feedback

A workflow for handling comments from reviewers on a pull request, which is a proposed code change awaiting review. It records the comments in feedback.md, applies fixes, and updates the pull request.

In plain words
What is it for?
Use it after a pull request receives review comments to inspect the discussion, document the feedback, make code changes, and push the updates.
Why use it?
It prevents review requests from being lost or handled inconsistently. The permanent record shows what reviewers raised and how each point was resolved.

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/abilenduke/copilot-developer/feedback
Any agent
npx skills add ABilenduke/copilot-developer --skill feedback
Clone the repo
git clone --depth 1 https://github.com/ABilenduke/copilot-developer

Made for: Claude Code, Codex.

Per session 58 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,649 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 $0.00058 $0.01649
Opus 5 $0.00029 $0.00825
Sonnet 5 $0.00012 $0.00330
Haiku 4.5 $0.00006 $0.00165

Measured yesterday against content hash 47b561d1364f, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

feedback 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 yesterday.

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/feedback/SKILL.md · 179 lines

How it starts

The opening of the file, as written. The whole thing — 179 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Feedback Skill

Purpose

This skill handles the review cycle after /execute creates a PR. It reads PR comments, creates an immutable feedback.md record in the story directory, applies fixes, and updates the PR. The feedback document is a permanent historical record of what was raised and how it was resolved.

Pipeline position: /execute → PR created → reviewer comments → /feedback → feedback.md + PR updates → merge → Done


First Steps

When this skill is invoked:

  1. Read the template: templates/feedback-template.md
  2. Identify the PR:
    • If the user specified a PR number, use that
    • If on a feature branch, check for an open PR:
      ~/.local/bin/gh pr list --head "$(git branch --show-current)" \
        --repo ABilenduke/content-engine --json number,title --jq '.[0]'
      
    • If no PR found, ask the user for the PR number
  3. Read PR details:
    ~/.local/bin/gh pr view {number} --repo ABilenduke/content-engine \
      --json title,body,reviews,comments,reviewDecision
    
  4. Locate the story directory: Find the story that produced this PR by:
    • Checking the PR description for story document paths
    • Checking the branch name for feature/story naming
    • Asking the user if neither works
  5. Read existing story documents: Load plan.md and design.md for context on what was intended
  6. Read PR review comments:
    # General PR comments
    ~/.local/bin/gh pr view {number} --repo ABilenduke/content-engine --comments
    
    # Inline code review comments
    ~/.local/bin/gh api repos/ABilenduke/content-engine/pulls/{number}/comments \
      --jq '.[] | {path: .path, line: .line, body: .body, user: .user.login}'
    
    # Review summaries
    ~/.local/bin/gh api repos/ABilenduke/content-engine/pulls/{number}/reviews \
      --jq '.[] | {state: .state, body: .body, user: .user.login}'
    
  7. Begin processing feedback

Process

Phase 1: Gather & Categorize Feedback

Read the full file on GitHub · 179 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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. yesterday First seen · 179 lines · 58 tokens per session scan A 47b561d1364f

Subscribe to this mod's changes

feedback is a skill published in the GitHub repository ABilenduke/copilot-developer (4 stars, last pushed 6mo ago), licensed MIT. It adds 58 tokens to every session and 1,649 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.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

babysit-pr

Babysit a GitHub pull request after creation by continuously polling review comments, CI checks/workflow runs, and mergeability state until the PR is merged/closed or user help is required. Diagnose failures, retry likely flaky failures up to 3 times, auto-fix/push branch-related issues when appropriate, and keep…

openai/codex · 114 tokens

imagegen

Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output…

openai/codex · 113 tokens

cpu-profile-analysis

Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…

microsoft/vscode · 71 tokens

next-cache-components-optimizer

Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…

vercel/next.js · 170 tokens