Repository Documentation Generator

Production-ready 6-phase pipeline with intelligent orchestration, research-first validation, and conflict-free file ownership.

<what> This command orchestrates specialized AI agents in 6 phases to analyze your code repository and generate comprehensive documentation: </what>

Runtime Compatibility

• Model labels such as Opus, Sonnet, and Haiku are role hints, not hard requirements. Map them to the strongest available host models for the job.
• Task means the host runtime's parallel subagent mechanism. IF the host cannot run true parallel subagents → THEN execute the same work sequentially and preserve exclusive file ownership.
• Pseudocode blocks in this document are behavioral templates. Adapt helper names, file APIs, and retry helpers to the active runtime instead of treating them as literal APIs.
• Session artifacts live under .octocode/documentation/{session-name}/. Short names like analysis.json below refer to files inside that session directory unless stated otherwise.

<steps> <phase_1> Discovery+Analysis (Phase 1) Agent Role: High-capability reasoning model Parallel: 4 parallel agents What: Analyze language, architecture, flows, and APIs Input: Repository path Output: analysis.json </phase_1>

<phase_2> Engineer Questions (Phase 2) Agent Role: High-capability reasoning model What: Generates comprehensive questions based on the analysis Input: analysis.json Output: questions.json </phase_2>

<phase_3> Research Agent (Phase 3) 🆕 Agent Role: Fast research/execution model Parallel: Dynamic (based on question volume) What: Deep-dive code forensics to ANSWER the questions with evidence Input: analysis.json + questions.json Output: research.json </phase_3>

<phase_4> Orchestrator (Phase 4) Agent Role: High-capability reasoning model What: Groups questions by file target and assigns exclusive file ownership to writers Input: questions.json + research.json Output: work-assignments.json (file-based assignments for parallel writers) </phase_4>

<phase_5> Documentation Writers (Phase 5) Agent Role: Fast writing model Parallel: 1-8 parallel agents (dynamic based on workload) What: Synthesize research and write comprehensive documentation with exclusive file ownership Input: analysis.json + questions.json + research.json + work-assignments.json Output: documentation/*.md (16 core docs, 5 required, plus writer-owned supplementary files; QA-SUMMARY.md is generated in Phase 6) </phase_5>

<phase_6> QA Validator (Phase 6) Agent Role: Fast validation model What: Validates documentation quality using LSP-powered verification Input: documentation/*.md + analysis.json + questions.json + research.json Output: qa-results.json + QA-SUMMARY.md </phase_6> </steps>

<subagents> Use the host's subagent mechanism to explore code with MCP tools (localSearchCode, lspGetSemantics(type=definition), lspGetSemantics(type=callers/callees), lspGetSemantics(type=references)). Pick model tiers by capability, not by hard-coded model names. </subagents>

<mcp_discovery> Before starting, detect available research tools.

Check: Is octocode-mcp available as an MCP server? Look for Octocode MCP tools (e.g., localSearchCode, lspGetSemantics, ghSearchCode, npmSearch).

If Octocode MCP exists but local tools return no results:

Suggest: "For local codebase research, add ENABLE_LOCAL=true to your Octocode MCP config."

If Octocode MCP is not installed:

Suggest: "Install Octocode MCP for deeper research: ``json { "mcpServers": { "octocode": { "command": "npx", "args": ["-y", "octocode-mcp"], "env": {"ENABLE_LOCAL": "true"} } } } `` Then restart your editor."

Proceed with whatever tools are available — do not block on setup. </mcp_discovery>

Documentation Flow: analysis.json → questions.json → research.json → work-assignments.json → documentation (conflict-free!)

⚠️ CRITICAL: Parallel Agent Execution

<parallel_execution_critical importance="maximum">

STOP. READ THIS TWICE.

1. THE RULE

Use the strongest parallel mechanism the host supports. Prefer single-message fan-out when the runtime supports concurrent Task calls.

2. FORBIDDEN BEHAVIOR

FORBIDDEN: Claiming work ran in parallel when the host actually executed it sequentially. REASON: False concurrency claims hide runtime limits and make failures harder to reason about.

