agentleFS
kuker24kuker24/OpenCodeBestFriend/skills/academic/SKILL.md

academic

Skill0 starsChanged 2 months agoMITView on GitHub

Academic research, literature synthesis, thesis/paper drafting, and peer review. Use for scholarly literature surveys, claim-to-source tracing, academic manuscripts (IMRaD/proposals), and rigorous peer critique. Not for library/API docs (research), one-off document OCR/render (smartdoc), or local book ingest (smartbook-ingest).

3 other projects use a version of this.

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The file

We split this skill into the rules it gives an agent

5 rules. About 0 are common, 3 are distinctive, and 2 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈3

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 2

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Always observe academic integrity standards in references/integrity.md.

    line 23
  2. Distinctive:

    One mode per session turn: Do not execute all four phases simultaneously.

    line 41
  3. Very specific:

    Missing toolchains are NOT_CONFIGURED: If compilation tools (Pandoc, LaTeX, typst) or reference managers (Zotero) are absent from the host …

    line 42
  4. Distinctive:

    No detector evasion or fake similarity scores: Never promise 0% similarity or Turnitin evasion.

    line 43
  5. Very specific:

    Primary source verification: Every cited factual claim must be backed by a verifiable paper title, DOI, author, or canonical URL.

    line 44

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Academic

One primary specialist for scholarly research workflows: literature surveying, manuscript drafting, structured peer review, and revision ledgers.

Load One Mode

Select exactly one mode reference based on the user's current goal. Do not load all mode files at once:

ModePurposeReference
researchLiterature discovery, claim-source matrix, DOI/URL verificationreferences/research.md
writeScholarly drafting (IMRaD, thesis, proposal, methodology)references/write.md
reviewCriterion-bound critique, methodology evaluation, anti-sycophancyreferences/review.md
reviseChange ledger, rebuttal response matrix, critique addressingreferences/revise.md

Always observe academic integrity standards in references/integrity.md.

Boundaries

TaskRoute
Official software library, framework specs, or first-party API factsresearch + Context7
Why this codebase made an architectural choice/why (manual)
Document ingestion, OCR, table extraction, or PDF renderingsmartdoc
Compiling reusable local textbook/module knowledgesmartbook-ingest
Product feature interview and ADR writinggrill-with-docs
User-facing prose AI-tell cleanuphumanizer / /unslop
Scholarly literature, thesis, manuscript, and peer critiqueacademic

smartdoc may render the final locked manuscript to PDF/DOCX. academic owns the academic claims, citations, and scholarly structure; smartdoc owns rendering and document formats.

Core Rules

  1. One mode per session turn: Do not execute all four phases simultaneously. Complete the current phase and request confirmation before advancing.
  2. Missing toolchains are NOT_CONFIGURED: If compilation tools (Pandoc, LaTeX, typst) or reference managers (Zotero) are absent from the host environment, report NOT_CONFIGURED. Never fake build or export success.
  3. No detector evasion or fake similarity scores: Never promise 0% similarity or Turnitin evasion. Do not participate in deceptive rewriting loops.
  4. Primary source verification: Every cited factual claim must be backed by a verifiable paper title, DOI, author, or canonical URL. Flag unverified claims explicitly as [UNVERIFIED].
  5. Anti-sycophancy in review: Academic reviews must stress-test assumptions, assess sample size/method limitations, and identify overclaiming. Honest critique takes priority over compliments.

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