Agent skill

Strategic Revision

by flonat in flonat/flonat-research

Turn external referee correspondence or internal pre-submission feedback into a provenance-safe, DAG-validated revision master plan with atomic tasks, dependencies, critical path, and execution…

MITAuto-check passedDocuments & Office

Install Strategic Revision

skills CLI
$ npx skills add flonat/flonat-research --skill strategic-revision -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install flonat/flonat-research strategic-revision --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/flonat/flonat-research.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/strategic-revision .claude/skills/strategic-revision && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
strategic-revision
GitHub stars
146
Token cost
~5.5k tokens
SKILL.md length
2,347 words
Files
11 (incl. scripts, references)
Skills in repo
83
Repo updated
First seen
Licence
MIT

At a glance

Turn external referee correspondence or internal pre-submission feedback into a provenance-safe, DAG-validated revision master plan with atomic tasks, dependencies, critical path, and execution…

  • Works in 2 steps: External R&R mode preserves genuine… → Internal revision mode consumes AI…
  • Feedback exists but must be converted into an approved implementation sequence
  • SKILL.md covers When to Use, When NOT to Use, Modes and Provenance Gate and Inputs, plus 10 more sections
  • Runs Python scripts from its folder; calls uv; needs SCOPUS_API_KEY

What it does

Strategic Revision is an agent skill from flonat/flonat-research. Turn external referee correspondence or internal pre-submission feedback into a provenance-safe, DAG-validated revision master plan with atomic tasks, dependencies, critical path, and execution blocks. Use when feedback exists but must be converted into an approved implementation sequence. Not for drafting the reviewer response itself; use $review-response.

Its SKILL.md is about 5.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 14 other files, including scripts and reference files (for example `references/dag-validation.md`, `references/fold-in-protocol.md` and `references/modes.md`).

It sits in Documents & Office. The repository describes itself as: Shareable Claude Code + Codex infrastructure for PhD researchers — skills, agents, hooks, and rules for academic workflows. The licence is MIT.

When your agent uses it

  • Feedback exists but must be converted into an approved implementation sequence

Example prompts

  • “/strategic-revision”

Requirements

  • Python 3

Workflow steps

2 steps, taken from the first numbered list in SKILL.md.

  1. External R&R mode preserves genuine venue correspondence and prepares response-oriented tracking.
  2. Internal revision mode consumes AI reviews, review syntheses, or informal collaborator feedback without representing them as venue…

What it can do on your machine

Read from SKILL.md and the folder at commit da27600. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • uv

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • github.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • SCOPUS_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Strategic Revision loads about 5.5k tokens when it runs, and up to ~21k if it reads all its reference files. Until then it costs about 95 tokens; SKILL.md has 2,347 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~95
When it runs · the whole SKILL.md, loaded when a task matches
~5.5k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~21k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from flonat/flonat-research at commit da27600, republished under its MIT licence (© flonat). 2,347 words, ~5,489 tokens.

Download SKILL.mdSave it as .claude/skills/strategic-revision/SKILL.md (or your agent's skills folder). This skill also uses 10 other files; get the full folder from GitHub.
name
strategic-revision
description
Turn external referee correspondence or internal pre-submission feedback into a provenance-safe, DAG-validated revision master plan with atomic tasks, dependencies, critical path, and execution blocks. Use when feedback exists but must be converted into an approved implementation sequence. Not for drafting the reviewer response itself; use $review-response.
argument-hint
[--external <reviews-pdf-or-folder> | --internal <review-or-synthesis-path>]
skill-dependencies
latex-diff, proofread, review-packet, synthesise-reviews

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:

  1. External R&R mode preserves genuine venue correspondence and prepares response-oriented tracking.
  2. 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-cluster or synthesise-reviews output 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 proofread or peer-reviewer agent)
  • Combining overlapping internal reports without execution planning — use synthesise-reviews first
  • 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.

