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 flonat/flonat-research --skill synthesise-reviewsgit clone --depth 1 https://github.com/flonat/flonat-researchWrote 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/flonat/flonat-research/synthesise-reviews)<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.
<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>- NVIDIA SkillSpector pass
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 | $0.00054 | $0.02184 |
| Opus 5 | $0.00027 | $0.01092 |
| Sonnet 5 | $0.00011 | $0.00437 |
| Haiku 4.5 | $0.00005 | $0.00218 |
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.
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.mdinside the project, where<scope>is the paper slug (e.g.paper-jtp) or_projectfor 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.mdexists, write a one-line entry under "Latest per source" pointing at the new file. Otherwisereview-recapwill 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 --internalhandoff 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
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.
- 9d ago First seen · 183 lines · 54 tokens per session scan A 85bf82f6d7e0
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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