Planner Refine Assignments

planner-refine-assignments

L1 session that refines the assignments for a single phase by spawning one L0 subagent per assignment in parallel (3–5 assignments per phase). Receives a per-phase context file produced by merge_tier_results → _write_refine_contexts, which contains only this phase's assignments and peer_summaries (id/name/goal stubs) for all other phases. Each L0 reviews its assignment in the context of peer_summaries and refined_plan.json, returning structured suggestions. L1 applies suggestions, resolves intra-phase WP ownership conflicts, and writes the phase result file to $3/refine_contexts/{phase_id}_result.json.

When to Use

• Dispatched by the L2 planner recipe in parallel — one session per phase
• Accepts a per-phase context file from $3/refine_contexts/context_{phase_id}.json and refined_plan.json for phase context
• Produces {phase_id}_result.json as input for the downstream merge_refined_assignments step

Arguments

• $1 — Absolute path to the per-phase context file ($3/refine_contexts/context_{phase_id}.json). The file contains:
  • phase_id — identifier for the phase this session processes
  • task_file_path — path to the task description file (read from disk, not inline)
  • assignments — full AssignmentElaborated objects for this phase only (3–5 entries)
  • peer_summaries — {id, name, goal} stubs for all assignments in other phases
• $2 — Absolute path to refined_plan.json (PlanDocument with phases as PhaseElaborated, for phase context)
• $3 — Absolute path to the run-scoped planner directory (e.g., {{AUTOSKILLIT_TEMP}}/planner/run-YYYYMMDD-HHMMSS). Output is written to $3/refine_contexts/{phase_id}_result.json.

Critical Constraints

NEVER:

• Write any file outside $3/
• Directly modify the context file ($1) — always write a new result file
• Allow an L0 subagent to write files directly (L0s return structured text only)
• Emit phase_refined_path before writing the result file
• Skip emitting phase_refined_path even if all L0s fail (write unchanged assignments, still emit)
• Spawn more than 6 L0s in a single parallel batch
• Read {{AUTOSKILLIT_TEMP}} artifacts not passed as positional arguments
• Run subagents in the background (run_in_background: true is prohibited)
• Write L0 prompts to intermediate l0_prompts/ files and read them back into the L1 context — spawn L0 subagents directly from in-memory context packets
• Read source code files, test files, or recipe YAML files directly — codebase exploration is the L0 subagents' responsibility
• Run Bash, Grep, or Glob commands for codebase exploration between L0 spawns

ALWAYS:

• Read phase_id from the context file to construct the output path
• Validate each L0 response for assignment_id, changes (array), dependency_corrections (array), wp_proposal_adjustments (array)
• Log WARNING to stdout for any L0 response that fails validation (skip that assignment)
• Log CRITICAL to stdout for any L0 subagent that fails entirely (proceed with N-1, partial result)
• When two assignments propose WPs covering the same files, assign ownership to the numerically earlier assignment_id using natural sort on numeric suffixes (e.g. P1-A1 beats P1-A2; P1-A2 beats P1-A10); log each resolution
• Emit: phase_refined_path = <absolute path to $3/refine_contexts/{phase_id}_result.json>

Workflow

Step 1: Parse input and validate

Read $1 (per-phase context file). Extract phase_id, assignments, peer_summaries, and task_file_path. Fail immediately (exit non-zero) if assignments is empty or the file is malformed — do not proceed to spawn L0s. The failure message must include the file path and the parse/validation error string:

FATAL: failed to parse {path}: {error_detail}

Read $2 (refined_plan.json). Build a map phase_id → PhaseElaborated for L0 context.