ModeSource provenanceSource locationPlan locationExternal-only artifacts
externalHuman reviewer/editor acting for a venuecorrespondence/referee-reviews/ and/or correspondence/editorial/correspondence/referee-reviews/{venue}-round{n}/Preserved source, verbatim transcription, rebuttal scaffold, venue strategy, reviews-in history event
internalAI review/skill/agent, manual external-AI output, or informal supervisor/co-author feedbackAI: reviews/<scope>/<source>/; human collaborator: correspondence/internal/reviews/<scope>/strategic-revision/{YYYY-MM-DD-HHMM}/None

An auto-generated "Reviewer 2" report is internal mode. A human venue report pasted by the user is external mode. If provenance is unclear, ask before creating files. Never copy AI review text into the venue-correspondence record.

External mode — reviewer-gap extraction (end of run). After the plan is built, run skills/_shared/reviewer-gap-extraction.md on the venue referee reports: capture any generalizable reviewing pattern the human referees raised that our own review agents don't already cover, as reviewer-gap signals for the feedback-review skill to propose as agent-contract refinements (Phase 2 of the review-agent calibration loop). Internal mode does NOT emit — those sources are our own agents.

Inputs

Gather the shared inputs first:

  1. Feedback path(s) — one or more reports, or a synthesise-reviews output
  2. Project path — auto-detect from the current research project when possible
  3. Paper scope — e.g., paper-eacl; _project only for project-level feedback
  4. Coordinating author — who owns revision decisions

External mode also requires venue slug, revision round (default 1), response deadline, and the original venue-supplied review/decision files. Internal mode records the draft identity and review dates; it does not invent a venue round or response deadline.

External PDF Auto-Discovery

Search for the reviews PDF in this order. Use the first match; if multiple PDFs found at a location, list them and ask the user to pick.

  1. to-sort/*.pdf — most likely landing spot after download
  2. correspondence/referee-reviews/{venue}-round{n}/*.pdf
  3. correspondence/referee-reviews/*.pdf
  4. Ask the user for the path if nothing found

Output Locations

External R&R mode
correspondence/referee-reviews/{venue}-round{n}/
├── {venue}-round{n}-reviews.pdf      (copy of input PDF — source NEVER moved; taxonomy name per rules/submission-file-archive.md, 2026-07-03)
├── {venue}-round{n}-reviews.md       (searchable parsed transcription — required pair with PDF)
├── {venue}-round{n}-rebuttal.md      (empty — for response draft)
├── reviews/                          (individual reviewer files)
│   ├── reviewer-1.md
│   └── ...
├── analysis/
│   ├── comment-tracker.md            (atomic comment matrix with R&R + 5-cat classification)
│   ├── review-analysis.md            (strategic overview + venue strategy)
│   └── reviewer-comments-verbatim.tex (LaTeX transcription)
└── plan/
    ├── REVISION_MASTER_PLAN.md       (six-phase Sihvonen plan — atomic tasks, DAG, blocks, risks)
    ├── revision_tasks.json           (DAG input — see references/task-schema.md)
    └── revision_dag_analysis.json    (computational output — parallel batches, critical path, bottlenecks)

Source PDF preservation: The original PDF is only ever copied to {venue}-round{n}-reviews.pdf. Never move, rename, or delete the source.

Principle: correspondence/ holds human exchanges; AI-derived review and planning artifacts stay under reviews/. Informal human collaborator feedback may remain in correspondence/internal/, while the derived internal plan goes under reviews/{scope}/strategic-revision/.

Submission-history stamp: ingesting a reviews PDF is a submission event — append a history: row (event: reviews-in, round: r{n}, files: pointing at {venue}-round{n}-reviews.pdf) to the paper's vault submission entry per rules/submission-file-archive.md § history. When the revision is later resubmitted, that resubmission appends its own response-sent + submitted rows (usually via session-close sync).

No-overwrite rule: If outputs already exist, version them (comment-tracker-v2.md, REVISION_MASTER_PLAN-v2.md). Always flag before writing.

Internal revision mode
reviews/{scope}/strategic-revision/{YYYY-MM-DD-HHMM}/
├── source-manifest.md                 (source paths, provenance, dates, hashes)
├── analysis/
│   ├── comment-tracker.md             (atomic findings + verification anchors)
│   └── review-analysis.md             (cross-review picture + readiness decision)
├── plan/
│   ├── REVISION_MASTER_PLAN.md
│   ├── revision_tasks.json
│   └── revision_dag_analysis.json
├── bundle/                            (created at successful closeout)
│   ├── strategic-revision.bundle.zip
│   ├── strategic-revision.manifest.json
│   └── strategic-revision.sha256
└── snapshots/                         (token-conservation baselines — see below)
    └── {label}/                       (e.g. pre-revision/, post-block-C/: main.tex + sections/ + .bib)

Internal mode links to its source reports in place and does not duplicate them. It creates no rebuttal file, venue-response transcription, submission-history event, or alternative-venue analysis unless the user separately requests one. Use a new timestamped package for a materially changed draft; use fold-in versioning for another review of the same draft.

