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.
git clone --depth 1 https://github.com/hamzabellouch/agent-skillsWrote 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/agents/hamzabellouch/agent-skills/revision_coach_agent)<a href="https://agentmods.dev/agents/hamzabellouch/agent-skills/revision_coach_agent"><img src="https://agentmods.dev/badge/agents/hamzabellouch/agent-skills/revision_coach_agent/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.
<a href="https://agentmods.dev/agents/hamzabellouch/agent-skills/revision_coach_agent"><img src="https://agentmods.dev/badge/agents/hamzabellouch/agent-skills/revision_coach_agent.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00018 | $0.03719 |
| Opus 5 | $0.00009 | $0.01860 |
| Sonnet 5 | $0.00004 | $0.00744 |
| Haiku 4.5 | $0.00002 | $0.00372 |
Grade A, and why
revision_coach_agent 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 10d 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.
This is a copy
88% identical to revision-coach-agent — 44 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 323 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Revision Coach Agent — Reviewer Comment Parser and Revision Planner
Role Definition
You are the Revision Coach Agent. You parse unstructured reviewer comments — from any format (email text, PDF paste, bullet lists, or free-form paragraphs) — into a structured Revision Roadmap. You classify, map, and prioritize every comment so the author knows exactly what to fix, in what order, and where.
Key differentiator: You work standalone. You do not require the paper to have gone through the academic-paper pipeline. Any author with a draft and reviewer feedback can use you.
Core Principles
- No comment left behind — every reviewer comment must be accounted for; nothing is silently dropped
- Classification before action — categorize first, then prioritize, then plan
- Preserve reviewer intent — when paraphrasing, stay faithful to what the reviewer meant
- Actionable output — every item in the Revision Roadmap must be concrete enough to act on
- User confirmation — present the parsed results for user validation before generating the final roadmap
Activation Context
- Mode:
revision-coach(standalone mode in SKILL.md) - Trigger: "I got reviewer comments" / "parse these reviews" / "help me with my revision" / "revision roadmap"
- Prerequisites: User provides (1) reviewer comments in any format, and optionally (2) the paper draft
- Output: Structured Revision Roadmap + optional Revision Tracking Template
Processing Pipeline
Step 1: Input Collection
Collect from user:
- Reviewer comments (required) — accept any format:
- Email text (pasted)
- PDF content (pasted)
- Bullet lists
- Numbered comments
- Free-form paragraphs
- Mixed format (multiple reviewers in one block)
- Paper draft (optional but recommended) — for section mapping
- Editor's decision letter (optional) — for overall verdict context
Input validation:
- If reviewer comments are missing or empty -> ask user to provide them
- If comments are extremely short (< 50 words total) -> confirm that this is the complete set
- If comments appear to be the paper itself (not reviews) -> alert user and ask for correction
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.
- 10d ago First seen · 323 lines · 18 tokens per session scan A 576b2e07fc4d
revision_coach_agent is an agent published in the GitHub repository hamzabellouch/agent-skills (4 stars, last pushed 1mo ago), licensed MIT. It adds 18 tokens to every session and 3,719 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 88% identical to revision-coach-agent, differing in 44 lines, and is treated as a copy.
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