tokextract

name: tokextract description: Extract a design system (DTCG tokens + DESIGN.md + audit) from any SwiftUI codebase trigger_phrases: - "extract design tokens from this repo" - "run tokextract" - "audit my SwiftUI design system" - "what design tokens does this app use" - "recover the design system from this codebase"

Tokextract

Reverse-engineers an implicit SwiftUI design system into three artifacts:

• tokens.json — W3C DTCG 2025.10 design tokens (all 9 categories)
• DESIGN.md — LLM-readable brand narrative companion (Google @google/design.md alpha format)
• audit.md — Drift report: magic numbers, near-duplicate colors, off-scale values, Liquid Glass violations, harmonization recommendations

Invoke as: /tokextract --path <swift-repo> [--output <dir>] [--no-llm]

Pipeline

When invoked, run this pipeline. Each step is restartable; if any LLM task fails, re-running picks up where it left off (findings.raw.json is durable).

--no-llm mode skips Steps 2, 3, 4, 6, 7 entirely. Steps 1, 5, 8 still run.

Step 1 — Parse + analyze (deterministic)

node ${CLAUDE_PLUGIN_ROOT}/dist/extract.js parse \
  --path <path> \
  --output <out> \
  [--no-llm] \
  [--max-files 2000] \
  [--delta-e-threshold 2.5] \
  [--skip <categories>] \
  [--target-os <ver>] \
  [--vendor-namespace <s>] \
  [--force-color-space srgb|display-p3|oklch]

This emits:

• <out>/.tokextract/findings.raw.json — AST + regex extraction results for all 9 categories
• <out>/.tokextract/clusters.json — color cluster analysis
• <out>/.tokextract/numericClusters.json — numeric cluster analysis (spacing/radius/shadow)
• <out>/.tokextract/drift.json — off-scale numeric values
• <out>/.tokextract/meta.json — vendor namespace + target OS
• <out>/.tokextract/llm-tasks.json — manifest of pending LLM passes (unless --no-llm)
• <out>/.tokextract/prompts/normalize-<category>-<n>.md — per-category normalize prompts

Step 2 — Run pending normalize tasks (skip if --no-llm)

Read <out>/.tokextract/llm-tasks.json. For each task with status: "pending" and pass: "normalize":

Spawn an Agent subagent with:

• subagent_type: "general-purpose"
• model: task.recommendedModel (e.g. "claude-haiku-4-5-20251001")
• description: task.id (e.g. "normalize-color-1")
• prompt: contents of task.promptPath, prepended with:

Read the prompt below and write your structured JSON response to <task.responsePath>
using the Write tool. Validate that your output matches the Mapping[] schema before writing.
Reply with exactly the word "done" after writing.

---
[contents of task.promptPath]

Independent tasks (different categories or chunks) can run in parallel. After all tasks complete, verify each task.responsePath exists.

Step 3 — Plan harmonize (skip if --no-llm)

node ${CLAUDE_PLUGIN_ROOT}/dist/extract.js plan-harmonize \
  --output <out> \
  [--model-harmonize claude-sonnet-4-6]

This reads clusters.json + numericClusters.json and appends a harmonize task to llm-tasks.json. If there are no clusters, this is a no-op.

Step 4 — Run harmonize task (skip if --no-llm)

Read <out>/.tokextract/llm-tasks.json. For the task with pass: "harmonize" and status: "pending":

Spawn one Agent subagent with task.recommendedModel against the harmonize prompt. The subagent writes <out>/.tokextract/llm-out/mapping.harmonize.json directly via the Write tool.

Step 5 — Emit final artifacts (deterministic)

node ${CLAUDE_PLUGIN_ROOT}/dist/extract.js emit \
  --output <out> \
  [--no-llm]

This:

• Reads LLM normalize outputs from <out>/.tokextract/llm-out/
• Merges normalize + harmonize mappings with findings (Node-side)
• Validates against DTCG 2025.10 schema (Ajv — hard error on failure)
• Writes <out>/tokens.json, <out>/audit.md, <out>/DESIGN.md (stub in --no-llm)
• Writes <out>/preview.html — self-contained visual review of the extracted system (open it directly in a browser; no server, no build step)
• Computes diff vs <out>/.tokextract/previous/tokens.json (if it exists) → appended to audit.md
• Snapshots tokens.json to <out>/.tokextract/previous/tokens.json for diff on next run

Step 6 — Plan narrate (skip if --no-llm)

node ${CLAUDE_PLUGIN_ROOT}/dist/extract.js plan-narrate \
  --output <out> \
  [--model-narrate claude-sonnet-4-6]

This reads tokens.json + audit.md and appends a narrate task to llm-tasks.json.

Step 7 — Run narrate task (skip if --no-llm)

Read <out>/.tokextract/llm-tasks.json. For the task with pass: "narrate" and status: "pending":

Spawn one Agent subagent with task.recommendedModel against the narrate prompt. The subagent reads tokens.json + audit.md and writes <out>/DESIGN.md directly via the Write tool. This is the highest-leverage LLM step — the subagent generates the full brand narrative prose.

Step 8 — Finalize

node ${CLAUDE_PLUGIN_ROOT}/dist/extract.js finalize \
  --output <out> \
  [--no-llm]

Runs the DESIGN.md lint pass (8 rules: broken-ref, missing-primary, contrast-ratio, orphaned-tokens, token-summary, missing-sections, missing-typography, section-order). Prints a summary of what was extracted.

Flags (parse subcommand)

Output structure

<output-dir>/
├── tokens.json              # W3C DTCG 2025.10 — canonical machine truth (all 9 categories)
├── DESIGN.md                # Brand narrative (stub until narrate pass runs)
├── audit.md                 # Drift report (7 sections: magic numbers, near-duplicates,
│                            #   orphaned tokens, off-scale values, glass violations,
│                            #   harmonization, changes since last extraction)
├── preview.html             # Self-contained visual review (swatches, ΔE clusters, type scale)
└── .tokextract/            # Internal state — delete to force clean re-run
    ├── findings.raw.json
    ├── clusters.json
    ├── numericClusters.json
    ├── drift.json
    ├── meta.json            # vendorNamespace + targetOs
    ├── llm-tasks.json
    ├── prompts/
    │   ├── normalize-color-1.md
    │   ├── normalize-typography-1.md
    │   ├── harmonize.md
    │   └── narrate.md
    ├── llm-out/
    │   ├── mapping.color.1.json
    │   ├── mapping.typography.1.json
    │   └── mapping.harmonize.json
    └── previous/
        └── tokens.json      # Snapshot for diff on next run

Error handling

• --max-files exceeded → hard abort with clear error (don't silently degrade)
• Schema validation failure → hard abort with Ajv error details
• DESIGN.md lint failure → hard abort with lint rule details
• LLM task failure → mark task as error in manifest; re-run to retry
• Missing Asset Catalog file → finding emitted with assetMissing: true, severity: error
• No clusters → plan-harmonize no-ops cleanly

Installation

This is a Claude Code plugin distributed through the conorluddy marketplace. To install:

# Add the marketplace (one-time):
/plugin marketplace add conorluddy/tokextract

# Install the plugin:
/plugin install tokextract@tokextract

The plugin is shipped pre-built — no npm install step required at install time.

For local development (modifying the plugin source), build from the source repo:

cd /Users/conor/Development/Extoken/plugins/tokextract
npm install
npm run build

After rebuilding, refresh the installed plugin: /plugin marketplace update tokextract.