handoff

Phase 3: Handoff

Declarative phase skill. Invoked by the prd-taskmaster orchestrator when current_phase is HANDOFF. Never called directly by a user.

The one rule: detect what the user has, recommend ONE mode, give the user a structured choice, dispatch the chosen mode. Mode D executes only on tier=premium; otherwise it is a locked teaser.

Entry gate

1. Call mcp__plugin_prd_go__check_gate(phase="HANDOFF", evidence={}) for diagnostics.

   check_gate is an EXIT gate: it requires user_mode_choice and plan_file_exists — both produced by HANDOFF itself, i.e. evidence to advance, not to enter. On first entry neither exists yet, so a gate_passed: false here is EXPECTED — the state machine's legal transitions already guarantee only legal entry.

   • First entry (no evidence yet): note the result and continue with the Procedure.
   • Re-entry: if the gate reports violations, report them and stop — it protects against re-running a completed phase or skipping ahead from GENERATE.

2. Read the GENERATE outputs — .taskmaster/docs/prd.md, .taskmaster/tasks/tasks.json, .taskmaster/reports/task-complexity-report.json. If any are missing, report and stop. The gate should have caught this, but belt-and-braces.

Handoff checklist

Copy into your response before running the procedure:

HANDOFF CHECKLIST:
- [ ] Capabilities detected (tier + per-capability flags)
- [ ] Recommended mode: ___ (reason: ___)
- [ ] Summary displayed (spec location, task count, capabilities)
- [ ] CLAUDE.md task workflow appended (idempotent)
- [ ] AskUserQuestion mode picker surfaced (or prose fallback if hook-blocked)
- [ ] User choice dispatched (Mode A / B / C, or D when tier=premium)
- [ ] Debrief scaffold emitted (optional, silently tolerated)
- [ ] Handoff complete

Step 1: Detect capabilities

MCP (preferred): mcp__plugin_prd_go__detect_capabilities()

CLI fallback: python3 script.py detect-capabilities

Returns a tier field ("free" or "premium") plus per-capability flags. Key signals:

Capability	What It Enables
superpowers plugin	Modes A, C (brainstorm, plans, subagents)
task-master-ai (CLI or MCP)	Mode B (native auto-execute loop)
ralph-loop plugin	Mode C (iterative execution loop)
atlas-launcher MCP (licensed)	Mode D — Atlas Fleet (tier=premium)
atlas-loop / atlas-cdd skills	legacy Mode-D seeds — superseded by atlas-launcher detection
Research model (task-master or MCP)	Deep research per task
Playwright MCP	Tier S browser verification

Mode D (Atlas Fleet) unlocks on tier: "premium" only — i.e. a licensed atlas-launcher MCP registration detected by detect_atlas_launcher(). Local atlas-loop/atlas-cdd skills do NOT unlock it. See Step 2 and the Mode D section below.

Step 2: Recommend ONE mode

Decision logic (first match wins):

• superpowers + ralph-loop present → Mode C (recommended free)
• superpowers only → Mode A (plan-only, manual drive)
• task-master-ai only → Mode B (native auto-execute)
• Fallback → Mode A

External-tool modes (E–J: Cursor, RooCode, Codex, Gemini, CodeRabbit, Aider) are offered as alternatives via the alternative_modes field, not primary recommendations. Mode D is recommended iff tier == "premium" AND the task graph parallelizes (>= 2 independent dependency chains — check fleet-waves output: any wave with >= 2 chunks). Premium + serial graph: recommend the best free mode and say why ("your tasks form a single dependency chain — Verified Loop is the right tool here"); Fleet stays selectable but not default. Free tier: Mode D is a locked Atlas Pro teaser, never selectable, regardless of which local plugins are installed.

Mode A: Plan Only (Manual)

Recommended: Plan Only
  superpowers:writing-plans creates your implementation plan
  Plan references TaskMaster task IDs from tasks.json
  You drive execution manually

Mode B: TaskMaster Auto-Execute (Mode B — TaskMaster backend only)

Recommended: TaskMaster Auto-Execute
  MCP:  mcp__task-master-ai__next_task -> implement -> set_task_status(id, "done") (Mode B — TaskMaster backend only)
  CLI:  task-master next -> implement -> task-master set-status --id N --status done (Mode B — TaskMaster backend only)
  Native TaskMaster execution loop (no external orchestrator required)

Mode C: Plan + Ralph Loop (Recommended Free)

Recommended: Plan + Ralph Loop
  superpowers:writing-plans → implementation plan referencing tasks.json IDs
  ralph-loop wraps each task:
    next_task → set_task_status("in-progress") → research if <80% confident
    → subagent-driven-development → execution gate (Tier A+ evidence)
    → post-doubt check → log to .claude/verification-log.md
    → set_task_status("done") → TodoWrite → repeat
  Completion: doubt agent reviews verification log before promise satisfied.