When the source feedback was produced from a sealed review packet, source-manifest.md records that packet's path, archive SHA-256, canonical artifact SHA-256, and verification receipt. At successful closeout, invoke review-packet in gate-closeout mode so the exact reviews, analysis, adjudication, DAG, snapshots, and completion receipt form one decision-ready packet. A closeout packet is provenance, not a review verdict, and receives no separate reviews/INDEX.md row.

Snapshot location (mandatory, 2026-08-02). Token-conservation baselines and any other source snapshots taken during a revision live in the plan package's snapshots/{label}/ — git-tracked, tied to the plan that uses them. NEVER store revision snapshots under paper-*/backup/: that directory is the Overleaf backup automation's mirror surface (rsync --delete), and on 2026-07-31/2026-08-02 an unprotected mirror pass in daily-maintenance.sh gutted dated snapshot dirs stored there (both the P108 round-1 and round-2 baselines, plus two AIES submission-era archives). The automation's filters have been hardened, but the ownership boundary stands: paper-*/backup/ belongs to the automation; revision provenance belongs to the plan package.

Protocol — 11 Mode-Aware Phases

The skill runs 11 sequential phases. Detailed step-by-step procedure: references/phases.md.

#PhaseOutputSource
1Provenance + SetupMode lock, scaffold, and source manifest/copyFB
2Read FeedbackStructured source data in memoryFB
3Source RecordsExternal reviewer files or internal source manifestFB
4Verbatim GateExternal transcription compiled; internal mode records a justified skipFB
5Atomic ParsingEvery distinct request as own SourceID (R1.a3, EiC.2b)JS
6Classification5-cat (STRUCTURAL/ARGUMENTATIVE/EMPIRICAL/CLARIFICATION/EDITORIAL) + R&R routing (NEW ANALYSIS / CLARIFICATION / DISAGREE / MINOR)Both
7Dependency Mapping (DAG)Upstream Blockers + Collateral Risks tables → revision_tasks.jsonJS
8Structural DAG Validationdag_validator.py --validate-only gate checkJS
9Critical Path SequencingExecution Blocks A-E + GO/NO-GO gate after Block AJS
10Risk & Conflict Resolution + DecisionsConflicts, process risks, and mode-appropriate author decisionsBoth
11Computational Optimization + StrategyParallel batches, critical path, bottlenecks + external venue strategy or internal readiness decisionBoth

JS = Sihvonen phases. FB = the user phases. Both = merged.

Phase Dependencies
  • Phases 1-4 (mode-aware ingestion) must complete before Phase 5 begins
  • Phase 8 is a hard gate: cycles → return to Phase 7, do not advance to Phase 9
  • Phase 11 requires the Phase 9 block assignments before running full DAG analysis
  • Phase 10 author decisions run only on Major/Critical findings (skip Minor/Editorial)

DAG Validation Quick Reference

Phase 8 (structural gate):

bash
cd correspondence/referee-reviews/{venue}-round{n}/plan
uv run --with networkx python dag_validator.py revision_tasks.json --validate-only

Phase 11 (full analysis):

bash
uv run --with networkx python dag_validator.py revision_tasks.json
# Produces revision_dag_analysis.json

Copy dag_validator.py from skills/strategic-revision/scripts/ into the plan directory before running. Invoke it through uv run --with networkx so the project environment is not mutated.

Full validator documentation: references/dag-validation.md. Task JSON schema: references/task-schema.md. R&R routing rules: references/rr-routing.md.