3. REQUIRED FALLBACK

IF the runtime cannot perform true parallel fan-out:

• Run the same worker scopes sequentially
• Preserve exclusive file ownership
• Keep the same phase boundaries and aggregation steps
• Tell the user that execution is in sequential fallback mode

4. REQUIRED CONFIRMATION

Before launching any parallel phase (1, 3, 5), you MUST verify:

• [ ] The host can run the chosen fan-out pattern
• [ ] No dependencies exist between these parallel agents
• [ ] Each agent has exclusive scope (no file conflicts)

<correct_pattern title="✅ CORRECT: Single response launches all agents concurrently">

// In ONE assistant message, include ALL Task tool invocations when the host supports it:
Task(description="Discovery 1A-language", subagent_type="general-purpose", prompt="...", model="opus")
Task(description="Discovery 1B-components", subagent_type="general-purpose", prompt="...", model="opus")
Task(description="Discovery 1C-dependencies", subagent_type="general-purpose", prompt="...", model="opus")
Task(description="Discovery 1D-flows", subagent_type="general-purpose", prompt="...", model="opus")
// ↑ All 4 execute SIMULTANEOUSLY

</correct_pattern>

<wrong_pattern title="❌ WRONG: Sequential calls lose parallelism">

// DON'T DO THIS when the host supports concurrency - each waits for previous to complete
Message 1: Task(description="Discovery 1A") → wait for result
Message 2: Task(description="Discovery 1B") → wait for result
Message 3: Task(description="Discovery 1C") → wait for result
Message 4: Task(description="Discovery 1D") → wait for result
// ↑ 4x slower! No parallelism achieved

</wrong_pattern>

</parallel_execution_critical>

Execution Flow Diagram

flowchart TB
    Start([/octocode-documentation-writer PATH]) --> Validate[Pre-Flight Validation]
    Validate --> Init[Initialize Workspace]

    Init --> P1[Phase 1: Discovery+Analysis]

    subgraph P1_Parallel["🔄 RUN IN PARALLEL (4 agents)"]
        P1A[Agent 1A:<br/>Language & Manifests]
        P1B[Agent 1B:<br/>Components]
        P1C[Agent 1C:<br/>Dependencies]
        P1D[Agent 1D:<br/>Flows & APIs]
    end

    P1 --> P1_Parallel
    P1_Parallel --> P1Agg[Aggregation:<br/>Merge into analysis.json]
    P1Agg --> P1Done[✅ analysis.json created]

    P1Done -->|Reads analysis.json| P2[Phase 2: Engineer Questions<br/>Single High-Capability Agent]
    P2 --> P2Done[✅ questions.json created]

    P2Done -->|Reads questions.json| P3[Phase 3: Research 🆕<br/>Parallel Research Agents]
    
    subgraph P3_Parallel["🔄 RUN IN PARALLEL"]
       P3A[Researcher 1]
       P3B[Researcher 2]
       P3C[Researcher 3]
    end
    
    P3 --> P3_Parallel
    P3_Parallel --> P3Agg[Aggregation:<br/>Merge into research.json]
    P3Agg --> P3Done[✅ research.json created<br/>Evidence-backed answers]

    P3Done -->|Reads questions + research| P4[Phase 4: Orchestrator<br/>Single High-Capability Agent]
    P4 --> P4Group[Group questions<br/>by file target]
    P4 --> P4Assign[Assign file ownership<br/>to writers]
    P4Assign --> P4Done[✅ work-assignments.json]

    P4Done --> P5[Phase 5: Documentation Writers]
    P5 --> P5Input[📖 Input:<br/>work-assignments.json<br/>+ research.json]
    P5Input --> P5Dist[Each writer gets<br/>exclusive file ownership]

    subgraph P5_Parallel["🔄 RUN IN PARALLEL (1-8 agents)"]
        P5W1[Writer 1]
        P5W2[Writer 2]
        P5W3[Writer 3]
        P5W4[Writer 4]
    end

    P5Dist --> P5_Parallel
    P5_Parallel --> P5Verify[Verify Structure]
    P5Verify --> P5Done[✅ documentation/*.md created]

    P5Done --> P6[Phase 6: QA Validator<br/>Single Validation Agent]
    P6 --> P6Done[✅ qa-results.json +<br/>QA-SUMMARY.md]

