apply-review-feedback

apply-review-feedback is a skill for Claude Code from juliusz-cwiakalski/agentic-delivery-os. It costs 15 tokens per session (620 once invoked), scanned A, original, MIT.

A workflow for sorting accepted, rejected, and unclear code-review comments, then applying the accepted feedback. It also records the review results and changed files for later inspection.

In plain words
What is it for?
Use it to process pull-request or merge-request feedback, update the affected files, create review artifacts, and prepare a summary before manually reviewing and committing.
Why use it?
It prevents review comments from being applied inconsistently or without a clear decision. The classification makes it easier to see what was changed and what still needs human review.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: model in frontmatter; mentions OpenCode.

Part of the ados plugin — 20 skills, 24 agents shipped together

Good fit Use it to process pull-request or merge-request feedback, update the affected files, create review artifacts, and prepare a summary before manually reviewing and committing.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/juliusz-cwiakalski/agentic-delivery-os/apply-review-feedback
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 juliusz-cwiakalski/agentic-delivery-os --skill apply-review-feedback
Clone the repo
git clone --depth 1 https://github.com/juliusz-cwiakalski/agentic-delivery-os

Made for: Claude Code.

Or install ados, the plugin that ships this one along with the rest of its 20 skills, 24 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 apply-review-feedback

README.md
[![agentmods](https://agentmods.dev/badge/skills/juliusz-cwiakalski/agentic-delivery-os/apply-review-feedback/github.svg)](https://agentmods.dev/skills/juliusz-cwiakalski/agentic-delivery-os/apply-review-feedback)
Your own site
<a href="https://agentmods.dev/skills/juliusz-cwiakalski/agentic-delivery-os/apply-review-feedback"><img src="https://agentmods.dev/badge/skills/juliusz-cwiakalski/agentic-delivery-os/apply-review-feedback/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 apply-review-feedback

Your own site · 80×15
<a href="https://agentmods.dev/skills/juliusz-cwiakalski/agentic-delivery-os/apply-review-feedback"><img src="https://agentmods.dev/badge/skills/juliusz-cwiakalski/agentic-delivery-os/apply-review-feedback.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 15 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 620 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 $0.00015 $0.00620
Opus 5 $0.00008 $0.00310
Sonnet 5 $0.00003 $0.00124
Haiku 4.5 $0.00002 $0.00062

Measured 12d ago against content hash 15dda930da14, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

apply-review-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 12d 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.

.ados-claude/skills/apply-review-feedback/SKILL.md · 61 lines

What it actually says

<output_format> <what_to_return>Classification summary (accepted/rejected/ambiguous counts), list of modified files, artifact paths under tmp/review-feedback/<branchPath>/, and reminder to review and commit manually.</what_to_return> </output_format>

<user_input>$ARGUMENTS</user_input>

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. 12d ago First seen · 61 lines · 15 tokens per session scan A 15dda930da14

Subscribe to this mod's changes

apply-review-feedback is a skill published in the GitHub repository juliusz-cwiakalski/agentic-delivery-os (38 stars, last pushed 2d ago), licensed MIT. It adds 15 tokens to every session and 620 once invoked, about $0.0001 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.

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