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table-schema表模式

Agent Skill

table-schema 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

3

许可证

MIT

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:table-schema(表模式)
来源仓库:https://github.com/willoscar/research-units-pipeline-skills
仓库路径:skills/table-schema
安装命令:
npx skills add https://github.com/willoscar/research-units-pipeline-skills --skill table-schema
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/willoscar/research-units-pipeline-skills --skill table-schema

简介

table-schema 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中快速定位候选结果。

  • 适用于基于关键词或任务场景从来源线索中组织结构化数据。
  • 通过 npx skills add 命令从指定 GitHub 仓库安装并使用。
  • 建议确认权限范围和维护状态,避免触发联网或文件读写操作。
  • 可结合原始 README 进一步核验具体功能和调用方式。

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.

适合场景

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用户想查找某类 Agent Skill 时

02

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03

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能力概览

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能力 3

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能力 4

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能力 5

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安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

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8.02%
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