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 | $0.05041 |
| Opus 5.5 | $0.00026 | $0.02016 |
| Sonnet 5.5 | $0.00013 | $0.01008 |
| Haiku 4.5 | $0.00007 | $0.00504 |
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 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 — 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.
- 1mo ago Changed 48a0b50d72da
- 2mo 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 (149 stars, last pushed 3d ago), licensed MIT. It adds 66 tokens to every session and 5,041 once invoked, about $0.0003 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 2026-09-03.
strategic-revision compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| strategic-revisionflonat/flonat-research | Skill | 149 | 3d ago | MIT | A | 66 | Claude Code |
| morning-briefchrisblattman/claudeblattman | Skill | 476 | yesterday | MIT | A | 20 | Claude Code |
| triage-inboxchrisblattman/claudeblattman | Skill | 476 | yesterday | MIT | A | 19 | Claude Code |
| checkinchrisblattman/claudeblattman | Skill | 476 | yesterday | MIT | A | 26 | Claude Code |
| pre-meeting-briefchrisblattman/claudeblattman | Skill | 476 | yesterday | MIT | A | 23 | Claude Code |
| weekly-reviewchrisblattman/claudeblattman | Skill | 476 | yesterday | MIT | A | 24 | Claude Code |
| proposal-writechrisblattman/claudeblattman | Skill | 476 | yesterday | MIT | A | 20 | Claude Code |