talont-org/planner-elaborate-assignments

planner-elaborate-assignments

Pass 2 loop body. Receives one phase context file (written by expand_assignments), spawns one L0 subagent per assignment in parallel using the native Agent/Task tool, collects results, and writes per-assignment result files plus a phase sentinel file. The L1 (this skill) is the sole writer for this phase's assignments — no concurrent write races.

When to Use

• Invoked by the planner recipe when the recipe dispatches parallel elaboration via dispatch_items
• One invocation per phase; handles all assignments within the phase in a single session

Arguments

• $1 — Absolute path to the phase context file (written by expand_assignments) (contains id=<phase_id>, metadata.assignment_count, metadata.assignment_ids, metadata.assignment_names, prior_results)
• $2 — Absolute path to the run-scoped planner directory (e.g., {{AUTOSKILLIT_TEMP}}/planner/run-YYYYMMDD-HHMMSS)

Critical Constraints

NEVER:

• Allow L0 subagents to write files directly — L0s return JSON only
• Let an L0 failure abort the phase — always write a stub and continue
• Write output outside $2/assignments/
• Spawn L0s sequentially — always in parallel
• Read {{AUTOSKILLIT_TEMP}} artifacts outside your designated input files and output directory
• Explore parent directories of your input paths (e.g., ls $(dirname $1)/..)
• Read result files from other phases
• Run subagents in the background (run_in_background: true is prohibited)

ALWAYS:

• Spawn all L0 subagents in parallel using the native Agent/Task tool (NOT run_skill — skill session guard blocks it)
• Write the phase sentinel file before emitting the output token
• Emit: phase_assignments_result_dir = <absolute path to assignments/ directory>
• Write a stub result for any L0 that fails or returns invalid JSON

Workflow

Step 1: Parse context file

Read the context file at $1. Extract:

• id — the phase ID (e.g., "P1")
• metadata.assignment_ids — list of assignment IDs for this phase
• metadata.assignment_names — parallel list of assignment names
• prior_results — list of paths to result files from completed prior items

Step 2: Load phase context

Read $2/phases/{id}_result.json (the elaborated phase result). Extract:

• goal — phase-level goal
• scope — phase-level scope list
• technical_approach — overall technical approach for the phase
• assignments — array of assignment objects with name, goal, metadata

Read the task_file_path field from the context file at $1, then read the task description from disk at that path. Every assignment elaboration — its goal, scope, deliverables, and work_packages_preview — must serve the stated task. Do not read the full task text into the L1 context or embed it in the L0 prompt — pass the path reference only and instruct L0s to read the file from disk for scope creep verification.

Step 3: Build per-L0 context packets

For each assignment in the phase, build a self-contained context packet:

• Assignment ID, name, goal (from the phase result's assignments array)
• All other phase assignments in short-form (id, name, goal only — for overlap detection)
• Prior result file paths (from prior_results) for cross-phase dependency detection
• The phase's technical_approach and scope
• The planner directory path $2 for reading prior results if needed

Step 4: Spawn L0 subagents in PARALLEL

Use the native Agent/Task tool to spawn one L0 per assignment simultaneously. All L0s must be launched in a single batch — do NOT wait for one before starting the next.

Each L0 receives a self-contained prompt that:

1. Identifies the assignment (ID, name, goal)
2. Provides short-form context for all other phase assignments
3. Lists prior result file paths for cross-phase overlap analysis
4. Instructs the L0 to use Grep/Glob/Read for codebase analysis (no sub-subagents)
5. Instructs the L0 to decompose into 1–5 work packages
6. Instructs the L0 to return results as JSON between triple-backtick json fences

Each L0 MUST:

• Use Grep/Glob/Read for codebase analysis (no sub-subagent spawning — they are actual leaf nodes)
• Scan for dependencies by comparing scope against other phase assignments (short-form provided in prompt)
• Read prior result files (if paths provided) to detect cross-phase overlaps
• Decompose into 1–5 work packages with: id_suffix (WP1, WP2, ...), name, scope, estimated_files
• Return structured JSON between ```json and ``` delimiters
• Include dependency_notes (string) and overlap_notes (string) in the JSON

Expected L0 return schema:

{
  "id": "P1-A2",
  "phase_id": "P1",
  "name": "<assignment name>",
  "goal": "<one-sentence goal>",
  "technical_approach": "<technical approach description>",
  "dependency_notes": "Depends on P1-A1 for <dependency description>",
  "overlap_notes": "No overlap detected with other assignments",
  "proposed_work_packages": [
    {
      "id_suffix": "WP1",
      "name": "<work package name>",
      "scope": "<scope description>",
      "estimated_files": [
        "src/<path>/<file_a>.py",
        "src/<path>/<file_b>.py"
      ]
    }
  ]
}

Step 5: Collect and validate L0 responses

For each L0 response:

1. Extract JSON from between ```json and ``` delimiters
2. Validate required fields: id, phase_id, name, goal, technical_approach, proposed_work_packages
3. On validation failure OR no valid JSON found: emit WARNING: L0 elaboration failed for {assignment_id} and mark for stub creation

Step 6: Write per-assignment files

For each successful L0 result, write $2/assignments/{assignment_id}_result.json with the full result JSON.

For each failed L0, write $2/assignments/{assignment_id}_result.json with:

{
  "id": "...",
  "phase_id": "...",
  "name": "...",
  "goal": "...",
  "technical_approach": "",
  "proposed_work_packages": [],
  "elaboration_failed": true
}

After writing all assignment files, update $2/work_packages/wp_index.json by appending compact entries for all successful results only (skip stubs). Read the current index, append, and write back atomically. L1 is the sole writer for this phase's assignments — no concurrent writes.

Finally, write the phase sentinel file to $2/assignments/{phase_id}_result.json:

{"id": "<phase_id>", "status": "complete", "assignment_count": N, "failed_count": M}

The sentinel path MUST be $2/assignments/{phase_id}_result.json. The manifest's result_dir points to $2/assignments/, and this path is used to detect phase completion. Verify the path before writing.

Step 7: Emit output token

phase_assignments_result_dir = <absolute path to $2/assignments>