Taxonomy Builder

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass, no license.

.claude/skills

Audit 0.4.1 · source sha 81994516a430 · 19 Oct 2026Report an issue or request a re-scan

• PublisherWILLOSCAR GitHub userOwner of the repository this skill is read from
• Source folderWILLOSCAR/research-units-pipeline-skills/.codex/skills/taxonomy-builderThe exact folder we audited
• LicenceNot declaredAs the repository states it
• Commit pinned for this verdict81994516a430A new audit runs when the source changes
• Repository statusNot archivedLast push 14 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: Build a 2+ level taxonomy (`outline/taxonomy.yml`) from a core paper set and scope constraints, with short descriptions per node.

---
name: taxonomy-builder
description: "Build a 2+ level taxonomy (`outline/taxonomy.yml`) from a core paper set and scope constraints, with short descriptions per node."
---

# Taxonomy Builder (router, compatibility mode)

## Triggers & routing

- **Trigger**: taxonomy, taxonomy builder, 分类, 主题树, taxonomy.yml.
- **Use when**: survey/snapshot 的结构阶段(NO PROSE),已有 `papers/core_set.csv`,需要生成可映射且读者友好的主题结构。
- **Skip if**: 已经有批准过且可映射的 taxonomy(不要无意义重构)。
- **Network**: none.
- **Guardrail**: 避免泛化占位桶;保持 2+ 层且每节点有具体描述。


Build `outline/taxonomy.yml` from `papers/core_set.csv`.

P0 compatibility note:
- The output contract stays the same (`outline/taxonomy.yml`, YAML list, >=2 levels, concrete descriptions).
- Curated domain taxonomies now live in `assets/domain_packs/*.yaml` instead of Python prose.
- `scripts/run.py` stays a deterministic scaffold/helper: detect domain pack -> load pack when available -> otherwise fall back to the generic builder.

## Load Order

1. `references/overview.md`
2. `references/taxonomy_principles.md`
3. If a domain pack applies, read its `references/domain_pack_<domain>.md` and `assets/domain_packs/<domain>.yaml`
4. Otherwise read `references/archetypes_generic.md`
5. Calibrate naming/description quality with `references/examples_good.md` and `references/examples_bad.md`

Current compatibility packs:
- `llm_agents`
- `gen_image`
- `embodied_ai`
- `rag_evaluation`

## Explicit refinement marker

Create `outline/taxonomy.refined.ok` only after reviewing a manually refined taxonomy. The marker is honored only while it is newer than the taxonomy, its upstream evidence, and the generator; stale markers are removed and the prior taxonomy is backed up before regeneration.

## Inputs

- `papers/core_set.csv` (required)
- Optional: `papers/papers_dedup.jsonl`
- Optional: `DECISIONS.md`, `GOAL.md`, `queries.md`

## Outputs

- `outline/taxonomy.yml`

## Asset contract

- `assets/taxonomy_schema.json`: machine-readable shape for domain packs / output expectations
- `assets/domain_packs/*.yaml`: compatibility domain packs for supported domains

## Script role

Use `scripts/run.py` only for deterministic help:
- never overwrite non-placeholder user taxonomy
- preserve current CLI flags / output path
- load a supported domain taxonomy only when `GOAL.md` / `queries.md` explicitly match its detection contract
- keep the generic fallback builder for non-packed domains

## When to refine manually

Refine the generated taxonomy before marking the unit `DONE` if:
- top-level buckets feel like keyword clusters instead of chapter-level questions
- leaf names are generic (`Overview`, `Benchmarks`, `Open Problems`, `Misc`)
- descriptions lack scope cues or representative paper anchors
- domain detection chose the wrong pack

## Quick start

- `uv run python .codex/skills/taxonomy-builder/scripts/run.py --help`
- `uv run python .codex/skills/taxonomy-builder/scripts/run.py --workspace <workspace>`


## Execution notes

When running in compatibility mode, `scripts/run.py` currently reads:
- `papers/core_set.csv` as the required corpus input
  - `papers/papers_dedup.jsonl` when present for generic corpus signals
  - `GOAL.md` and `queries.md` as the authoritative domain-pack selection intent; corpus term co-occurrence cannot override them

## Script

### Quick Start

- `uv run python .codex/skills/taxonomy-builder/scripts/run.py --workspace <workspace>`

### All Options

- `--workspace <dir>`
- `--top-k <int>`
- `--min-freq <int>`
- `--unit-id <id>`
- `--inputs <a;b;...>`
- `--outputs <a;b;...>`
- `--checkpoint <C*>`

### Examples

- `uv run python .codex/skills/taxonomy-builder/scripts/run.py --workspace <workspace>`

## Troubleshooting

- If the wrong domain pack is chosen, inspect `GOAL.md`, `queries.md`, and the pack `detect` rules before changing Python.
- If `outline/taxonomy.yml` already contains a real non-placeholder taxonomy, the script intentionally returns without overwriting it.
- If no pack matches, the script falls back to the generic builder.

Excerpt, first 4,027 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

1. 2026-10-19
   ✓SAFE B 89 / 100

   first audit · source 81994516a430

Questions about Taxonomy Builder

• github.com/WILLOSCAR/research-units-pipeline-skills/tree/main/.c — the audited source at commit 81994516a430 and every finding aboveObserved · 19 Oct 2026

Spotted something wrong? Report a correction · Methodology