Mode D: Atlas Fleet (selectable on tier=premium; 🔒 locked teaser on free)

🔒 Atlas Fleet                                          Atlas Pro · $29/mo
  Parallel multi-session execution across Claude, Codex, and Gemini:
    your task graph split into dependency waves of isolated git worktrees
    checker-gated merges into one integration branch, one final PR
    durable inbox result collection (verified, not narrated)
    CDD evidence card per task; one SHIP_CHECK_OK at the end
    Walk away, come back to proof.

  Unlock: https://atlas-ai.au/pro   (the free modes above stay free forever)

When tier == "premium" (licensed atlas-launcher detected): Mode D is a real, selectable mode — dispatching it invokes /prd:execute-fleet (the wave orchestrator skill). Show the unlocked card:

▸ Atlas Fleet                     ★ Pro · license active
  <N> waves · est. from your dependency graph · walk-away

When tier == "free": Mode D is a locked teaser — not selectable, never executed. If the user selects it while locked, respond with:

"Atlas Fleet is part of Atlas Pro ($29/mo). On this project it would split your tasks into parallel waves across isolated worktrees with checker-gated merges and one final PR. Unlock at https://atlas-ai.au/pro — your spec and tasks are saved. Meanwhile, everything else is free forever: please pick one of the free modes below."

Then re-invoke the mode picker (AskUserQuestion) with Mode D removed from the options.

Alternative modes E–J (external AI tools)

detect_capabilities returns alternative_modes when these tools are installed. Users can pick any of them instead of Modes A–D. All are tool-agnostic wrappers around the same .taskmaster/tasks/tasks.json.

Mode	Tool	Invocation
E	Cursor Composer	cursor --open .taskmaster/tasks/tasks.json, @-ref in Composer
F	RooCode	VS Code command palette → RooCode: Run tasks.json
G	Codex CLI	python3 script.py next-task | codex implement (free via ChatGPT)
H	Gemini CLI	gemini --file .taskmaster/tasks/tasks.json implement next (free via Google)
I	CodeRabbit	Implement via A–H, open PR, CodeRabbit reviews per task. Combines with other modes.
J	Aider	aider --read .taskmaster/tasks/tasks.json — pair-programming style

Step 3: Append task workflow to CLAUDE.md

Use the deterministic subcommand — do not do raw Read+Edit. This path is idempotent, takes a timestamped backup when modifying an existing file, and uses HTML-comment sentinels so re-runs are no-ops.

1. The workflow content is the same every run — write it to a tempfile:

   ## Task Execution Workflow (prd-taskmaster)
   
   When implementing tasks, prefer backend operations:
   1. `python3 script.py next-task` — get next ready task
   2. `python3 script.py set-status --id <id> --status in-progress` — note hyphen; underscore is rejected
   3. Implement the task (follow the plan step linked to this task)
   4. `python3 script.py set-status --id <id> --status done` — mark complete
   5. Update TodoWrite with progress
   6. Repeat from step 1
   
   Valid statuses: `pending`, `in-progress`, `done`, `review`, `blocked`, `deferred`, `cancelled`.
   
   ### Progress Tracking
   - Update TodoWrite BEFORE and AFTER each task
   - Cannot proceed to next task without updating TodoWrite
   - TodoWrite = user visibility. TaskMaster = source of truth.
   

2. Run the append command. No Plan Mode preview — the subcommand itself is idempotent (HTML-comment sentinels gate the write), so the information is surfaced to the user via the Step-4 summary and the AskUserQuestion options (Step 5) before dispatch, not via a plan dialog:

   python3 $SKILL_DIR/script.py append-workflow \
     --target ./CLAUDE.md \
     --content-file /tmp/pdtm-workflow-section.md
   

   The JSON response reports one of:

   • action: "created" — no prior CLAUDE.md, fresh file with markers
   • action: "skipped" (reason: markers_present) — already wired, no-op
   • action: "appended" — existing CLAUDE.md untouched except for the appended marker block; backup_path points at CLAUDE.md.prd-taskmaster-backup-<ts>

   If the user wants a preview before the write, surface the planned content inside the AskUserQuestion options or as an informational paragraph in the Step 4 summary — describe what would be written without invoking any plan-dialog tool.