    P6Done --> Complete([✅ Documentation Complete])

    style P1_Parallel fill:#e1f5ff
    style P3_Parallel fill:#e1f5ff
    style P5_Parallel fill:#ffe1f5
    style P4 fill:#fff3cd
    style Complete fill:#28a745,color:#fff

Parallel Execution Rules

<execution_rules> <phase name="1-discovery" type="parallel" critical="true" spawn="single_message"> <gate> STOP. Verify parallel spawn requirements. REQUIRED: Use host parallelism when available. FALLBACK: Sequential execution is allowed only when the host cannot run true parallel work. </gate> <agent_count>4</agent_count> <description>Discovery and Analysis</description> <spawn_instruction>Prefer one-response fan-out; otherwise run sequential fallback and preserve exclusive scopes</spawn_instruction> <rules> <rule>Run all 4 agents concurrently when the host supports it; otherwise use sequential fallback</rule> <rule>Wait for ALL 4 to complete before aggregation</rule> <rule>Must aggregate 4 partial JSONs into analysis.json</rule> </rules> </phase>

<phase name="2-questions" type="single" critical="true" spawn="sequential"> <agent_count>1</agent_count> <description>Engineer Questions Generation</description> <spawn_instruction>Single agent, wait for completion</spawn_instruction> </phase>

<phase name="3-research" type="parallel" critical="true" spawn="single_message"> <gate> STOP. Verify parallel spawn requirements. REQUIRED: Use host parallelism when available. FALLBACK: Sequential execution is allowed only when the host cannot run true parallel work. </gate> <agent_count_logic> <case condition="questions &lt; 10">1 agent</case> <case condition="questions &gt;= 10">Ceil(questions / 15)</case> </agent_count_logic> <description>Evidence Gathering</description> <spawn_instruction>Prefer one-response fan-out; otherwise run sequential fallback and preserve batch boundaries</spawn_instruction> <rules> <rule>Split questions into batches BEFORE spawning</rule> <rule>Run all researchers concurrently when the host supports it; otherwise use sequential fallback</rule> <rule>Aggregate findings into research.json</rule> </rules> </phase>

<phase name="4-orchestrator" type="single" critical="true" spawn="sequential"> <agent_count>1</agent_count> <description>Orchestration and Assignment</description> <spawn_instruction>Single agent, wait for completion</spawn_instruction> <rules> <rule>Assign EXCLUSIVE file ownership to writers</rule> <rule>Distribute research findings to relevant writers</rule> </rules> </phase>

<phase name="5-writers" type="dynamic_parallel" critical="false" spawn="single_message"> <gate> STOP. Verify parallel spawn requirements. REQUIRED: Use host parallelism when available. FALLBACK: Sequential execution is allowed only when the host cannot run true parallel work. </gate> <agent_count_logic> <case condition="questions &lt; 25">1 agent</case> <case condition="questions 25-49">2-4 agents</case> <case condition="questions &gt;= 50">4-8 agents</case> </agent_count_logic> <spawn_instruction>Prefer one-response fan-out; otherwise run sequential fallback and preserve exclusive ownership</spawn_instruction> <rules> <rule>Each writer owns EXCLUSIVE files - no conflicts possible</rule> <rule>Run all writers concurrently when the host supports it; otherwise use sequential fallback</rule> <rule>Use provided research.json as primary source</rule> </rules> </phase>

<phase name="6-qa" type="single" critical="false" spawn="sequential"> <agent_count>1</agent_count> <description>Quality Validation</description> <spawn_instruction>Single agent, wait for completion</spawn_instruction> </phase> </execution_rules>

Pre-Flight Checks

<pre_flight_gate> HALT. Complete these requirements before proceeding:

Required Checks

1. Verify Path Existence

• IF repository_path missing → THEN ERROR & EXIT

1. Verify Directory Status

• IF not a directory → THEN ERROR & EXIT

1. Source Code Check

• IF < 3 source files → THEN WARN & Ask User (Exit if no)

1. Build Directory Check

• IF contains node_modules or dist → THEN ERROR & EXIT

1. Size Estimation

• IF > 200k LOC → THEN WARN & Ask User (Exit if no)

FORBIDDEN until gate passes:

• Any agent spawning
• Workspace initialization

</pre_flight_gate>

<instruction> Before starting, validate the repository path and check for edge cases.

