apply-review

apply-review is a skill for Claude Code from MasonEgger/bpe-claude-code-plugin. It costs 20 tokens per session (1,255 once invoked), scanned A, original, MIT.

A command that applies saved comments from a browser-based review to the reviewed Markdown document. It reads the review decisions and changes the document after confirmation.

In plain words
What is it for?
Use it after a /bpe:review session to load its feedback file, check whether the feedback is recent, and apply the requested changes.
Why use it?
It avoids manually copying review notes and helps ensure the user's accepted edits are applied to the correct document.

Skill for Claude Code

Written for Claude Code: disable-model-invocation in frontmatter. Also seen: model in frontmatter.

Part of the bpe plugin — 13 skills, 3 agents shipped together

Good fit Use it after a /bpe:review session to load its feedback file, check whether the feedback is recent, and apply the requested changes.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/masonegger/bpe-claude-code-plugin/apply-review
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 MasonEgger/bpe-claude-code-plugin --skill apply-review
Clone the repo
git clone --depth 1 https://github.com/MasonEgger/bpe-claude-code-plugin

Made for: Claude Code.

Or install bpe, the plugin that ships this one along with the rest of its 13 skills, 3 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

README.md
[![agentmods](https://agentmods.dev/badge/skills/masonegger/bpe-claude-code-plugin/apply-review/github.svg)](https://agentmods.dev/skills/masonegger/bpe-claude-code-plugin/apply-review)
Your own site
<a href="https://agentmods.dev/skills/masonegger/bpe-claude-code-plugin/apply-review"><img src="https://agentmods.dev/badge/skills/masonegger/bpe-claude-code-plugin/apply-review/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

Your own site · 80×15
<a href="https://agentmods.dev/skills/masonegger/bpe-claude-code-plugin/apply-review"><img src="https://agentmods.dev/badge/skills/masonegger/bpe-claude-code-plugin/apply-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 20 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,255 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
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • medium Excessive Agency · line 5
    Skill selects an external model or provider that may use a different account or billing plan than the operator expects. Undisclosed model switches can cause unexpected cost or quota consumption.
    Fix: Remove the model/provider override or disclose it prominently and require explicit operator approval before invoking an external coding CLI or billed model.
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 $0.00020 $0.01255
Opus 5.5 $0.00008 $0.00502
Sonnet 5.5 $0.00004 $0.00251
Haiku 4.5 $0.00002 $0.00126

Measured 1mo ago against content hash 13acc19ec3dd, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

apply-review 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 1mo 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.

bpe/skills/apply-review/SKILL.md · 85 lines

How it starts

The opening of the file, as written. The whole thing — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Apply Review Command

Consume the feedback JSON written by /bpe:review and apply the user's annotations to the reviewed markdown artifact. The user has already reviewed in the browser and clicked Save; this command surfaces their decisions and applies the changes after explicit confirmation.

Step 1: Find the Feedback File

If the user passed a feedback path as an argument, use that file. This is the escape hatch for replaying an older review or for the case where the user ran two reviews in parallel and the wrong one is "most recent."

Otherwise, find the most recent feedback JSON in the temp directory (/bpe:review generates the path with mktemp, which honors $TMPDIR):

ls -t "${TMPDIR:-/tmp}"/bpe-review-*-feedback.json 2>/dev/null | head -1

If none exists, tell the user to run /bpe:review first and stop.

If the chosen file's saved_at timestamp is more than 24 hours old, surface that to the user and ask whether they meant to apply this older feedback or run /bpe:review again.

Step 2: Load and Summarize

Read the feedback JSON. Expected shape:

{
  "sections": [
    {
      "id": "section-N",
      "heading": "<heading text>",
      "decision": "ship" | "update" | "redirect" | "reject" | "unset",
      "comment": "<free-text feedback>"
    }
  ],
  "global_comment": "<free-text>",
  "artifact_path": "<absolute path to the reviewed markdown file>",
  "saved_at": "<ISO timestamp>"
}

Each entry is one decision unit; the review is intentionally fine-grained, so expect many entries. Display a summary that front-loads the destructive decisions, since those are what the user most needs to verify before saying yes in Step 3:

  • One-line counts header: N reject, N redirect, N update, N unset, N ship.
  • Then group the non-ship entries under sub-headings, in this exact order (most-destructive first):
    1. Absolutely reject and delete (reject): list each unit's heading + comment. These are about to be removed; surface them first so the user can catch a mistake before authorizing.
    2. Completely off, do this instead (redirect): list each unit's heading + comment. These are full rewrites driven by the comment.
    3. Close, but update (update): list each unit's heading + comment. These are tweaks.
    4. No decision recorded (unset): list any units that have a comment (those'll be treated as update) and any that don't (those'll be left alone).
  • The global comment, if non-empty.
  • The artifact path that will be modified.

Read the full file on GitHub · 85 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. 1mo ago Changed 13acc19ec3dd
  2. 2mo ago First seen · 85 lines · 20 tokens per session scan A d225814c1bd7

Subscribe to this mod's changes

apply-review is a skill published in the GitHub repository MasonEgger/bpe-claude-code-plugin (7 stars, last pushed 27d ago), licensed MIT. It adds 20 tokens to every session and 1,255 once invoked, about $0.0001 per session on Opus 5.5. 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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