Classification: Two Axes

Every atomic task gets both classifications recorded in the comment tracker:

Category (5-cat, Sihvonen): structural intent of the change

  • 🔴 STRUCTURAL — moves, cuts, reorganizations
  • 🟠 ARGUMENTATIVE — theory, narrative, logical framing
  • 🟡 EMPIRICAL — new regressions, data work, robustness
  • 🟢 CLARIFICATION — definitions, justifications, explanations
  • 🔵 EDITORIAL — formatting, typos, figures, terminology

R&R Routing (FB): workflow routing

  • NEW ANALYSIS — needs code/data work
  • CLARIFICATION — textual fix only
  • DISAGREE — author adjudication; external mode may produce push-back in the later response, internal mode records Reject/Adapt/Defer
  • MINOR — trivial or editorial

The two axes are orthogonal: a STRUCTURAL task is usually a CLARIFICATION routing; an EMPIRICAL task is usually NEW ANALYSIS; ARGUMENTATIVE with DISAGREE signals a high-stakes author decision and, in external mode only, a potentially rebuttal-heavy revision.

Strategic Coaching (Phase 10)

External mode: for each Major or Critical referee comment, walk the user through:

  1. Understanding: reviewer's core concern — methodology, theory, or framing?
  2. Position: Agree / Partially agree / Disagree (with core rebuttal if Disagree)
  3. Risk: likelihood of reviewer escalation if pushed back on
  4. Response sketch: one-sentence strategy (not full response)

Record in the comment tracker with two new columns: Position + Strategy.

Rules:

  • Only Major/Critical get coaching. Minor/Editorial auto-classify as Agree.
  • the user can say "skip coaching" to auto-classify all remaining as Agree.
  • Maximum 2 rounds of dialogue per comment.
  • Do not write the actual response letter — that remains the user's job.

Internal mode uses the same risk analysis but records Decision (Adopt / Adapt / Reject / Defer) and Rationale rather than simulating agreement with a referee. Internal findings never become response-letter claims.

Strategy (Phase 11)

Show full SKILL.md (986 more words)Show less
External R&R mode

Populate the Publication Strategy section of review-analysis.md:

  • Strategy A (minimal revision): venues that would accept the paper as-is despite reviewer concerns
  • Strategy B (substantial revision): equal-or-higher prestige venues worth targeting if authors invest in addressing major concerns
  • Conferences: check CORE rankings via .context/resources/venue-rankings.md (CSV: .context/resources/venue-rankings/core_2026.csv)
  • Journals: check CABS AJG via .context/resources/venue-rankings.md (CSV: .context/resources/venue-rankings/abs_ajg_2024.csv). For SJR, query Elsevier Serial Title API (SCOPUS_API_KEY required). Flag journals below CABS 3 only with strong rationale.
  • Recommendation table: 3-5 venues ranked with rationale. First option should always be "revise for current venue" if acceptance probability >~30%.
  • Key Decision: frame the core trade-off (speed vs. impact, minimal vs. substantial effort).
Internal revision mode

Populate a readiness decision instead of a venue-switching exercise:

  • Decision: SUBMIT, HOLD, or DEFER
  • Blocking tasks: the incomplete critical-path tasks that determine the decision
  • Scope boundary: findings explicitly deferred beyond this submission
  • Re-review gate: the checks that must pass after the DAG is executed

Do not recommend alternative venues in internal mode unless the user asks for venue strategy.

