Agent skill

_template_reference_first

Internal template for creating or refactoring a skill into the repository's reference-first shape. **Trigger**: reference-first template, blueprint skill, create a reusable skill, refactor a script-heavy skill. **Use when**: you need a lean `SKILL.md`, explicit `references/`, machine-readable `assets/`, and a minimal deterministic `run.py`. **Skip if**: the task is a one-off workflow that will not be reused as a skill. **Network**: none. **Guardrail**: keep domain knowledge and writing exemplars out of `run.py`; make reference loading explicit; do not ship reader-facing placeholder text.

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Install this agent skill to your Project

npx add-skill https://github.com/WILLOSCAR/research-units-pipeline-skills/tree/main/.codex/skills/_template_reference_first

SKILL.md

Reference-First Skill Template

Why this exists

This package is the default starting point for new or refactored skills in this repo.

It demonstrates the intended split of responsibilities:

  • SKILL.md routes the workflow
  • references/ holds method, judgment, and exemplars
  • assets/ holds machine-readable contracts
  • scripts/ handles deterministic execution only

Use it as a shape to copy and customize, not as a domain-specific skill.

Inputs

  • the job the skill should encode
  • the expected inputs and outputs for that job
  • acceptance criteria and failure conditions
  • any domain packs, schemas, or existing artifacts that must be reused

Outputs

  • a lean SKILL.md
  • references/overview.md
  • references/examples_good.md
  • references/examples_bad.md
  • assets/schema.json
  • scripts/run.py

Workflow

  1. Define the job and its boundary
  • write down the trigger, intended outcome, and explicit non-goals
  • separate reusable behavior from one-off project context
  1. Write SKILL.md as a router
  • keep only the activation rule, inputs, outputs, workflow, block conditions, and resource routing
  • do not copy large judgment rules, domain essays, or sentence banks into this file
  1. Move reusable thinking into references/
  • put domain knowledge, decision rubrics, and method notes in references/overview.md
  • if the skill can emit reader-facing text, include both references/examples_good.md and references/examples_bad.md
  • keep reference files one hop away from SKILL.md; avoid deep reference chains
  1. Put contracts into assets/
  • store machine-readable schemas, templates, or static resource packs in assets/
  • use assets/schema.json for the structured artifact that the skill validates or emits
  1. Keep scripts/run.py deterministic
  • allow file discovery, normalization, validation, manifest generation, and external tool calls
  • keep prose templates, domain defaults, and reader-facing judgment out of Python
  1. Validate hygiene before reuse
  • make sure the package has no unresolved placeholders in reader-facing examples
  • make sure SKILL.md explicitly tells the agent when to read each reference file

When to read references/

  • Always read references/overview.md before customizing or applying this template.
  • Read references/examples_good.md when the skill writes reader-facing text or shapes another writer skill.
  • Read references/examples_bad.md when cleaning up generator voice, pipeline jargon, or weak deliverable framing.
  • If the skill has domain variants, add explicit domain-pack references and mention the selection rule here.

Assets to reuse

  • assets/schema.json: a generic contract for a reference-first skill manifest; adapt it to the concrete skill you are building.

Script role

  • scripts/run.py is a minimal validator and manifest builder.
  • Read or patch the script only when you need deterministic behavior.
  • Do not rely on the script to supply the skill's method, voice, domain taxonomy, or reader-facing examples.

Block conditions

Stop and fix the package before reuse if any of these are true:

  • SKILL.md duplicates long reference content instead of routing to references/
  • run.py contains domain defaults, sentence libraries, or filler prose
  • reader-facing examples contain unresolved placeholders or internal pipeline jargon
  • the schema does not match the artifact the skill is supposed to validate or emit

Done checklist

  • SKILL.md stays lean and references other files explicitly
  • references/ contains the actual method and exemplars
  • assets/ contains machine-readable contracts only
  • scripts/run.py stays deterministic and small
  • the package can be understood by reading SKILL.md and the referenced files without reading all Python first

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WILLOSCAR/research-units-pipeline-skills

