ai-training/.cursor/skills/implement/SKILL.md

---
name: implement
description: |
  Orchestrate task implementation with dependency-aware batching, parallel subagents, and integrated code review.
  Reads flat task files and _dependencies_table.md from TASKS_DIR, computes execution batches via topological sort,
  launches up to 4 implementer subagents in parallel, runs code-review skill after each batch, and loops until done.
  Use after /decompose has produced task files.
  Trigger phrases:
  - "implement", "start implementation", "implement tasks"
  - "run implementers", "execute tasks"
category: build
tags: [implementation, orchestration, batching, parallel, code-review]
disable-model-invocation: true
---

# Implementation Orchestrator

Orchestrate the implementation of all tasks produced by the `/decompose` skill. This skill is a **pure orchestrator** — it does NOT write implementation code itself. It reads task specs, computes execution order, delegates to `implementer` subagents, validates results via the `/code-review` skill, and escalates issues.

The `implementer` agent is the specialist that writes all the code — it receives a task spec, analyzes the codebase, implements the feature, writes tests, and verifies acceptance criteria.

## Core Principles

- **Orchestrate, don't implement**: this skill delegates all coding to `implementer` subagents
- **Dependency-aware batching**: tasks run only when all their dependencies are satisfied
- **Max 4 parallel agents**: never launch more than 4 implementer subagents simultaneously
- **File isolation**: no two parallel agents may write to the same file
- **Integrated review**: `/code-review` skill runs automatically after each batch
- **Auto-start**: batches launch immediately — no user confirmation before a batch
- **Gate on failure**: user confirmation is required only when code review returns FAIL
- **Commit per batch**: after each batch is confirmed, commit. Ask the user whether to push to remote unless the user previously opted into auto-push for this session.

## Context Resolution

- TASKS_DIR: `_docs/02_tasks/`
- Task files: all `*.md` files in `TASKS_DIR/todo/` (excluding files starting with `_`)
- Dependency table: `TASKS_DIR/_dependencies_table.md`

### Task Lifecycle Folders

```
TASKS_DIR/
├── _dependencies_table.md
├── todo/        ← tasks ready for implementation (this skill reads from here)
├── backlog/     ← parked tasks (not scheduled yet, ignored by this skill)
└── done/        ← completed tasks (moved here after implementation)
```

## Prerequisite Checks (BLOCKING)

1. `TASKS_DIR/todo/` exists and contains at least one task file — **STOP if missing**
2. `_dependencies_table.md` exists — **STOP if missing**
3. At least one task is not yet completed — **STOP if all done**

## Algorithm

### 1. Parse

- Read all task `*.md` files from `TASKS_DIR/todo/` (excluding files starting with `_`)
- Read `_dependencies_table.md` — parse into a dependency graph (DAG)
- Validate: no circular dependencies, all referenced dependencies exist

### 2. Detect Progress

- Scan the codebase to determine which tasks are already completed
- Match implemented code against task acceptance criteria
- Mark completed tasks as done in the DAG
- Report progress to user: "X of Y tasks completed"

### 3. Compute Next Batch

- Topological sort remaining tasks
- Select tasks whose dependencies are ALL satisfied (completed)
- If a ready task depends on any task currently being worked on in this batch, it must wait for the next batch
- Cap the batch at 4 parallel agents
- If the batch would exceed 20 total complexity points, suggest splitting and let the user decide

### 4. Assign File Ownership

For each task in the batch:
- Parse the task spec's Component field and Scope section
- Map the component to directories/files in the project
- Determine: files OWNED (exclusive write), files READ-ONLY (shared interfaces, types), files FORBIDDEN (other agents' owned files)
- If two tasks in the same batch would modify the same file, schedule them sequentially instead of in parallel

### 5. Update Tracker Status → In Progress

For each task in the batch, transition its ticket status to **In Progress** via the configured work item tracker (see `protocols.md` for tracker detection) before launching the implementer. If `tracker: local`, skip this step.

### 6. Launch Implementer Subagents

For each task in the batch, launch an `implementer` subagent with:
- Path to the task spec file
- List of files OWNED (exclusive write access)
- List of files READ-ONLY
- List of files FORBIDDEN
- **Explicit instruction**: the implementer must write or update tests that validate each acceptance criterion in the task spec. If a test cannot run in the current environment (e.g., TensorRT requires GPU), the test must still be written and skip with a clear reason.

Launch all subagents immediately — no user confirmation.

### 7. Monitor

- Wait for all subagents to complete
- Collect structured status reports from each implementer
- If any implementer reports "Blocked", log the blocker and continue with others

**Stuck detection** — while monitoring, watch for these signals per subagent:
- Same file modified 3+ times without test pass rate improving → flag as stuck, stop the subagent, report as Blocked
- Subagent has not produced new output for an extended period → flag as potentially hung
- If a subagent is flagged as stuck, do NOT let it continue looping — stop it and record the blocker in the batch report

### 8. AC Test Coverage Verification

Before code review, verify that every acceptance criterion in each task spec has at least one test that validates it. For each task in the batch:

1. Read the task spec's **Acceptance Criteria** section
2. Search the test files (new and existing) for tests that cover each AC
3. Classify each AC as:
   - **Covered**: a test directly validates this AC (running or skipped-with-reason)
   - **Not covered**: no test exists for this AC

If any AC is **Not covered**:
- This is a **BLOCKING** failure — the implementer must write the missing test before proceeding
- Re-launch the implementer with the specific ACs that need tests
- If the test cannot run in the current environment (GPU required, platform-specific, external service), the test must still exist and skip with `pytest.mark.skipif` or `pytest.skip()` explaining the prerequisite
- A skipped test counts as **Covered** — the test exists and will run when the environment allows

Only proceed to Step 9 when every AC has a corresponding test.

