synthesise-reviews

synthesise-reviews is a skill for Claude Code from flonat/flonat-research. It costs 54 tokens per session (2,184 once invoked), scanned A, original, MIT.

A workflow for combining several completed review reports into one ordered revision plan. It makes shared findings, disagreements, and task dependencies explicit.

In plain words
What is it for?
It helps plan revisions to a research paper after multiple reviewers have provided feedback.
Why use it?
Separate reviews can repeat the same points or suggest conflicting changes. This brings them together into one actionable sequence.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: mentions subagents; names the AskUserQuestion tool; $skill-name invocation.

Good fit It helps plan revisions to a research paper after multiple reviewers have provided feedback.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/flonat/flonat-research/synthesise-reviews
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 flonat/flonat-research --skill synthesise-reviews
Clone the repo
git clone --depth 1 https://github.com/flonat/flonat-research

Made for: Claude Code.

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 synthesise-reviews

README.md
[![agentmods](https://agentmods.dev/badge/skills/flonat/flonat-research/synthesise-reviews/github.svg)](https://agentmods.dev/skills/flonat/flonat-research/synthesise-reviews)
Your own site
<a href="https://agentmods.dev/skills/flonat/flonat-research/synthesise-reviews"><img src="https://agentmods.dev/badge/skills/flonat/flonat-research/synthesise-reviews/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 synthesise-reviews

Your own site · 80×15
<a href="https://agentmods.dev/skills/flonat/flonat-research/synthesise-reviews"><img src="https://agentmods.dev/badge/skills/flonat/flonat-research/synthesise-reviews.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 54 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,184 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 pass 7 Sept 2026
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 $0.00054 $0.02184
Opus 5 $0.00027 $0.01092
Sonnet 5 $0.00011 $0.00437
Haiku 4.5 $0.00005 $0.00218

Measured 9d ago against content hash 85bf82f6d7e0, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

synthesise-reviews 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 9d 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.

skills/synthesise-reviews/SKILL.md · 183 lines

How it starts

The opening of the file, as written. The whole thing — 183 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Synthesise Reviews

Combine multiple review reports into a single prioritised revision plan with cross-reviewer consensus ranking.

Output Path

Per rules/review-artefact-routing.md (auto-loads in research projects (path-scoped to paper-*/ and paper/)):

  • Source slug: synthesise-reviews
  • Write reports to: reviews/<scope>/synthesise-reviews/YYYY-MM-DD-HHMM.md inside the project, where <scope> is the paper slug (e.g. paper-jtp) or _project for project-level synthesis. Path is relative to the research project root, not the Task-Management repo.
  • Never at project root (./CRITIC-REPORT.md-style filenames are forbidden — pre-rule layout).
  • Idempotency: if today's file exists, append a same-day descriptor ({date}-revision.md, {date}-r2.md, {date}-pre-submission.md) — never overwrite.
  • Index update: if reviews/INDEX.md exists, write a one-line entry under "Latest per source" pointing at the new file. Otherwise review-recap will rebuild the index next time it runs.
  • Infrastructure repos (Task-Management, atlas-workspace, etc.): this section does not apply — the path-scoped rule won't load there.

Purpose

After running parallel review agents (paper-critic, domain-reviewer, referee2-reviewer), this skill reads their internal reports, cross-references issues, and produces a unified synthesis grouped into workstreams by priority and theme. It decides the consolidated issue set; strategic-revision --internal is the separate step that turns a complex issue set into an executable DAG.

Inspired by APE Papers' reviewer_response_plan_1.md pattern — workstreams grouped by priority, each concern traced to its reviewer.

When to Use

  • After running 2+ review agents on a paper
  • After a council review round
  • When preparing a revision plan from multiple feedback sources
  • Before an optional strategic-revision --internal handoff when the consolidated issues are interdependent

When NOT to Use

  • Before reviews exist — run the review agents first
  • To run reviews — use the individual agents (paper-critic, domain-reviewer, referee2-reviewer)
  • For a single review — just read the report directly
  • For genuine venue referee reports or an R&R response — use strategic-revision --external

Read the full file on GitHub · 183 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. 9d ago First seen · 183 lines · 54 tokens per session scan A 85bf82f6d7e0

Subscribe to this mod's changes

synthesise-reviews is a skill published in the GitHub repository flonat/flonat-research (133 stars, last pushed 17d ago), licensed MIT. It adds 54 tokens to every session and 2,184 once invoked, about $0.0003 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 2026-09-03.

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