Step 4: Display summary

Render the handoff panel and print it: MCP render_status(phase="HANDOFF") → print rendered; CLI python3 script.py status --phase HANDOFF. (Fallback — emit a compact block before the mode picker so the user has full context:)

Spec Generated: .taskmaster/docs/prd.md
Validation: <GRADE> (<score>/<max>)
Tasks: <count> tasks parsed with dependencies (see .taskmaster/tasks/tasks.json)
Complexity: analyzed via TaskMaster (.taskmaster/reports/task-complexity-report.json)
Research: <expanded|skipped>

Capabilities:
  [check] TaskMaster (MCP|CLI)
  [check|circle] Playwright (browser verification)
  [check|circle] Research provider
  [check|circle] Ralph-loop plugin
  [check|circle] Atlas Fleet (premium: selectable · free: locked)

Step 5: Mandatory AskUserQuestion for mode selection

HANDOFF is the moment of user agency. Prose recommendations are skippable; tool calls are not. You MUST invoke AskUserQuestion in this step. This is hard-enforced — prose-only fallback is a bug, not a shortcut.

AskUserQuestion gives the user an explicit, structured, machine-readable choice. That's the durable handoff record. The user's selection is logged programmatically and downstream steps dispatch on it directly — no parsing natural-language affirmatives, no ambiguity.

Sequence

1. Emit a handoff summary covering:

   • PRD path + validation grade
   • Task count + complexity breakdown
   • Recommended mode (A/B/C) + one-line reason
   • Alternative modes available (E–J when detected, collapsed under "Use another tool…")
   • Mode D 🔒 Atlas Fleet teaser with the Atlas Pro price and /pro URL
   • A "next step" description scoped to the recommended mode (e.g. for Mode B: "run task-master next" with the first ready task ID (Mode B — TaskMaster backend only))

2. Call AskUserQuestion with a multi-option question listing each available execution mode. Use the user-facing names (internal IDs in parentheses are for this skill only — never shown to the user):

   • Plan & Drive (Mode A) — get the plan, implement it yourself
   • Auto-Execute (Mode B) — TaskMaster's native loop, lighter verification
   • Verified Loop (Mode C) — evidence-gated single-session loop (recommended when superpowers + a loop runner are present)
   • 🔒 Atlas Fleet (Mode D) — Atlas Pro $29/mo, parallel multi-session
   • "Use another tool…" — expands the applicable alternatives from E–J
   • "Show me more detail before I decide" — loops back to Step 4 summary

   Mark the recommended mode as the default (Atlas Fleet may be the default only when tier=premium AND the graph parallelizes). Selecting Atlas Fleet while locked (free tier) returns the upgrade response (see the Mode D block in Step 2) and re-prompts with only the free modes (plus any applicable alternatives).

3. Dispatch the chosen mode:

   • Mode A handoff: invoke superpowers:writing-plans with spec path .taskmaster/docs/prd.md

   • Mode B handoff (Mode B — TaskMaster backend only): show the task-master next command + the first ready task ID surfaced from .taskmaster/tasks/tasks.json

   • Mode C handoff: write .claude/atlas-loop-prompt.md describing the task-execution contract, then invoke /goal with the condition:

     "SHIP_CHECK_OK has been emitted by .atlas-ai/ship-check.py AND all tasks in .taskmaster/tasks/tasks.json show status=done AND /sync has been invoked this session"

     The /goal session continues until the Haiku evaluator verifies the condition. Each iteration runs the execute-task 13-step cycle and checks the condition after step 13. /sync MUST be the last action before SHIP_CHECK_OK is emitted (per execute-task Termination). (Migrated from /ralph-loop:ralph-loop 2026-06-04 — Claude Code's built-in /goal evaluator structurally solves the controller-wears- different-hats triple-verify rot caught in the 2026-06-03 audit.)

   • Mode D handoff (tier=premium): invoke /prd:execute-fleet — it owns the wave loop, worker dispatch, verification, merges, and SHIP_CHECK_OK termination. (free tier: upgrade response only, re-prompt)

Hook-blocked fallback (graceful degradation)

If PreToolUse:AskUserQuestion is hook-blocked (automated / orchestrator / fleet session), fall back to a prose option table preserving the same semantics — labels, descriptions, Mode D locked Atlas Pro teaser, recommended mode marked. Surface the hook block as an [AI] insight block so the parent orchestrator can detect the fallback:

[AI] Hook blocked AskUserQuestion — a PreToolUse hook disables interactive questions for this session (automated mode). Surfacing the mode picker in prose instead. A parent orchestrator should either lift the hook for skills with requires_user_agency:true or supply the mode selection as part of the spawn directive.

