Agent skill

table-schema

Define evidence-first table schemas for a survey: what each table must answer, row unit, columns, and which evidence-pack fields are required to fill it. **Trigger**: table schema, schema-first tables, table design, 表格 schema, 先 schema 后填充. **Use when**: you want survey tables that are verifiable and fillable before LaTeX (typically Stage C4, after evidence packs exist). **Skip if**: `outline/table_schema.md` already exists and is refined (covers both index tables and Appendix tables; no placeholders; evidence mapping is explicit). **Network**: none. **Guardrail**: no invented facts; schema must be checkable and map each column to an evidence source.

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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/table-schema

SKILL.md

Table Schema (two layers: index vs Appendix)

Tables are not decorations; they are compression.

A common failure mode in this pipeline: the first table that gets generated looks like an internal index. So we separate tables into two layers:

  1. outline/tables_index.md (internal)
  • purpose: coverage/debugging + fast evidence scan
  • allowed to be more exhaustive
  • should NOT be inserted into the paper
  1. outline/tables_appendix.md (reader-facing)
  • purpose: publishable survey tables (clean layout + high information density)
  • can be inserted into the final PDF as Appendix

This skill designs both layers before filling.

Default mode: semantic (LLM-first)

Treat this as a design task, not running a script.

If a column cannot be filled from existing evidence packs without guessing, the schema is wrong.

Roles (use explicitly)

Table Designer (reader lens)

Mission: choose tables that answer reader questions, not pipeline questions.

Do:

  • make each table answer one question
  • keep tables small enough to fill (two good tables beat one impossible mega-table)

Avoid:

  • internal/log-style tables inside the paper
  • column labels that only make sense inside this repo

Evidence Steward (fillability)

Mission: refuse schemas that require invented facts.

Do:

  • map every column to concrete upstream fields
  • reject columns that would force long paragraph cells

Avoid:

  • TODO columns
  • placeholders ("TBD", "...", "(placeholder)")

Workflow (explicit inputs)

  • Use GOAL.md to keep the reader question and scope stable.
  • Use outline/outline.yml to align table row units with the paper structure.
  • Use outline/subsection_briefs.jsonl to ground table dimensions/axes in the approved structure.
  • Use outline/evidence_drafts.jsonl to ensure every planned column is fillable without guessing.

Inputs

  • outline/outline.yml
  • outline/subsection_briefs.jsonl
  • outline/evidence_drafts.jsonl
  • Optional: GOAL.md

Output

  • outline/table_schema.md

Non-negotiables (schema contract)

  • Minimum definitions:
    • Index tables: >=2
    • Appendix tables: >=2
  • Every table definition must include:
    • the question it answers
    • the row unit (H3 / benchmark / work / failure mode)
    • columns (with cell style constraints)
    • evidence mapping (which upstream fields fill which columns)
  • Cell style: short phrases; avoid paragraph cells.
  • Paper voice: Appendix table captions/columns must be publishable (no pipeline jargon).

Recommended defaults (arxiv-survey family)

Index tables (for table-filler -> outline/tables_index.md)

I1) Subsection map (axes + representative works)

  • Row unit: H3
  • Columns: subsection; axes; representative works
  • Evidence sources: subsection_briefs.axes + citations from evidence_drafts

I2) Concrete anchors (benchmarks / numbers / caveats)

  • Row unit: H3
  • Columns: subsection; anchor facts; representative works
  • Evidence sources: anchor_sheet.anchors

Appendix tables (for appendix-table-writer -> outline/tables_appendix.md)

A1) Method/architecture map (representative works)

  • Row unit: work/system line
  • Columns: work; core idea; loop + interface assumptions; key refs
  • Evidence sources: evidence_drafts (comparisons + definitions) + anchor_sheet

A2) Evaluation protocol / benchmark map

  • Row unit: benchmark or evaluation setting (fallback: protocol dimension)
  • Columns: benchmark/setting; task+metric; key protocol constraints; key refs
  • Evidence sources: anchor_sheet + evidence_drafts.evaluation_protocol

Positive / negative examples

Good (publishable question + fillable columns):

  • Question: "Which benchmarks anchor evaluation in this area, and what task/metric/constraints do they imply?"
  • Columns: Benchmark; Task+metric; Protocol constraints; Key refs
  • Evidence mapping: evidence_drafts.evaluation_protocol + anchor_sheet

Bad (internal/pipeline voice):

  • Question: "What is evidence readiness + verification needs?"
  • Columns: evidence levels, missing fields, TODO checklist

If you want internal diagnostics, put them in an audit report, not in reader-facing tables.

Script (optional bootstrap)

Quick Start

  • python .codex/skills/table-schema/scripts/run.py --help
  • python .codex/skills/table-schema/scripts/run.py --workspace workspaces/<ws>

All Options

  • --workspace <workspace_dir> (required)
  • --unit-id <id> (optional; used only for runner bookkeeping)
  • --inputs <outline;briefs;packs;goal> (optional; override inputs)
  • --outputs <relpath> (optional; defaults to outline/table_schema.md)
  • --checkpoint <C#> (optional; ignored by the bootstrapper)

Examples

  • Bootstrap a two-layer schema (index + Appendix):

    python .codex/skills/table-schema/scripts/run.py --workspace workspaces/<ws>

  • Write to a custom schema path (rare):

    python .codex/skills/table-schema/scripts/run.py --workspace workspaces/<ws> --outputs outline/table_schema.md

Notes:

  • Use the script as a starting point, then refine the schema as a paper artifact.
  • If a column cannot be filled from evidence packs without guessing, the schema is wrong.

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WILLOSCAR/research-units-pipeline-skills

thesis-compile-review

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

thesis-question-list

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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