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对中文毕业论文进行编译、warning 分级、模板模式检查、数据与引用复查,并把问题回写成可继续迭代的 review checklist。 **Trigger**: 毕业论文编译检查, thesis compile review, warning 分级, 终稿复查, main.pdf 检查. **Use when**: 论文已经回写到 TeX 交付层,需要确认是否真正达到“可提交”的质量,而不是只做到能编译。 **Skip if**: 还处于中间层重构阶段,`chapters/*.tex` 尚未形成稳定交付稿。 **Network**: none. **Guardrail**: 不在这里重构章节主线;如果发现结构问题,明确回退到上游修复。

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WILLOSCAR/research-units-pipeline-skills

front-matter-writer

Write the survey's front matter files (Abstract, Introduction, Related Work, Discussion, Conclusion) in paper voice, with high citation density and a single evidence-policy paragraph. **Trigger**: front matter writer, introduction writer, related work writer, abstract writer, discussion writer, conclusion writer, 引言, 相关工作, 摘要, 讨论, 结论. **Use when**: you are in C5 (prose allowed) and need the paper-like shell to stop the draft reading like stitched subsections. **Skip if**: `Approve C2` is missing in `DECISIONS.md`, or `citations/ref.bib` is missing. **Network**: none. **Guardrail**: no invented facts/citations; no pipeline jargon in final prose; no repeated evidence disclaimers; only use keys present in `citations/ref.bib`.

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WILLOSCAR/research-units-pipeline-skills

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维护中文毕业论文的 `codex_md/question_list.md`:把本轮问题、边界、优先级、协作方案和验收口径结构化,作为整条 thesis pipeline 的控制面。 **Trigger**: 毕业论文问题清单, thesis question list, 论文修改清单, 本轮目标, 结构问题梳理, review问题整理. **Use when**: 你已经有一批材料或上一轮 review 结果,需要明确这一轮到底修什么、不修什么,并给后续重构与编译复查提供统一入口。 **Skip if**: 当前只是在做一次性局部措辞修改,且没有形成新一轮结构/证据/编译问题。 **Network**: none. **Guardrail**: 不在这里写正文;不把问题单写成长篇散文;每条问题必须可执行、可验收。

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WILLOSCAR/research-units-pipeline-skills

novelty-matrix

Create a novelty/prior-work matrix comparing the submission’s contributions against related work (overlaps vs deltas). **Trigger**: novelty matrix, prior-work matrix, overlap/delta, 相关工作对比, 新颖性矩阵. **Use when**: peer review 中评估 novelty/positioning,需要把贡献与相关工作逐项对齐并写出差异点证据。 **Skip if**: 缺少 claims(先跑 `claims-extractor`)或你不打算做新颖性定位分析。 **Network**: none (retrieval of additional related work is out-of-scope unless provided). **Guardrail**: 明确 overlap 与 delta;尽量给出可追溯证据来源(来自稿件/引用/作者陈述)。

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WILLOSCAR/research-units-pipeline-skills

protocol-writer

Write a systematic review protocol into `output/PROTOCOL.md` (databases, queries, inclusion/exclusion, time window, extraction fields). **Trigger**: protocol, PRISMA, systematic review, inclusion/exclusion, 检索式, 纳入排除. **Use when**: systematic review pipeline 的起点(C1),需要先锁定 protocol 再开始 screening/extraction。 **Skip if**: 不是做 systematic review(或 protocol 已经锁定且不允许修改)。 **Network**: none. **Guardrail**: protocol 必须包含可执行的检索与筛选规则;需要 HUMAN 签字后才能进入 screening。

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WILLOSCAR/research-units-pipeline-skills

rubric-writer

Write a rubric-based peer review report (`output/REVIEW.md`) using extracted claims and evidence gaps (novelty/soundness/clarity/impact). **Trigger**: rubric review, referee report, peer review write-up, 审稿报告, REVIEW.md. **Use when**: peer-review pipeline 的最后阶段(C3),已有 `output/CLAIMS.md` + `output/MISSING_EVIDENCE.md`(以及可选 novelty matrix)。 **Skip if**: 上游产物未就绪(claims/evidence gaps 缺失)或你不打算输出完整审稿报告。 **Network**: none. **Guardrail**: 给可执行建议(actionable feedback),并覆盖 novelty/soundness/clarity/impact;避免泛泛而谈。

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