1. Verify Path Existence

• Ensure repository_path exists.
• If not, raise an ERROR: "Repository path does not exist: " + path and EXIT.

1. Verify Directory Status

• Confirm repository_path is a directory.
• If not, raise an ERROR: "Path is not a directory: " + path and EXIT.

1. Source Code Check

• Count files ending in .ts, .js, .py, .go, or .rs.
• Exclude directories: node_modules, .git, dist, build.
• If fewer than 3 source files are found:
• WARN: "Very few source files detected ({count}). This may not be a code repository."
• Continue automatically in low-confidence mode unless the caller explicitly requested strict validation.

1. Build Directory Check

• Ensure the path does not contain node_modules, dist, or build.
• If it does, raise an ERROR: "Repository path appears to be a build directory. Please specify the project root." and EXIT.

1. Size Estimation

• Estimate the repository size.
• If larger than 200,000 LOC:
• WARN: "Large repository detected (~{size} LOC)."
• Continue automatically, but prefer conservative exploration and batching.

</instruction>

Initialize Workspace

<init_gate> STOP. Verify state before initialization.

Required Actions

1. Define Directories (CONTEXT_DIR, DOC_DIR)

• REQUIRED: Derive a stable SESSION_NAME first (caller-provided if available; otherwise use a short repository-based name)

1. Handle Existing State

• IF state.json exists in a non-terminal phase → THEN Resume automatically
• IF caller explicitly requests a fresh run → THEN Reset state

1. Create Directories
2. Initialize New State (if not resuming)

FORBIDDEN:

• Starting Phase 1 before state is initialized.

</init_gate>

<instruction>

Workspace Initialization

Before starting the pipeline, set up the working environment and handle any existing state.

1. Define Directories

• Session Directory (CONTEXT_DIR): ${REPOSITORY_PATH}/.octocode/documentation/${SESSION_NAME}
• Documentation Directory (DOC_DIR): ${REPOSITORY_PATH}/documentation

1. Handle Existing State

• Check if ${CONTEXT_DIR}/state.json exists.
• If it exists and the phase is NOT "complete" or "failed":
• Default Behavior: Resume from the saved checkpoint.
• Set RESUME_MODE = true
• Set START_PHASE from the saved state.
• Only if the caller explicitly requests restart/fresh generation:
• WARN: "Restarting from beginning. Previous progress will be overwritten."
• Set RESUME_MODE = false
• Set START_PHASE = "initialized"
• If state.json does not exist or previous run finished/failed, start fresh (RESUME_MODE = false).

1. Create Directories

• Ensure CONTEXT_DIR exists (create if missing).
• Ensure DOC_DIR exists (create if missing).

1. Initialize New State (If NOT Resuming)

• Create a new state.json using the schema defined in schemas/state-schema.json.

</instruction>

Progress Tracker

Display real-time progress:

📊 Documentation Generation Progress v3.1
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Repository: {REPOSITORY_PATH}
Mode: {RESUME_MODE ? "Resume" : "New"}

{if RESUME_MODE}
Resuming from: {START_PHASE}
{end}

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Agent Pipeline Execution

Phase 1: Discovery+Analysis Agent

<phase_1_gate> GATE: START Phase 1 REQUIRED: Spawn 4 agents in ONE message. FORBIDDEN: Sequential calls. </phase_1_gate>

Agent Spec: references/agent-discovery-analysis.md Task Schema/Config: schemas/discovery-tasks.json

See references/agent-discovery-analysis.md → Orchestrator Execution Logic section for full implementation.

Phase 2: Engineer Questions Agent

Agent Spec: references/agent-engineer-questions.md

See references/agent-engineer-questions.md → Orchestrator Execution Logic section for full implementation.

Phase 3: Research Agent 🆕

<phase_3_gate> GATE: START Phase 3 REQUIRED: Spawn N agents in ONE message. FORBIDDEN: Sequential calls. </phase_3_gate>

Agent Spec: references/agent-researcher.md

See references/agent-researcher.md → Orchestrator Execution Logic section for full implementation.

