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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.

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See how to install

Files included (2)

  • SKILL.md5.9 KB
  • scripts/run.py8.0 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

Table Schema (two layers: index vs Appendix)

Triggers & routing

  • 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).

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.
  • Read queries.md:draft_profile when present; it controls the table budget, not the evidence standard.
  • 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 by draft_profile:
    • course_paper: >=1 Index table and >=1 Appendix table
    • survey / deep: >=2 Index tables and >=2 Appendix tables
  • 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)

For course_paper, use I1 and A1 as the compact default. Add I2 or A2 only when the evidence supports a distinct reader question. For survey and deep, use the full I1/I2/A1/A2 set below.

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

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

All Options

  • --workspace <workspace> (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):

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

  • Write to a custom schema path (rare):

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