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.
npx agentmods add skills/masonegger/bpe-claude-code-plugin/apply-reviewnpx skills add MasonEgger/bpe-claude-code-plugin --skill apply-reviewgit clone --depth 1 https://github.com/MasonEgger/bpe-claude-code-pluginWrote 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.
[](https://agentmods.dev/skills/masonegger/bpe-claude-code-plugin/apply-review)<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="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00020 | $0.01248 |
| Opus 5 | $0.00010 | $0.00624 |
| Sonnet 5 | $0.00004 | $0.00250 |
| Haiku 4.5 | $0.00002 | $0.00125 |
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 5d 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.
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-
shipentries under sub-headings, in this exact order (most-destructive first):- 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. - Completely off, do this instead (
redirect): list each unit's heading + comment. These are full rewrites driven by the comment. - Close, but update (
update): list each unit's heading + comment. These are tweaks. - No decision recorded (
unset): list any units that have a comment (those'll be treated asupdate) and any that don't (those'll be left alone).
- Absolutely reject and delete (
- The global comment, if non-empty.
- The artifact path that will be modified.
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.
- 5d ago First seen · 85 lines · 20 tokens per session scan A d225814c1bd7
apply-review is a skill published in the GitHub repository MasonEgger/bpe-claude-code-plugin (7 stars, last pushed 3d ago), licensed MIT. It adds 20 tokens to every session and 1,248 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 2026-08-31.
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