Phase 4: Orchestrator Agent

Agent Spec: references/agent-orchestrator.md

See references/agent-orchestrator.md → Orchestrator Execution Logic section for full implementation.

Phase 5: Documentation Writers

<phase_5_gate> GATE: START Phase 5 REQUIRED: Spawn all writers in ONE message. FORBIDDEN: Sequential calls. </phase_5_gate>

Agent Spec: references/agent-documentation-writer.md

#### Writer Scaling Strategy

See references/agent-documentation-writer.md → Orchestrator Execution Logic section for full implementation.

Phase 6: QA Validator

Agent Spec: references/agent-qa-validator.md

See references/agent-qa-validator.md → Orchestrator Execution Logic section for full implementation.

Completion

update_state({
  phase: "complete",
  completed_at: new Date().toISOString(),
  current_agent: null
})

DISPLAY: "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
DISPLAY: "✅ Documentation Complete!"
DISPLAY: ""
DISPLAY: "📁 Location: {DOC_DIR}/"
DISPLAY: "📊 QA Report: {DOC_DIR}/QA-SUMMARY.md"
DISPLAY: ""

if (parsed_qa && parsed_qa.overall_score):
  DISPLAY: "Quality Score: {parsed_qa.overall_score}/100 ({parsed_qa.quality_rating})"

  if (parsed_qa.overall_score >= 90):
    DISPLAY: "Status: Excellent ✅ - Ready for release"
  else if (parsed_qa.overall_score >= 75):
    DISPLAY: "Status: Good ✅ - Minor improvements recommended"
  else if (parsed_qa.overall_score >= 60):
    DISPLAY: "Status: Fair -️ - Address gaps before release"
  else:
    DISPLAY: "Status: Needs Work -️ - Major improvements required"

  if (parsed_qa.gaps && parsed_qa.gaps.length > 0):
    DISPLAY: ""
    DISPLAY: "Next Steps:"
    for (i = 0; i < Math.min(3, parsed_qa.gaps.length); i++):
      gap = parsed_qa.gaps[i]
      DISPLAY: "  {i+1}. {gap.fix}"

DISPLAY: ""
DISPLAY: "📝 Documentation Coverage:"
DISPLAY: "   {parsed_questions.summary.total_questions} questions researched"
DISPLAY: "   {parsed_qa.question_coverage.answered} questions answered in docs"
DISPLAY: ""
if (exists(DOC_DIR + "/index.md")):
  DISPLAY: "View documentation: {DOC_DIR}/index.md"
else:
  DISPLAY: "View documentation: {DOC_DIR}/01-project-overview.md"
DISPLAY: "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"

EXIT code 0

Error Recovery

If any agent fails critically:

function handle_critical_failure(phase, error):
  DISPLAY: "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
  DISPLAY: "❌ Documentation Generation Failed"
  DISPLAY: ""
  DISPLAY: "Phase: {phase}"
  DISPLAY: "Error: {error.message}"
  DISPLAY: ""

  if (error.recoverable):
    DISPLAY: "This error is recoverable. Run /octocode-documentation-writer again to resume."
    DISPLAY: "State saved in: {CONTEXT_DIR}/state.json"
  else:
    DISPLAY: "This error is not recoverable. Please check the error and try again."
    DISPLAY: "You may need to fix the issue before retrying."

  DISPLAY: ""
  DISPLAY: "Logs: {CONTEXT_DIR}/state.json"
  DISPLAY: "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"

  EXIT code 1

Helper Functions

IMPORTANT: State Synchronization Only the main orchestrator process should update state.json. Individual parallel agents (Discovery 1A-1D, Researchers, Writers) must NOT directly modify state.json to avoid race conditions. Parallel agents should only write to their designated partial result files inside .octocode/documentation/{session-name}/ using their phase-specific contract paths (for example partial-1a-language.json or research-results/partial-research-0.json). The orchestrator aggregates these results and updates state.json after all parallel agents complete.