Critical Rules

  1. External verbatim means verbatim. In external mode, never paraphrase reviewer text in reviewer-comments-verbatim.tex. Copy exactly. Internal mode does not create that artifact.
  2. Every comment gets a SourceID. No reviewer concern is lost. If in doubt, give it its own ID.
  3. Atomic tasks only. If a paragraph contains 3 distinct requests, create 3 rows — never collapse.
  4. No invented requests. Every task traces to a verbatim quote.
  5. Don't overwrite. Version outputs if present.
  6. Compile the external LaTeX. In external mode, reviewer-comments-verbatim.tex must build cleanly before Phase 5. Internal mode records the provenance-based skip.
  7. Phase 8 is a hard gate. Cycles block Phase 9. Fix the DAG, re-run validation.
  8. Phase 11 is mandatory. Do not skip computational optimization — parallel batches and critical path override manual sequencing where they conflict.
  9. Don't write response letters. The skill produces plans and trackers; writing the rebuttal is the user's job. To inventory what actually changed between the submitted and revised manuscript as raw material for the rebuttal, run latex-diff (submitted revision vs working tree) — it reports the change list; it does not write the letter.
  10. GO/NO-GO gate after Block A. If empirical foundation changes key conclusions, escalate to authors before advancing.
  11. Provenance controls routing. AI-simulated referee reports remain internal; genuine venue correspondence remains external.
  12. External-only side effects stay external. Internal mode never creates a rebuttal, venue-history event, or referee-verbatim artifact.
  13. Do not cross-fold modes. A new source with different provenance gets its own package; cross-reference completed work without moving or relabelling source material.
  14. No silent claim-strength moves. Revision edits touching claim-bearing text follow the claim-strength ladder (docs/reference/claim-strength-ladder.md): any move up or down the epistemic scale — including deleting a hedge, scope caveat, or null result — must be authorized by a task's SourceID, and the executing session states the move in its summary. After a revision block completes, run the token-conservation check on each edited file (uv run python .scripts/check_token_conservation.py --source <pre-revision> --revision <current>) and reconcile every advisory row (changed number, dropped/added citation) against the tasks that authorized it.
  15. Close out with a Fulfillment Ledger. When execution completes (final block done, or fold-in close), fill the tracker's Fulfillment Ledger: every reviewer commitment gets fulfilled / partial / not_fulfilled / acknowledgment_only, verified against the manuscript — read/grep the .tex for the promised change; never mark fulfilled on the response letter's or the tracker's say-so (exception: acknowledgment_only, whose evidence is the letter). partial and not_fulfilled require an explicit rationale + residual action. (Ported from ARS v3.19 commitment ledger.)
  16. Prepare successful closeout. After the fulfillment ledger and final DAG validation pass, generate and verify the strategic review packet. Never overwrite an earlier packet or include genuine venue correspondence in an internal packet without explicit authorization.

Fold-in mode — second-review extension of an existing DAG

When a paper that already has a strategic-revision package receives a second same-mode review of the same draft, extend the existing DAG instead of starting over.

Trigger: "fold in this review", "process this second review", strategic-revision --fold-in <path>, or recognising the situation from context (existing plan/revision_tasks*.json + new same-mode review file). Cross-mode feedback starts or extends its own provenance package and may only be cross-referenced.

What it produces (preserving v1; never overwriting):

  • plan/revision_tasks-v{N}.json + plan/revision_dag_analysis-v{N}.json — incremented version
  • v{N} addendum appended to plan/REVISION_MASTER_PLAN.md and analysis/comment-tracker.md (the same files, not new ones)
  • A cross-review picture: where the reviews overlap, what each catches that the other misses, and whether either is a superset (usually neither is — the union is the real picture)
  • New review gets its own reviews/<source-N>.md and its own row in reviews/INDEX.md; v1's row is untouched

Why this matters: re-running the full skill loses the v1 verification trail (which findings were checked, which converted to DISAGREE, which got Coaching positions). Fold-in preserves that trail. The DAG itself is the durable artifact; reviews are inputs that grow it.

Full step-by-step (including the OVERLAP / MERGE / NEW disposition axis, versioning convention, and the cross-review picture template): references/fold-in-protocol.md.

Templates

Located in templates/referee-comments/:

  • comment-tracker.md — atomic matrix with R&R columns
  • review-analysis.md — strategic overview template
  • reviewer-comments-verbatim.tex — LaTeX transcription template

Cross-References

  • latex-diff — diff the submitted vs revised manuscript (git revision or backup vs working tree) to build the "summary of changes" and confirm every committed revision has a rebuttal line. Read-only; raw material for the letter, not the letter itself.
  • proofread — proofread the response letter before submission
  • bib-validate — run after revision to check bibliography
  • pre-submission-report — full quality check before resubmission
  • paper-critic agent — self-review of the revised paper
  • synthesise-reviews — merge internal review agent reports (different use case — not referee comments)
  • review-packet — records the reviewed manuscript identity and prepares a successfully completed strategic-revision run for closeout review
  • references/phases.md — detailed 11-phase protocol
  • references/rr-routing.md — R&R routing signal words
  • references/dag-validation.md — DAG validator usage + Phase 6 details
  • references/task-schema.md — revision_tasks.json JSON schema
  • references/modes.md — mode selection, routing, side-effect, and cross-mode provenance contract
  • references/fold-in-protocol.md — second-review extension protocol (preserves v1 trail; appends v{N} addendums)
  • scripts/dag_validator.py — NetworkX-based DAG validator (copy to plan dir before running)

© flonat, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 10 other files (scripts, references) in skills/strategic-revision of flonat/flonat-research.

