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 strategic-revisiongit 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/strategic-revision)<a href="https://agentmods.dev/skills/flonat/flonat-research/strategic-revision"><img src="https://agentmods.dev/badge/skills/flonat/flonat-research/strategic-revision/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/strategic-revision"><img src="https://agentmods.dev/badge/skills/flonat/flonat-research/strategic-revision.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Memory Poisoning · line 92 Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.Fix: Protect agent memory and state from modification by untrusted content. Use read-only memory for critical instructions and validate all state changes.
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 | $0.00066 | $0.05051 |
| Opus 5 | $0.00033 | $0.02525 |
| Sonnet 5 | $0.00013 | $0.01010 |
| Haiku 4.5 | $0.00007 | $0.00505 |
Grade A, and why
strategic-revision 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 8d 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 — 288 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Strategic Revision — Feedback to DAG-Validated Master Plan
Convert feedback on the author's own paper into a computationally validated revision plan. The shared analytical core produces atomic tasks, a dependency DAG, execution blocks A--E, a critical path, and bottleneck analysis. Provenance determines which of two modes supplies and stores the surrounding artifacts:
- External R&R mode preserves genuine venue correspondence and prepares response-oriented tracking.
- Internal revision mode consumes AI reviews, review syntheses, or informal collaborator feedback without representing them as venue correspondence.
Provenance: DAG validation + critical-path architecture adapted from Jukka Sihvonen's strategic-revision skill (https://github.com/jusi-aalto/strategic-revision). the user's ingestion layer (correspondence scaffolding, LaTeX verbatim, R&R routing, venue strategy, coaching) retained.
When to Use
- Received reviewer or editor reports from a journal or conference
- Need to turn internal pre-submission reviews into an executable revision sequence
- Have a
review-clusterorsynthesise-reviewsoutput that needs dependency mapping and critical-path validation - Need to extend an existing revision DAG with another review of the same draft
When NOT to Use
- Writing the actual response letter (use generated response blocks as a starting point, then write manually)
- Reviewing someone else's paper (use
proofreadorpeer-revieweragent) - Combining overlapping internal reports without execution planning — use
synthesise-reviewsfirst - Applying fixes directly without first producing and approving a plan
Modes and Provenance Gate
Select the mode from who authored the source feedback, not from whether it sounds like a referee report. Full routing and cross-mode rules: references/modes.md.
| Mode | Source provenance | Source location | Plan location | External-only artifacts |
|---|---|---|---|---|
external |
Human reviewer/editor acting for a venue | correspondence/referee-reviews/ and/or correspondence/editorial/ |
correspondence/referee-reviews/{venue}-round{n}/ |
Preserved source, verbatim transcription, rebuttal scaffold, venue strategy, reviews-in history event |
internal |
AI review/skill/agent, manual external-AI output, or informal supervisor/co-author feedback | AI: reviews/<scope>/<source>/; human collaborator: correspondence/internal/ |
reviews/<scope>/strategic-revision/{YYYY-MM-DD-HHMM}/ |
None |
What ships with it
10 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/dag-validation.md 7.4 KB
- references/fold-in-protocol.md 13 KB
- references/modes.md 5.1 KB
- references/phases.md 23 KB
- references/rr-routing.md 5.3 KB
- references/task-schema.md 5.6 KB
- scripts/dag_validator.py 26 KB runs code
- templates/referee-comments/comment-tracker.md 3.8 KB
- templates/referee-comments/review-analysis.md 2.2 KB
- templates/referee-comments/reviewer-comments-verbatim.tex 3.7 KB
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.
- 8d ago First seen · 288 lines · 66 tokens per session scan A 0a551374c8ba
strategic-revision is a skill published in the GitHub repository flonat/flonat-research (133 stars, last pushed 16d ago), licensed MIT. It adds 66 tokens to every session and 5,051 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.
Other skills, from other repositories
fin-paper-convert
Compile LaTeX to PDF and convert to target journal format.
fin-paper-plan
Generate structured paper outline adapted to target journal.
doc-audit
Evaluate one project document in detail — a workbook section, big picture, strategy map, handoff message, paper, brief, or primer — without editing it. Runs the mechanical lint, builds a claim ledger, types every headline against the eight claim statuses in CLAUDE.md, traces cited evidence to the scripts and logs that…
latex-compile
Compile a LaTeX document and fix every error plus aesthetic issue (overfull/underfull boxes, widows, alignment, fonts) for a clean PDF and log. Use this instead of running pdflatex/latexmk manually — it avoids the latexmk stale-log trap and silent grep failures on binary log output, and it reformats rather than…
claim-audit
Audit what a passing script actually established, before writing any prose about it — build the computed-object ledger, rewrite every check's label as the weakest statement that makes its body pass, and separate the verdict on someone else's work from your own new claim. Run after the script passes and BEFORE the…
nb-to-wolfbook
Convert Mathematica .nb or .m files to Wolfbook .wb format so they open and run in VS Code. Use when bringing existing .nb/.m files into Wolfbook, or to make an existing .wb bridge-safe.