// NOTE: This function should ONLY be called by the main orchestrator process,
// never by parallel sub-agents. Parallel agents use their designated partial-result path instead.
function update_state(updates):
  current_state = Read(CONTEXT_DIR + "/state.json")
  parsed = JSON.parse(current_state)

  for key, value in updates:
    parsed[key] = value

  Write(CONTEXT_DIR + "/state.json", JSON.stringify(parsed, null, 2))

function estimate_repo_size(path):
  // Quick estimate: count source files
  files = count_files(path, ["*.ts", "*.js", "*.py", "*.go", "*.rs", "*.java"], excludeDir=["node_modules", ".git", "dist", "build"])
  // Assume ~200 LOC per file average
  return files * 200

function count_files(path, patterns, excludeDir):
  // Use localFindFiles MCP tool (mcp__octocode__localFindFiles)
  // Return count of matching files

Retry & Data Preservation Logic

CRITICAL: Never lose partial work. All agents support retry with state preservation.

const RETRY_CONFIG = {
  discovery_analysis: { max_attempts: 3, backoff_ms: 2000 },
  engineer_questions: { max_attempts: 3, backoff_ms: 2000 },
  research:           { max_attempts: 3, backoff_ms: 3000 },
  orchestrator:       { max_attempts: 3, backoff_ms: 2000 },
  documentation:      { max_attempts: 3, backoff_ms: 5000 },  // per writer
  qa:                 { max_attempts: 2, backoff_ms: 1000 }
}

// === RETRY WRAPPER FOR ALL AGENTS ===
function retry_agent(phase_name, agent_fn, options = {}):
  config = RETRY_CONFIG[phase_name]
  state = get_retry_state(phase_name)

  while (state.attempts < config.max_attempts):
    state.attempts++
    update_retry_state(phase_name, state)

    DISPLAY: `⟳ ${phase_name} attempt ${state.attempts}/${config.max_attempts}`

    try:
      result = agent_fn(options)

      // Success - clear retry state
      clear_retry_state(phase_name)
      return { success: true, result }

    catch (error):
      state.last_error = error.message
      update_retry_state(phase_name, state)

      DISPLAY: `⚠️ ${phase_name} failed: ${error.message}`

      if (state.attempts < config.max_attempts):
        DISPLAY: `   Retrying in ${config.backoff_ms}ms...`
        sleep(config.backoff_ms * state.attempts)  // Exponential backoff
      else:
        DISPLAY: `❌ ${phase_name} exhausted all ${config.max_attempts} attempts`
        return { success: false, error, attempts: state.attempts }

  return { success: false, error: state.last_error, attempts: state.attempts }

// === PARALLEL AGENT RETRY (for Discovery, Research, Writers) ===
function retry_parallel_agents(phase_name, agent_tasks, options = {}):
  config = RETRY_CONFIG[phase_name]
  results = {}
  failed_tasks = []

  // First attempt - run all in parallel
  parallel_results = Task_Parallel(agent_tasks)

  for (task_id, result) in parallel_results:
    if (result.success):
      results[task_id] = result
      save_partial_result(phase_name, task_id, result)
    else:
      failed_tasks.push({ id: task_id, task: agent_tasks[task_id], attempts: 1 })

  // Retry failed tasks individually
  for failed in failed_tasks:
    while (failed.attempts < config.max_attempts):
      failed.attempts++
      DISPLAY: `⟳ Retrying ${phase_name}/${failed.id} (attempt ${failed.attempts}/${config.max_attempts})`

      try:
        result = Task(failed.task)
        if (result.success):
          results[failed.id] = result
          save_partial_result(phase_name, failed.id, result)
          break
      catch (error):
        DISPLAY: `⚠️ ${phase_name}/${failed.id} failed: ${error.message}`
        if (failed.attempts < config.max_attempts):
          sleep(config.backoff_ms * failed.attempts)

    if (failed.attempts >= config.max_attempts && !results[failed.id]):
      DISPLAY: `❌ ${phase_name}/${failed.id} failed after ${config.max_attempts} attempts`
      // Load any partial result saved during attempts
      results[failed.id] = load_partial_result(phase_name, failed.id) || { success: false, partial: true }

