--- slug: "prd-taskmaster" source_type: "skill_md" source_url: "https://cdn.jsdelivr.net/gh/anombyte93/prd-taskmaster@main/SKILL.md" repo: "https://github.com/anombyte93/prd-taskmaster" source_file: "SKILL.md" branch: "main" --- --- name: prd-taskmaster description: >- Zero-config goal-to-tasks engine (the Atlas engine). Takes any goal (software, pentest, business, learning), runs adaptive discovery via brainstorming, generates a validated spec, parses into TaskMaster tasks, and hands off to execution. Use when user says "PRD", "product requirements", "I want to build", invokes /atlas, or wants task-driven development. allowed-tools: - Read - Write - Edit - Grep - Glob - Bash - Skill - AskUserQuestion - WebSearch - ToolSearch - mcp__atlas-engine - mcp__plugin_prd_go - mcp__plugin_prd-taskmaster_go - mcp__plugin_atlas-go_go --- # prd-taskmaster — the Atlas engine Zero-config goal-to-tasks engine. AI handles discovery and content; the engine backend (MCP server preferred, `script.py` fallback — see Phase 0) handles mechanics. **Command:** `/atlas` is the canonical invocation (or `/prd-taskmaster`, or just say "I want to build …"). The full plugin install also exposes phase skills; this standalone skill runs the whole pipeline inline. **Script:** `~/.claude/skills/prd-taskmaster/script.py` (all commands output JSON). It is a thin shim over the bundled `prd_taskmaster/` package — the single source of truth shared with the plugin. **Manual flag:** If the user says `--manual`, `manual=true`, or "do it manually", perform the TaskMaster mechanics yourself: write `.taskmaster/docs/prd.md`, write `.taskmaster/tasks/tasks.json` with tasks and subtasks, run `validate-tasks`, then run `enrich-tasks`. Do not block on TaskMaster CLI/MCP parsing. ## When to Use Activate: PRD, product requirements, taskmaster, task-driven development, "I want to build X", any goal. Skip: API docs, test specs, project timelines, PDF creation. ## Phase 0: Engine Backend Resolution (MANDATORY — before any other engine operation) The engine has two interchangeable backends: the **atlas-engine MCP server** (preferred) and **script.py** (zero-dependency fallback). Resolve which one this session uses NOW. Do NOT silently default to script.py. **Claude Code note — deferred tools:** MCP tools are often *deferred*: their names appear in a system-reminder list but they are NOT callable until you load their schemas with the `ToolSearch` tool. "I don't see a callable engine_preflight tool" does NOT mean the server is absent — it almost always means you have not run ToolSearch yet. Resolution procedure, in order: 1. If a `ToolSearch` tool exists in your session: a. `ToolSearch(query="select:mcp__atlas-engine__engine_preflight")` b. If no match: `ToolSearch(query="+engine preflight atlas", max_results=10)` — this also catches plugin-scoped ids such as `mcp__plugin_prd_go__engine_preflight`. c. If a schema loads → **MCP-mode = ON**. Record the prefix (e.g. `mcp__atlas-engine__`). If both user-scope and plugin-scope match, prefer `mcp__atlas-engine__`. 2. No ToolSearch, but an `engine_preflight` MCP tool is already directly callable → **MCP-mode = ON** with that prefix. 3. Otherwise → **MCP-mode = OFF** (CLI-mode). Announce the result before Phase 1, exactly one line: `Engine backend: MCP (*)` or `Engine backend: script.py (CLI fallback)`. Hard rules for the rest of the run: - **MCP-mode ON:** every operation in the "Engine operations" table below MUST use its MCP tool. Running `python3 script.py ` for an op that has an MCP tool in this session's prefix is a compliance failure — the only exceptions are ops in the "Script/agent-only" table and ops whose tool is missing from the resolved prefix (some plugin installs expose fewer tools — fall back to script.py for just those ops). - **MCP-mode OFF:** use the script.py commands exactly as documented below. Other harnesses (codex, gemini) have no ToolSearch and may have no MCP server — CLI-mode is fully supported and not a degraded experience. - If an MCP call errors mid-run (server died/disconnected), say so explicitly, flip to CLI-mode, and continue. ## Phase 1: Zero-Config Preflight Run preflight and auto-detect everything. Ask zero setup questions. **MCP-mode (from Phase 0 — ONE batched call, no script spam):** call `engine_preflight` once — it covers preflight + taskmaster detection + provider configuration + capabilities and returns a `summary` list to present verbatim. Skip every individual script call below entirely. **CLI-mode (zero-dependency installs):** one batched subcommand, same result: ```bash python3 ~/.claude/skills/prd-taskmaster/script.py engine-preflight ``` **From preflight JSON, determine the state:** | Condition | Action | |-----------|--------| | `prd_path` exists + `task_count > 0` | Ask: execute tasks / update PRD / new PRD / review | | `backend.ai_ops == "agent"` | Backend resolves automatically; print ONE info line: add an API key or install `task-master-ai` for headless AI ops; proceed | | manual flag present | Proceed using Native Mode (TaskMaster optional), regardless of TaskMaster CLI/MCP state | | `has_taskmaster == false` + backend selected | Run `init-project` (below), then continue | | `has_taskmaster` but no PRD | Proceed to Discovery | | `has_crash_state` | Offer: resume from crash point or start fresh | **Initialise the project if needed, then auto-configure providers** (silent). Use `init-project` for the resolved backend. For the `taskmaster` backend, this preserves an existing `.mcp.json`; raw `task-master init` overwrites it with a placeholder template. Use `init-taskmaster` only when explicitly operating the `taskmaster` backend: ```bash python3 ~/.claude/skills/prd-taskmaster/script.py init-project # only when .taskmaster/ absent python3 ~/.claude/skills/prd-taskmaster/script.py init-taskmaster # taskmaster backend only python3 ~/.claude/skills/prd-taskmaster/script.py configure-providers python3 ~/.claude/skills/prd-taskmaster/script.py detect-providers ``` If `configure-providers` returns `recommended_action: "init_taskmaster"`, run `init-project` first; if the backend is explicitly `taskmaster`, `init-taskmaster` is also safe and preserves `.mcp.json`. Report compact status: ``` ✓ Backend: taskmaster-api|native-api|agent ✓ Detected: TaskMaster (MCP|CLI) ✓ Detected: Provider (Claude Code|Codex CLI|Anthropic API) ✓ Detected: Research (Perplexity API Free|Perplexity MCP|Perplexity API|fallback) ``` **Gate: backend resolved (always true). Report ai_ops capability. Proceed to Discovery.** ### Provider Defaults Always prefer subscription/native providers before paid API keys: 1. Main: `claude-code` / `sonnet` when `claude` exists; otherwise `codex-cli` / `gpt-5.2-codex` when `codex` exists. 2. Fallback: `codex-cli` / `gpt-5.2-codex` when available; otherwise `claude-code` / `sonnet`. 3. Research: local Perplexity API Free through TaskMaster `openai-compatible` provider: - model: `sonar` - baseURL: `http://127.0.0.1:8765` - `.env` key: `OPENAI_COMPATIBLE_API_KEY="local-perplexity-api-free"` (dummy local key only) Do not require `ANTHROPIC_API_KEY` or paid `PERPLEXITY_API_KEY` when native Claude/Codex and Perplexity API Free are available. ## Phase 2: Discovery Read the phase file and follow it: ``` Read ~/.claude/skills/prd-taskmaster/phases/DISCOVER.md ``` Progressive, adaptive, domain-agnostic discovery via superpowers:brainstorming. **Gate: Discovery complete and user approved design. Proceed to Generate.** ## Phase 3: Generate & Validate Read the phase file and follow it: ``` Read ~/.claude/skills/prd-taskmaster/phases/GENERATE.md ``` Generate spec, validate quality, parse tasks, enrich with metadata. **Gate: PRD validated GOOD+ and tasks created through TaskMaster parse/expand OR Native Mode. Proceed to Handoff.** ### Native Mode (TaskMaster optional) Formerly "Manual Mechanics Mode". The engine produces the same validated task graph without TaskMaster — use it when the user passes `--manual`, TaskMaster isn't installed, or its parsing/expansion is a poor fit. 1. Generate `.taskmaster/docs/prd.md` normally. 