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 skills add Rezenders/scientific_writing_agent --skill response-to-reviewersgit clone --depth 1 https://github.com/Rezenders/scientific_writing_agentWrote 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/rezenders/scientific_writing_agent/response-to-reviewers)<a href="https://agentmods.dev/skills/rezenders/scientific_writing_agent/response-to-reviewers"><img src="https://agentmods.dev/badge/skills/rezenders/scientific_writing_agent/response-to-reviewers/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/skills/rezenders/scientific_writing_agent/response-to-reviewers"><img src="https://agentmods.dev/badge/skills/rezenders/scientific_writing_agent/response-to-reviewers.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 | $0.00049 | $0.00541 |
| Opus 5.5 | $0.00020 | $0.00216 |
| Sonnet 5.5 | $0.00010 | $0.00108 |
| Haiku 4.5 | $0.00005 | $0.00054 |
Grade A, and why
response-to-reviewers 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.
How it starts
The opening of the file, as written. The whole thing — 51 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Response to Reviewers
Full resubmission response workflow for journal/conference reviews.
Workflow
Phase 0 — Parse and Categorize
Parse all reviewer comments into a structured inventory with IDs (R1.1, R1.2, R2.1…), category (technical / writing / consistency / scope / question), and identified manuscript location. Present inventory to user for confirmation before continuing.
Phase 1 — Technical Verification (technical comments only)
Use .agents/agents/paper-implementation-checker.md on technical claims.
Implementation reference paths:
- Primary implementation artifact:
<IMPLEMENTATION_REFERENCE> - Experiment or analysis scripts:
<IMPLEMENTATION_REFERENCE> - Generated data/tables:
<IMPLEMENTATION_REFERENCE> - Public interfaces, schemas, or configuration:
<IMPLEMENTATION_REFERENCE>
Surface DIVERGES findings to the user before drafting responses.
Phase 2 — Draft Responses
Use .agents/agents/writer.md to draft a response per comment: (1) acknowledge, (2) state action taken, (3) point to manuscript location, (4) quote revised text if short. Also propose the manuscript edit. Do not modify files.
Phase 3 — Consistency Check
Use .agents/agents/consistency-auditor.md on all proposed edits in aggregate. Pay attention to required macros versus free-text variants, project-name consistency, baseline framing, and terminology rules in .agents/context/terminology.md.
Phase 4 — Assemble Letter
Produce the complete response letter: cover paragraph thanking reviewers, then per-reviewer / per-comment structure — original comment quoted, response, manuscript location.
Phase 5 — Hold for Approval
Present the full letter and all proposed manuscript edits labelled by comment ID. Do not modify any file until the user explicitly approves each edit.
Constraints
- Never modify files before explicit approval.
- Do not fabricate manuscript locations — flag uncertainty instead.
- Do not add citations not in the bibliography.
- Do not silently weaken limitations or strengthen claims to satisfy a reviewer.
- Preserve all canonical LaTeX macros (
<KEY_MACRO>,<KEY_MACRO>,<KEY_MACRO>,<KEY_MACRO>,<KEY_MACRO>,<KEY_MACRO>,<KEY_MACRO>,<KEY_MACRO>,<KEY_MACRO>, etc.) in proposed edits.
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.
- 1mo ago Changed · -120 lines · -24 tokens per session 7fabd5a7fe26
- 2mo ago First seen · 171 lines · 73 tokens per session scan A 503f7c655eca
response-to-reviewers is a skill published in the GitHub repository Rezenders/scientific_writing_agent (14 stars, last pushed 17d ago), licensed Apache-2.0. It adds 49 tokens to every session and 541 once invoked, about $0.0002 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 2026-08-30.
response-to-reviewers compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| response-to-reviewersRezenders/scientific_writing_agent | Skill | 14 | 17d ago | Apache-2.0 | A | 49 | Claude Code, Codex |
| instrument-data-to-allotropeanthropics/knowledge-work-plugins | Skill | 35k | 2d ago | Apache-2.0 | A | 123 | Claude Code |
| exploratory-data-analysisK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 83 | Claude Code |
| matlabK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 44 | Claude Code |
| phylogeneticsK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 71 | Claude Code, Codex |
| research-engineerdavila7/claude-code-templates | Skill | 33k | yesterday | MIT | A | 43 | Claude Code, Codex |
| mapping-to-snomedmaziyarpanahi/openmed | Skill | 5.8k | yesterday | Apache-2.0 | A | 205 | Claude Code |