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CC academic-paper

12-agent academic paper writing pipeline. 11 modes (full/plan/outline/revision/revision-coach/abstract/lit-review/format-convert/citation-check/disclosure/rebuttal-audit). 6 paper types, 5 citation formats, bilingual abstracts, LaTeX/DOCX-via-Pandoc/PDF output. Style Calibration + Writing Quality Check + Anti-Patterns with IRON RULE markers. Triggers: write paper, academic paper, guide my paper, parse reviews, I got reviewer comments, revision roadmap, should we push back, conference rebuttal, grant panel response, audit my rebuttal, check my response draft, AI disclosure, check citations, citation check, check my references, verify references, look over the refs, 寫論文, 學術論文, 引導我寫論文, 審查意見, 我收到審查意見, 修訂路線圖, 評估回覆, 檢查引用, 引用檢查, 檢查參考文獻, 核對文獻, 논문 작성, 초록 작성, 논문 수정, 논문 계획을 도와줘, 심사 의견을 받았어, 심사 의견 반영, 답변서 점검, AI 사용 고지, 인용 확인, 인용 형식 검사, enmendar mi artículo, redactar artículo, guía mi artículo, analizar reseñas, auditar mi respuesta, verificar borrador de respuesta, verificar citas, divulgación de IA.

Imbad0202/academic-research-skills Agent Skills author: Imbad0202 NOASSERTION 62 files body ≈ 15 576 tokens Open the sourcegithub.com↗ analyzed 5 d ago

12-agent academic paper writing pipeline.

As a process C 56/100 · Has gaps — References files that are not bundled: ../shared/output_language_pair.md, scripts/check_pipeline_integrity.py, scripts/check_acronyms.py

ProcedureLaTeXWordResearchtype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
C
81/100
safety, quality, tests
Safety 60%
99
Quality 40%
54
Run on models
none yet
Process rating
C
56/100
Has gaps
References files that are not bundled: ../shared/output_language_pair.md, scripts/check_pipeline_integrity.py, scripts/check_acronyms.py
Tools and files w 18
0
Inputs and preconditions w 11
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
  3. The text references files that are not there: add them or drop the references.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 1

✓ No critical or high findings

Medium and low: 1
  • low Obfuscation obf-hex-escape-chain references/venue_disclosure_policies.md:128
    Escaped/char-code string obfuscation (documentation of a security skill)
    | Source URL | Official submission guide: https://gjyk.ijournals.cn/gjykcn/site/menu/20120104163914001 ; GenAI notice: https://gjyk.ijournals.cn/uploadfile/gjykcn/20260423/%E7%94%9F%E6%88%90%E5%BC%8F%
    security skill

Files scanned: 62. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning body-long SKILL.md body ≈ 15576 tokens (recommended < 5000); move details to references/
  • warning missing-ref reference to a missing file: ../shared/output_language_pair.md
  • warning missing-ref reference to a missing file: scripts/check_pipeline_integrity.py
  • warning missing-ref reference to a missing file: scripts/check_acronyms.py
  • warning missing-ref reference to a missing file: scripts/check_review_form_note_sync.py
  • warning missing-ref reference to a missing file: scripts/ars_anchorize_draft.py
  • warning missing-ref reference to a missing file: scripts/ars_apply_revision_patch.py
  • warning missing-ref reference to a missing file: scripts/claim_strength_drift_disposition.py
  • note edit-residue the text marks something as outdated (lines 473, 608): check that old rules are not kept next to new ones — the full check reads the text for contradictions

Process rating: all ten parameters 56/100

Will not run. References files that are not bundled: ../shared/output_language_pair.md, scripts/check_pipeline_integrity.py, scripts/check_acronyms.py
  • 0Tools and files. 7 referenced file(s) missing: ../shared/output_language_pair.md, scripts/check_pipeline_integrity.py, scripts/check_acronyms.py
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 20When it triggers. No condition that starts the skill
  • 40Execution cost. Instruction body is 15576 tokens: crowds the task out of the window
  • 85Steps. 112 steps, 2 vague phrases
  • 100Result and completion. Output format and completion criterion are stated
  • 100Failures and branches. 3 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 26 top-level sections: this looks like several domains in one skill

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Description length 1003: 120–800 characters recommended
  • +1No license
  • +2Single-language instructions
  • +4Structure: 52 headings
  • +3Step-by-step instructions: 112 items
  • +3Output format is stated explicitly
  • +4Has examples (4 code blocks)
  • +4Reference files are cited in the instructions (12 of 29)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 54.