2. Manually write `.taskmaster/tasks/tasks.json` in TaskMaster-compatible shape: - top-level object with `tasks: []` - each task has `id`, `title`, `description`, `details`, `testStrategy`, `status`, `dependencies`, `priority`, and `subtasks` - every task has at least 2 subtasks with `id`, `title`, `description`, `status`, and `dependencies` 3. Run: ```bash python3 ~/.claude/skills/prd-taskmaster/script.py validate-tasks python3 ~/.claude/skills/prd-taskmaster/script.py enrich-tasks python3 ~/.claude/skills/prd-taskmaster/script.py validate-tasks --require-phase-config ``` 4. Treat successful validation + enrichment + phaseConfig validation as equivalent to TaskMaster parse + expand. ## Phase 4: Handoff Read the phase file and follow it: ``` Read ~/.claude/skills/prd-taskmaster/phases/HANDOFF.md ``` Detect capabilities, recommend ONE execution mode, hand off. Modes (user-facing names): **Verified Loop** (recommended when superpowers + a loop runner are present), **Auto-Execute** (TaskMaster's native loop), **Plan & Drive** (plan only). **Atlas Fleet** — parallel multi-session execution — appears as an **Atlas Pro** option when a licensed `atlas-launcher` is detected; otherwise it shows as a locked teaser pointing to https://atlas-ai.au/pro. The free engine is always fully usable on its own. **Gate: User chose mode and handoff complete.** ## Feedback At debrief time, every executing agent records how the run went. MCP-mode: `feedback_submit` / `feedback_report`. CLI-mode: `python3 script.py feedback-add --rating <1-5> --agent --harness --task-ref --well --failed --suggest `. Feedback is stored in `.atlas-ai/feedback.jsonl`; summarize it with `python3 script.py feedback-report`. ## Engine operations This table is normative — instruction sites reference operations by name. In MCP-mode use the MCP tool (substitute the Phase-0 prefix); in CLI-mode use the script.py command. | Operation | MCP tool (MCP-mode) | script.py (CLI-mode / fallback) | |-----------|---------------------|---------------------------------| | `engine-preflight` | `engine_preflight` | `engine-preflight` | | `preflight` | `preflight` | `preflight` | | `detect-taskmaster` | `detect_taskmaster` | `detect-taskmaster` | | `backend-detect` | `backend_detect` | `backend-detect` | | `init` | `init_project` | `init-project` | | `init-taskmaster` | `init_taskmaster` | `init-taskmaster` | | `validate-setup` | `validate_setup` | (covered by `engine-preflight`) | | `detect-capabilities` | `detect_capabilities` | `detect-capabilities` | | `load-template` | `load_template` | `load-template --type comprehensive\|minimal` | | `calc-tasks` | `calc_tasks` | `calc-tasks --requirements [--scale solo\|team\|enterprise]` | | `validate-prd` | `validate_prd` | `validate-prd --input ` | | `backup-prd` | `backup_prd` | `backup-prd --input ` | | `parse-prd` | `parse_prd` | `parse-prd --input --num-tasks N [--tag]` | | `rate` | `rate_tasks` | `rate [--tag] [--no-research]` | | `expand` | `expand_tasks` | `expand [--id N ...] [--no-research] [--tag]` | | `next` | `next_task` | `next-task [--tag]` | | `set-status` | `set_task_status` | `set-status --id --status [--tag]` | | `fleet-waves` | `compute_fleet_waves` | `fleet-waves` | | `feedback-add` | `feedback_submit` | `feedback-add --rating <1-5> ...` | | `feedback-report` | `feedback_report` | `feedback-report` | | `status` | `render_status` | `status [--phase P] [--format boxed\|ascii\|json] [--all]` | Render the progress panel at each phase boundary (and on demand) via `status` / `render_status` — the boxed phase tracker, validation scorecard, ship-check gates, and execute progress. Backend behavior is identical through either interface: the `taskmaster` backend wraps native TaskMaster operations safely (init/parse/rate/expand); the `native` backend uses direct API calls or returns `agent_action_required`; `next`/`set-status` are engine-native under every backend. ## Script/agent-only operations (no MCP tool — always script.py, any mode) | Command | Purpose | |---------|---------| | `configure-providers` | Configure native Claude/Codex + local Perplexity API Free defaults | | `detect-providers` | Auto-detect AI providers | | `validate-tasks [--input ] [--require-phase-config]` | Validate manually-authored tasks.json | | `enrich-tasks` | Add phaseConfig metadata to tasks | | `parallel-plan [--missing-only]` | Emit per-task research packets for parallel subagents | | `parallel-apply --input ` | Merge parallel research results atomically | | `parallel-extract --output ` / `parallel-inject --input ` | Tagged ⇄ flat tasks bridge | | `economy-report` | Summarize telemetry per (op_class, model) | ## Parallel Research & Complexity **Decision tree for expansion + research** (token-economy aware): ``` Manual flag → Native Mode (unchanged) pending tasks ≤ 3 → TaskMasterBackend.expand internal: serial NATIVE rate --research, then expand per task (main