Input schema for the per-phase context file:

{
  "phase_id": "P2",
  "task_file_path": "/path/to/task.md",
  "assignments": [
    {
      "id": "P2-A1",
      "phase_id": "P2",
      "name": "...",
      "goal": "...",
      "technical_approach": "...",
      "dependency_notes": "...",
      "work_packages": [...]
    }
  ],
  "peer_summaries": [
    {"id": "P1-A1", "name": "...", "goal": "..."},
    {"id": "P3-A1", "name": "...", "goal": "..."}
  ]
}

Step 2: Build L0 context packets

For each assignment in assignments, build a context packet containing:

• The full serialized assignment object (AssignmentElaborated)
• The peer_summaries list for cross-phase dependency detection
• The PhaseElaborated entry for the assignment's phase_id from $2
• The target_assignment_id
• task_file_path — the path to the task description on disk (pass the path reference only; do NOT read the task text into the L1 context or embed it in the L0 prompt)
• Instructions: review the target assignment in light of peer_summaries; always read the task from disk at task_file_path for scope creep verification (flag scope creep if the assignment's goal, scope, or deliverables introduce work not requested by the task); return structured suggestions only — do NOT edit files

Step 3: Spawn parallel L0 subagents

Since each phase has 3–5 assignments (always within the 6-L0 ceiling), spawn all in one parallel batch via Agent/Task. Do not spawn more than 6 L0s in a single parallel batch: if assignment count > 6 (unexpected), spawn sequential batches of 6. Between batches, emit anti-prose guard line: --- next batch ---.

Reading l0_prompts/*.txt files back before spawning adds ~15K tokens to the L1 context per L0 with no benefit.

Each L0 must return structured text in this exact format:

assignment_id = P1-A2
changes = [
  {"field": "dependency_notes", "new_value": "Requires P1-A1-WP1 interface before starting"},
  {"field": "technical_approach", "new_value": "..."}
]
dependency_corrections = [
  {"missing_dep": "P1-A1-WP2", "reason": "Uses the auth token cache defined there"}
]
wp_proposal_adjustments = [
  {"wp_id_suffix": "WP1", "action": "remove", "reason": "Duplicate of P1-A1-WP3 which owns users.py"}
]

Step 4: Validate L0 responses

For each L0 response:

• assignment_id must be present and match the expected assignment ID for that L0
• changes must be a valid JSON array (may be empty [])
• dependency_corrections must be a valid JSON array (may be empty [])
• wp_proposal_adjustments must be a valid JSON array (may be empty [])

On assignment_id mismatch (field present but does not match expected ID):

WARNING: L0 response assignment_id mismatch — expected {expected}, got {actual} — skipping

On other validation failure (field absent or array invalid):

WARNING: L0 response for {assignment_id} failed validation — skipping

On L0 subagent complete failure (no response / timeout):

CRITICAL: L0 for {assignment_id} failed — proceeding with N-1 suggestions

Step 5: Resolve WP proposal conflicts

Collect all wp_proposal_adjustments from validated L0 responses. Group by the set of files each WP proposal covers (use estimated_files from the elaborated WP). When two assignments both claim a file set: assign ownership to the assignment with the numerically earlier assignment_id using natural sort on numeric suffixes (e.g. P1-A1 beats P1-A2; P1-A2 beats P1-A10). Log each resolution:

WP CONFLICT: {assignment_id_A} vs {assignment_id_B} — {field} assigned to {winner}

Apply the remove action from the losing assignment's wp_proposal_adjustments.

Step 6: Apply changes

Apply all validated changes to the in-memory assignments document, in assignment ID order. Apply conflict resolutions before applying changes for affected assignments. Skip unrecognized field names:

WARNING: Unrecognized field '{field}' in changes for {assignment_id} — skipping

Apply dependency_corrections by appending to the dependency_notes field.

Step 7: Write output

Write the phase result file to $3/refine_contexts/{phase_id}_result.json, where phase_id is read from the context file. The output schema:

{
  "schema_version": 1,
  "assignments": [...]
}

The assignments list contains only this phase's assignments (3–5 entries) with refinements applied.

Step 8: Emit output token

phase_refined_path = <absolute path to $3/refine_contexts/{phase_id}_result.json>