The prose fallback MUST NOT invoke any plan-mode dialog. AskUserQuestion is the sole user-agency mechanism in this skill — when it is unavailable, the prose table is the documented fallback.

Hard-coded programmatic path (for tests and fleet orchestrators)

The skill's deterministic layer exposes python3 script.py handoff-gate --recommended <A|B|C> (when implemented). This emits the full mode option set as structured JSON on stdout, enabling tests and external orchestrators to drive the handoff without the LLM layer. Use this when you need deterministic, LLM-skippable handoff enforcement.

Anti-pattern: prose-only prompt

DO NOT say "Ready to proceed with Mode X? (or type 'options')" as your only gate. That is a prose prompt the model can skip or satisfy with a fake affirmative. The v4 dogfood (LEARNING #16 → #20) surfaced this exact pattern as a user-agency hole. AskUserQuestion is the fix.

Step 6: Auto-scaffold dogfood debrief

Every successful HANDOFF calls the deterministic debrief scaffolder as its final act, so the run does not leave only artifacts (PRD, tasks.json, complexity report) with no record of what actually happened:

SLUG="$(basename "$PWD")"
python3 "$SKILL_DIR/script.py" debrief \
  --slug "$SLUG" \
  --grade "$VALIDATION_GRADE" \
  --output-dir docs/v4-release 2>/dev/null || true

• Uses the project's directory name as slug (stable, matches human convention).
• Embeds the validation grade captured in Step 4's summary (EXCELLENT 56/57, etc.).
• Defaults to .taskmaster/{tasks/tasks.json, reports/task-complexity-report.json, docs/prd.md} — no path flags needed in the common case.
• Silently tolerates failure (|| true) — a missing complexity report or gitignored docs/v4-release/ must never block a handoff that otherwise succeeded.
• Output path is returned as output_path in the JSON response; surface it to the user as "Debrief scaffolded at: . Judgment sections (worked / broke / meta) left as TODO — fill them in before the memory fades."

If docs/v4-release/ doesn't exist in the target project (most projects won't — this is a prd-taskmaster convention), skip the call or let it fail silently. The scaffold is only useful for projects that retain it.

Evidence Gate

Gate: capabilities detected AND a mode recommended AND CLAUDE.md workflow appended AND AskUserQuestion surfaced (or prose fallback with [AI] insight if hook-blocked) AND the user's choice dispatched.

Emit a compact one-block status:

Handoff:
  capabilities tier: <free|premium>
  recommended mode: <A|B|C>
  CLAUDE.md: <created|appended|skipped>
  picker: <AskUserQuestion|prose-fallback>
  user choice: <A|B|C|D-teased>
  dispatched: <skill/command invoked, or "waitlist re-prompt">
  debrief: <path or "skipped">

Exit gate

After the evidence gate passes:

1. Call mcp__plugin_prd_go__advance_phase(expected_current="HANDOFF", target="EXECUTE", evidence={"user_mode_choice": "<A|B|C>", "plan_file_exists": True, "capabilities_tier": "<free|premium>"}). The call atomically transitions pipeline.json from HANDOFF to EXECUTE. The expected_current field is the compare-and-swap guard; evidence is stored under phase_evidence[EXECUTE] for audit.
2. Return control to the orchestrator (prd-taskmaster skill). Do NOT invoke EXECUTE directly — the orchestrator re-reads current_phase and routes.

Red flags (stop and report, do not paper over)

• "The user typed 'yeah sure' so I'll treat that as Mode C approval" → NO. Use AskUserQuestion. A structured choice is the durable record; prose affirmatives are skippable.
• "AskUserQuestion is hook-blocked so I'll just pick Mode C myself" → NO. Fall back to the prose option table + [AI] insight block. The user still picks; you just surface the choice in a different shape.
• "Mode D (Atlas Fleet) is available locally because atlas-loop is installed, so I'll execute it" → NO. Mode D is always a teaser. Detection returns atlas_auto: false until the feature ships.
• "The CLAUDE.md append had markers already, so I'll skip Step 3 entirely" → NO. action: "skipped" is the expected idempotent outcome; emit it and proceed. Skipping the call means you don't know the state.
• "I can call advance_phase without the evidence gate passing" → NO. Gate first, always.
• "The debrief scaffolder failed so I'll abort the whole handoff" → NO. The scaffolder is silently tolerant (|| true) — a missing docs/v4-release/ is not a handoff failure.

Non-exits

This skill does not use explicit process termination. A hard block reports the reason and returns control to the orchestrator; the orchestrator decides whether to surface to the user.