dir) task-master ≥ 0.43 AND research role is a REAL structured API → TaskMasterBackend.expand internal: NATIVE-PARALLEL (sonar/anthropic/openai… key) one serial analyze-complexity, then N isolated workdirs each running native `expand --id N --research` with an economy-tier model; ONE atomic harvest merge. Failed packets → agent-parallel rerun. free local proxy / no API key / TM provider errors / TM < 0.43 → native/agent path: AGENT-PARALLEL (fallback): parallel-plan → N subagents → parallel-apply ``` Why isolation dirs: task-master 0.43+ uses proper-lockfile + atomic writes, but its 10s lock-stale window vs 30–120s AI calls makes concurrent invocations in ONE directory unsafe — N isolated project dirs sidestep the lock entirely and double as the per-attempt model mechanism (expand has no --model flag; each workdir carries its own config.json). The free local Perplexity proxy returns prose where TaskMaster needs strict JSON — that is why the proxy keeps the agent-normalized path while real APIs get the native path. Pattern — the parallelism lives in the AGENT, not the script: ```bash python3 ~/.claude/skills/prd-taskmaster/script.py parallel-plan --missing-only # research packets JSON # AGENT: split packets into N groups (by lane/domain), spawn N parallel research # subagents; each verifies files in-repo + researches APIs and returns # [{id, complexityScore, recommendedSubtasks, reasoning, researchNotes, subtasks[]}] # AGENT: concatenate results -> results.json python3 ~/.claude/skills/prd-taskmaster/script.py parallel-apply --input results.json # ONE atomic write # + writes .taskmaster/reports/task-complexity-report[_].json (TaskMaster format) # + returns needs_more_subtasks (score >= threshold w/ too-few subtasks) for a second pass ``` If the `perplexity-api-free` MCP wrapper times out or says the proxy is unreachable, check direct proxy health: ```bash curl -sS -X POST http://127.0.0.1:8765/chat/completions \ -H 'Content-Type: application/json' \ -d '{"model":"sonar","messages":[{"role":"user","content":"Return exactly: ok"}],"max_tokens":16}' ``` If direct `curl` works, continue: use MCP, direct proxy calls, or agent research to produce the `results.json` schema, then normalize prose into valid JSON before `parallel-apply`. Do not block on native `task-master analyze-complexity --research`. Tag bridge for explicit flat-file workflows (the script also reads tagged TaskMaster files directly): ```bash python3 ~/.claude/skills/prd-taskmaster/script.py parallel-extract --output /tmp/flat.json python3 ~/.claude/skills/prd-taskmaster/script.py validate-tasks --input /tmp/flat.json python3 ~/.claude/skills/prd-taskmaster/script.py enrich-tasks --input /tmp/flat.json python3 ~/.claude/skills/prd-taskmaster/script.py validate-tasks --input /tmp/flat.json --require-phase-config python3 ~/.claude/skills/prd-taskmaster/script.py parallel-inject --input /tmp/flat.json ``` All commands default `--tag` to `.taskmaster/state.json` currentTag and run from the project root. ## Context **Standalone:** Works on its own. Takes any goal, produces spec + tasks. **Produces:** spec.md + tasks.json (in `.taskmaster/`). **Then:** hand off to an execution mode (Verified Loop / Auto-Execute / Plan & Drive), or **Atlas Fleet** for parallel multi-session execution with Atlas Pro. ## Critical Rules 1. Zero setup questions — detect everything, ask only discovery questions 2. Discovery via superpowers:brainstorming — one question at a time, adaptive 3. Domain-agnostic — works for any goal (app, pentest, business, anything) 4. Validate PRDs catch placeholders — mustache, TBD, TODO patterns fail validation 5. Manual flag means "do the TaskMaster mechanics manually", not "skip validation" 6. Handoff recommends ONE mode — present best fit, not equal choices 7. Phase files must be Read explicitly — they are not auto-loaded 8. Native/free provider defaults are enforced by `configure-providers`; do not drift back to paid Anthropic/Perplexity APIs unless native/free routes are unavailable 9. Perplexity API Free research must be normalized through `parallel-apply`; native TaskMaster research is only acceptable when it returns valid structured output and validation passes 10. Phase 0 backend resolution is mandatory — in MCP-mode, script.py is forbidden for any op that has an MCP tool in the resolved prefix