's CLI (e.g. `gh`, `az repos`, `glab`), else its REST API through `curl` with a token from the environment. If none works,
plugins/story-flow/skills/prepare-implementation-review-comments/SKILL.md · 105 lines

How it starts

The opening of the file, as written. The whole thing — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Prepare implementation review comments on a pull request

Overview

Helps reviewers by posting, as one review on each pull request:

  • inline comments on the lines they concern: risks, discussion points, screenshots, accessibility violations, API calls, failed BDD scenarios and coverage gaps
  • a summary of what belongs to no single line: TL;DR, reading order, requirement traceability, BDD results and coverage

Instructions

Phase 1: Find the code host

  • Work out the host from each PR URL given, else from git remote get-url origin (e.g. GitHub, Azure DevOps, GitLab).
  • For each operation below, use the host's MCP server tools if the project has one, else the host's CLI (e.g. gh, az repos, glab), else its REST API through curl with a token from the environment. If none works, tell the developer what to install or log into (/mcp to check status) and stop — never guess.
  • Operations:
    • Read the PR: number, author, URL, changed files, head commit and diff.
    • List the PR's existing comments, to find ones carrying a <!-- story-flow:prepare-implementation-review... --> marker.
    • Post a review: a summary body with inline comments, each on a file and a line on the new side of the diff. Where the host has no single review, post the summary and each inline comment separately (e.g. Azure DevOps threads, GitLab discussions).
    • Post a file-level comment, where the host supports one (e.g. GitHub subject_type: file).
    • Edit a comment.
    • Reply to a comment's thread.
    • Resolve a thread (e.g. GitHub resolveReviewThread through GraphQL, Azure DevOps thread status fixed, GitLab resolved=true).
    • Link to a file and line in the PR.
    • Host an image for a comment. Use the host's upload where it has one (e.g. Azure DevOps PR attachments, GitLab project uploads). Otherwise (e.g. GitHub), commit it to a pr-evidence orphan branch through a temporary git worktree, so the developer's checkout is untouched, push, and link the raw file at that commit.

Read the full file on GitHub · 105 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. today Changed 90b593c3e9f6
  2. 3d ago Changed · +4 lines 796473644314
  3. 6d ago Changed · +3 lines 9ade1f0818ee
  4. 7d ago Changed 4a2bb7a10bb9
  5. 8d ago First seen · 98 lines · 42 tokens per session scan A 53bd2f525c66

Subscribe to this mod's changes

Prepare implementation review comments on a pull request is a skill published in the GitHub repository Intai/story-flow (16 stars, last pushed yesterday), licensed MIT. It adds 42 tokens to every session and 2,791 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-22.

Claude CodeCode reviewTestingUnit testingagenticautomationbddclaudeqa
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