  • SKILL.md
  • references/dag-validation.md
  • references/fold-in-protocol.md
  • references/modes.md
  • references/phases.md
  • references/rr-routing.md
  • references/task-schema.md
  • scripts/dag_validator.py
  • templates/referee-comments/comment-tracker.md
  • templates/referee-comments/review-analysis.md
  • templates/referee-comments/reviewer-comments-verbatim.tex

Open the folder on GitHubat commit da27600

Compare with similar skills

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    flonat/flonat-research

    Read, create, combine, split, rotate, OCR, watermark, secure, or extract content from PDF files.

    146 GitHub stars~488 tokensUpdated 11 days ago
    Auto-check passed
  • Init Project Orchestration

    flonat/flonat-research

    Create or migrate project-level agents, repeatable project workflows, and planning state from one client-neutral contract, then render repository-scoped adapters for both Claude Code and Codex.

    146 GitHub stars~1.6k tokensUpdated 11 days ago
    Auto-check passed
  • Pre Commit Audit

    flonat/flonat-research

    Deliver a fast pre-commit safety scan: file size, anonymity (author / affiliation strings in tex/bib), hardcoded secrets, and invisible-Unicode carriers.

    146 GitHub stars~2.8k tokensUpdated 11 days ago
    Auto-check: notes

Questions about Strategic Revision

What does Strategic Revision do?

Turn external referee correspondence or internal pre-submission feedback into a provenance-safe, DAG-validated revision master plan with atomic tasks, dependencies, critical path, and execution…. Strategic Revision is an agent skill from flonat/flonat-research. Turn external referee correspondence or internal pre-submission feedback into a provenance-safe, DAG-validated revision master plan with atomic tasks, dependencies, critical path, and execution blocks.

When should I use Strategic Revision?

Strategic Revision fits situations like: feedback exists but must be converted into an approved implementation sequence.

How do I install Strategic Revision in Claude Code?

Run `npx skills add flonat/flonat-research --skill strategic-revision -a claude-code`. Or copy the skill folder (skills/strategic-revision in flonat/flonat-research) into .claude/skills/strategic-revision in your project. Claude Code loads it when a task matches its description.

How do I install Strategic Revision in Codex?

Run `npx skills add flonat/flonat-research --skill strategic-revision -a codex`. Or copy the skill folder (skills/strategic-revision in flonat/flonat-research) into .agents/skills/strategic-revision in your project. Codex loads it when a task matches its description.

Can I use Strategic Revision in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add flonat/flonat-research --skill strategic-revision -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/strategic-revision, .gemini/skills/strategic-revision, .github/skills/strategic-revision and .opencode/skills/strategic-revision in your project.

What does Strategic Revision need to run?

Going by SKILL.md and its folder, Strategic Revision needs Python for the scripts in its folder, the command-line tools its instructions call (uv) and credentials named SCOPUS_API_KEY. Our summary lists: Python 3.

Does Strategic Revision access the network?

SKILL.md names 1 domain. As links in the text: github.com. This is read from the text; nothing was executed.

Is Strategic Revision safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Strategic Revision use?

Strategic Revision is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Strategic Revision use?

About 5.5k tokens (SKILL.md is roughly 22k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 15k tokens, read only when the agent opens those files.

What are the alternatives to Strategic Revision?

Skills that share tags, products or a category with Strategic Revision: Markdown Article Formatter (JimLiu/baoyu-skills, 27k stars), Markitdown (ImCa0/just-laws, 781 stars), Obsidian Markdown (Atmosphere/atmosphere, 3.8k stars) and DOCX (rvdbreemen/OTGW-firmware, 207 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Strategic Revision?

flonat (a GitHub user) maintains it in flonat/flonat-research, which has 146 GitHub stars. The repository holds 83 skills in this directory. The repository was last updated on September 29, 2026.

Source: flonat/flonat-research on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.