### 9. Code Review

- Run `/code-review` skill on the batch's changed files + corresponding task specs
- The code-review skill produces a verdict: PASS, PASS_WITH_WARNINGS, or FAIL

### 10. Auto-Fix Gate

Bounded auto-fix loop — only applies to **mechanical** findings. Critical and Security findings are never auto-fixed.

**Auto-fix eligibility matrix:**

| Severity | Category | Auto-fix? |
|----------|----------|-----------|
| Low | any | yes |
| Medium | Style, Maintainability, Performance | yes |
| Medium | Bug, Spec-Gap, Security | escalate |
| High | Style, Scope | yes |
| High | Bug, Spec-Gap, Performance, Maintainability | escalate |
| Critical | any | escalate |
| any | Security | escalate |

Flow:

1. If verdict is **PASS** or **PASS_WITH_WARNINGS**: show findings as info, continue to step 11
2. If verdict is **FAIL**:
   - Partition findings into auto-fix-eligible and escalate (using the matrix above)
   - For eligible findings, attempt fixes using location/description/suggestion, then re-run `/code-review` on modified files (max 2 rounds)
   - If all remaining findings are auto-fix-eligible and re-review now passes → continue to step 11
   - If any non-eligible finding exists at any point → stop auto-fixing, present the full list to the user (**BLOCKING**)
3. User must explicitly approve each non-auto-fix finding (accept, request manual fix, mark as out-of-scope) before proceeding.

Track `auto_fix_attempts` and `escalated_findings` in the batch report for retrospective analysis.

### 11. Commit (and optionally Push)

- After user confirms the batch (explicitly for FAIL, implicitly for PASS/PASS_WITH_WARNINGS):
  - `git add` all changed files from the batch
  - `git commit` with a message that includes ALL task IDs (tracker IDs or numeric prefixes) of tasks implemented in the batch, followed by a summary of what was implemented. Format: `[TASK-ID-1] [TASK-ID-2] ... Summary of changes`
  - Ask the user whether to push to remote, unless the user previously opted into auto-push for this session

### 12. Update Tracker Status → In Testing

After the batch is committed and pushed, transition the ticket status of each task in the batch to **In Testing** via the configured work item tracker. If `tracker: local`, skip this step.

### 13. Archive Completed Tasks

Move each completed task file from `TASKS_DIR/todo/` to `TASKS_DIR/done/`.

### 14. Loop

- Go back to step 2 until all tasks in `todo/` are done

### 15. Final Test Run

- After all batches are complete, run the full test suite once
- Read and execute `.cursor/skills/test-run/SKILL.md` (detect runner, run suite, diagnose failures, present blocking choices)
- Test failures are a **blocking gate** — do not proceed until the test-run skill completes with a user decision
- When tests pass, report final summary

## Batch Report Persistence

After each batch completes, save the batch report to `_docs/03_implementation/batch_[NN]_report.md`. Create the directory if it doesn't exist. When all tasks are complete, produce a FINAL implementation report with a summary of all batches. The filename depends on context:

- **Test implementation** (tasks from test decomposition): `_docs/03_implementation/implementation_report_tests.md`
- **Feature implementation**: `_docs/03_implementation/implementation_report_{feature_slug}.md` where `{feature_slug}` is derived from the batch task names (e.g., `implementation_report_core_api.md`)
- **Refactoring**: `_docs/03_implementation/implementation_report_refactor_{run_name}.md`

Determine the context from the task files being implemented: if all tasks have test-related names or belong to a test epic, use the tests filename; otherwise derive the feature slug from the component names.

## Batch Report

After each batch, produce a structured report:

```markdown
# Batch Report

**Batch**: [N]
**Tasks**: [list]
**Date**: [YYYY-MM-DD]

## Task Results

| Task | Status | Files Modified | Tests | AC Coverage | Issues |
|------|--------|---------------|-------|-------------|--------|
| [TRACKER-ID]_[name] | Done | [count] files | [pass/fail] | [N/N ACs covered] | [count or None] |

## AC Test Coverage: [All covered / X of Y covered]
## Code Review Verdict: [PASS/FAIL/PASS_WITH_WARNINGS]
## Auto-Fix Attempts: [0/1/2]
## Stuck Agents: [count or None]

## Next Batch: [task list] or "All tasks complete"
```

## Stop Conditions and Escalation

| Situation | Action |
|-----------|--------|
| Implementer fails same approach 3+ times | Stop it, escalate to user |
| Task blocked on external dependency (not in task list) | Report and skip |
| File ownership conflict unresolvable | ASK user |
| Test failure after final test run | Delegate to test-run skill — blocking gate |
| All tasks complete | Report final summary, suggest final commit |
| `_dependencies_table.md` missing | STOP — run `/decompose` first |

## Recovery

Each batch commit serves as a rollback checkpoint. If recovery is needed:

- **Tests fail after final test run**: `git revert <batch-commit-hash>` using hashes from the batch reports in `_docs/03_implementation/`
- **Resuming after interruption**: Read `_docs/03_implementation/batch_*_report.md` files to determine which batches completed, then continue from the next batch
- **Multiple consecutive batches fail**: Stop and escalate to user with links to batch reports and commit hashes

## Safety Rules

- Never launch tasks whose dependencies are not yet completed
- Never allow two parallel agents to write to the same file
- If a subagent fails or is flagged as stuck, stop it and report — do not let it loop indefinitely
- Always run the full test suite after all batches complete (step 15)