# Planner Extract Domain

> Extract domain knowledge and naming conventions for planning context

- Skill: `talont-org/planner-extract-domain` (Agent Skill)
- Install (CLI): `npx skillmds@latest add talont-org/planner-extract-domain`
- Raw SKILL.md: https://api.skillmd.com/api/skills/talont-org/planner-extract-domain/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: TalonT-Org (https://skillmd.com/u/talont-org)
- Updated: 2026-09-10
- Page: https://skillmd.com/skills/talont-org/planner-extract-domain

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# planner-extract-domain

Extract domain knowledge, naming conventions, and structural patterns specific to the project. Optional step — failure is non-fatal and the planner recipe continues without domain context.

## When to Use

- Invoked by the planner recipe after `planner-analyze` completes
- Provides richer domain context for decomposition planning

## Arguments

- **$1** — Absolute path to `analysis.json` produced by `planner-analyze`
- **$2** — Absolute path to a file containing the task description. When provided and non-empty, focus domain extraction on areas relevant to the stated task. When empty, perform a full-codebase survey.

## Critical Constraints

**NEVER:**
- Modify any target project files
- Abort the calling recipe on failure — log a warning and return gracefully
- Run subagents in the background (`run_in_background: true` is prohibited)
- If `$1` is empty or the file does not exist, STOP immediately and report failure

**ALWAYS:**
- Read the analysis file from argument $1 before spawning subagents
- Use Explore subagents for all file reads
- Spawn subagents in parallel

## Workflow

### Step 1: Read analysis

Read the `analysis.json` file from argument $1. Use its `language`, `framework`, `architecture_style`, and `key_patterns` fields to focus subagent queries.

### Step 2: Launch 3–5 parallel Explore subagents

Read the task description: if $2 is provided and non-empty, read the file at that path.

If the task description is available, include it in each subagent's prompt: "Focus exploration on
domain vocabulary, abstractions, and integration points relevant to this task: {task}.
Prioritize areas the task will touch over exhaustive full-codebase coverage."

Spawn all concurrently with `model: "sonnet"`. Always spawn agents 1–3; spawn agents 4–5 only when the project has >20 modules or architecture_style is layered/hexagonal:

1. **Domain Vocabulary** — Extract domain-specific terms, entity names, and verb patterns used in identifiers. Look for: class names, function names, docstrings, README files, ADR documents.

2. **Existing Abstractions** — Identify base classes, protocols, ABCs, and reusable interfaces. Look for: `class * (Protocol)`, `ABC`, the `abstractmethod` decorator, shared base types.

3. **Integration Points** — Identify external system boundaries, HTTP clients, database adapters, message queues. Look for: import of third-party HTTP/DB libraries, adapter classes, port/adapter naming.

4. **Cross-cutting Concerns** (deep mode) — Identify async patterns, error handling conventions, logging strategy. Look for: `async def`, custom exception hierarchies, structured logging calls.

5. **Data Flow Patterns** (deep mode) — Identify pipeline stages, transformation chains, data schemas. Look for: dataclass chains, TypedDict, Pydantic models, transformation functions.

### Step 3: Synthesize

Merge all agent outputs into a coherent `domain_knowledge.md` Markdown document with sections: Domain Vocabulary, Key Abstractions, Integration Points, Cross-cutting Concerns, Data Flow Patterns.

### Step 4: Write output (non-fatal)

Write to `$(dirname $1)/domain_knowledge.md`. If any step fails, log a warning to stdout and exit with code 0 — do not propagate the error to the recipe.