  return results

// === PARTIAL RESULT PRESERVATION ===
// Uses atomic writes to prevent corruption from concurrent access
function resolve_partial_result_path(phase_name, task_id):
  if (phase_name == "discovery-analysis"):
    discovery_paths = {
      "agent-1a-language": CONTEXT_DIR + "/partial-1a-language.json",
      "agent-1b-components": CONTEXT_DIR + "/partial-1b-components.json",
      "agent-1c-dependencies": CONTEXT_DIR + "/partial-1c-dependencies.json",
      "agent-1d-flows-apis": CONTEXT_DIR + "/partial-1d-flows-apis.json"
    }
    return discovery_paths[task_id] || (CONTEXT_DIR + "/partials/" + phase_name + "/" + task_id + ".json")

  if (phase_name == "research"):
    index = task_id.replace("researcher-", "")
    return CONTEXT_DIR + "/research-results/partial-research-" + index + ".json"

  return CONTEXT_DIR + "/partials/" + phase_name + "/" + task_id + ".json"

function save_partial_result(phase_name, task_id, result):
  target_path = resolve_partial_result_path(phase_name, task_id)
  partial_dir = dirname(target_path)
  mkdir_p(partial_dir)
  temp_path = target_path + ".tmp." + random_uuid()

  // Atomic write: write to temp file, then rename (rename is atomic on POSIX)
  Write(temp_path, JSON.stringify(result))
  rename(temp_path, target_path)  // Atomic operation

function load_partial_result(phase_name, task_id):
  path = resolve_partial_result_path(phase_name, task_id)
  if (exists(path)):
    return JSON.parse(Read(path))
  return null

function load_all_partial_results(phase_name):
  partial_dir = CONTEXT_DIR + "/partials/" + phase_name
  if (!exists(partial_dir)):
    return {}
  files = list_files(partial_dir, "*.json")
  results = {}
  for file in files:
    task_id = file.replace(".json", "")
    results[task_id] = JSON.parse(Read(partial_dir + "/" + file))
  return results

// === RETRY STATE MANAGEMENT ===
function get_retry_state(phase_name):
  state = Read(CONTEXT_DIR + "/state.json")
  parsed = JSON.parse(state)
  return parsed.retry_state?.[phase_name] || { attempts: 0 }

function update_retry_state(phase_name, retry_state):
  update_state({
    retry_state: {
      ...current_state.retry_state,
      [phase_name]: retry_state
    }
  })

function clear_retry_state(phase_name):
  state = JSON.parse(Read(CONTEXT_DIR + "/state.json"))
  if (state.retry_state):
    delete state.retry_state[phase_name]
    Write(CONTEXT_DIR + "/state.json", JSON.stringify(state, null, 2))

Phase-Specific Retry Behavior

Critical Data Protection Rules

// RULE 1: Never overwrite successful output until new output is validated
function safe_write_output(path, content):
  backup_path = path + ".backup"
  if (exists(path)):
    copy(path, backup_path)

  try:
    Write(path, content)
    validate_json(path)  // Ensure valid JSON
    delete(backup_path)  // Only delete backup after validation
  catch (error):
    // Restore from backup
    if (exists(backup_path)):
      copy(backup_path, path)
    throw error

// RULE 2: Aggregate partial results even on failure
// Uses file locking to prevent race conditions during aggregation
function aggregate_with_partials(phase_name, new_results):
  lock_file = CONTEXT_DIR + "/partials/" + phase_name + "/.aggregate.lock"

  // Acquire exclusive lock before aggregation
  lock_fd = acquire_file_lock(lock_file, timeout_ms=5000)
  if (!lock_fd):
    throw new Error("Failed to acquire lock for aggregation: " + phase_name)

  try:
    existing = load_all_partial_results(phase_name)
    merged = { ...existing, ...new_results }
    return merged
  finally:
    release_file_lock(lock_fd)
    delete(lock_file)

// RULE 3: Resume-aware execution
function should_skip_task(phase_name, task_id):
  partial = load_partial_result(phase_name, task_id)
  return partial?.success === true

Key Features

<key_features>

</key_features>

<efficiency_summary>

Efficiency Maximization

Phase 1: 4 agents × parallel = ~4x faster than sequential
Phase 3: N agents × parallel = ~Nx faster than sequential
Phase 5: M agents × parallel = ~Mx faster than sequential

Total speedup: Significant when host parallelism is available

Remember: Use the strongest fan-out the host supports. When true parallelism is unavailable, fall back to sequential execution and preserve the same ownership boundaries. </efficiency_summary>