>- Phase 0 of the prd-taskmaster pipeline. Resolves the active backend, initializes the project, configures the provider stack when the TaskMaster backend is active (DETECT-FIRST — never overwrite a working user config), block is hit. Declares the Setup phase complete so DISCOVER can follow.
npx skills add https://github.com/anombyte93/prd-taskmaster --skill setup
Declarative phase skill. Invoked by the prd-taskmaster orchestrator when
current_phase is null or SETUP. Never called directly by a user.
mcp__plugin_prd_go__check_gate(phase="SETUP", evidence={}) for diagnostics.check_gate is an EXIT gate: it verifies you have the evidence to *advance*, not to
*enter*. On first entry you have no evidence yet (Step 4 below produces
validate_setup.ready=true), so a gate_passed: false result here is EXPECTED — the
state machine's legal transitions (None → SETUP) already guarantee only legal entry.
against re-running a completed phase or skipping ahead.
Enforce the gate when you ADVANCE (after the procedure), not on entry.
Run backend detection:
python3 script.py backend-detect
The native engine is the sole generator and needs no external binary — a
keyless host CLI (claude / codex / gemini) on PATH, or a provider API key,
is sufficient (see Chunk 7's atlas setup wizard). The task-master binary is
no longer required or supported; backend-detect reports its presence purely as
informational. Continue with the resolved (native) backend.
Check whether the current project has a .taskmaster/ directory (the engine
still reads/writes the .taskmaster/ file format for tasks and config).
If missing, run backend op init:
python3 script.py init-project
This initialises the native project state and the .taskmaster/ file format. If
.taskmaster/ is present, continue.
execute-task requires .atlas-ai/customizations/system-prompt-template.md
to exist as a precondition (its Entry gate halts otherwise). It cannot
create the file from inside the loop — the failure mode is a hard halt with
no recovery path.
This step ensures the file exists BEFORE execute-task ever runs:
PLUGIN_SKEL="${CLAUDE_PLUGIN_ROOT}/skel/customizations"
mkdir -p .atlas-ai/customizations
if [ ! -f .atlas-ai/customizations/system-prompt-template.md ]; then
if [ -d "$PLUGIN_SKEL" ]; then
cp -n "$PLUGIN_SKEL"/*.md .atlas-ai/customizations/ # -n: no-clobber, copy starter pack
else
: > .atlas-ai/customizations/system-prompt-template.md # empty is fine per execute-task Entry gate
fi
fi
The starter pack (domain-vocabulary.md, system-prompt-template.md,
task-enrichment-rules.md, verification-preferences.md) is editable —
users tune them to project-specific terminology. Empty is acceptable; the
file simply must exist.
Also scaffold .atlas-ai/ship-check.py if it doesn't already exist:
if [ ! -f .atlas-ai/ship-check.py ] && [ -f "${CLAUDE_PLUGIN_ROOT}/skel/ship-check.py" ]; then
cp "${CLAUDE_PLUGIN_ROOT}/skel/ship-check.py" .atlas-ai/ship-check.py
chmod +x .atlas-ai/ship-check.py
fi
(Codified 2026-06-04 — yesterday's run halted at execute-task Entry
because system-prompt-template.md was missing; the file had to be
manually touch-ed from outside the loop.)
When the TaskMaster backend is active, **read task-master models output BEFORE
setting anything.** This is the load-bearing rule. A working user config must
NOT be overwritten silently. When the native backend is active, provider
configuration is handled by the resolved backend and this TaskMaster-specific
step is informational only.
| task-master models output | Action |
|---|---|
| Main / Research / Fallback all populated with a supported provider | SKIP — go to Step 4. |
| Main set, Research/Fallback empty | Partial mutate — fill the empty roles only. |
| All three empty (fresh install) | Full configure — use the default stack below. |
| Provider flagged unsupported / deprecated | Ask the user before mutating. |
Why DETECT-FIRST: v4 dogfood (2026-04-13, LEARNING #9) caught the skill
overwriting a working gemini-cli / gemini-3-pro-preview config because the
procedure wasn't branch-aware. Detect first, mutate only the empty slots.
Default stack (fresh install only):
task-master models --set-main gemini-3-pro-preview --gemini-cli
task-master models --set-research gemini-3-pro-preview --gemini-cli
task-master models --set-fallback gemini-3-flash-preview --gemini-cli
Why Gemini CLI: ~113× more token-efficient than sonnet on parse-prd, free via
any Google account, no API key. One provider, three roles, zero cost.
Alternatives: Claude Max (--claude-code sonnet/opus/haiku), any of the
12 task-master provider families, or a registered MCP research tool for the
Research role.
If tasks already exist, call the MCP tool
mcp__plugin_prd_go__validate_setup or run backend op rate:
python3 script.py rate
If no tasks exist yet (fresh project), skip the probe — Step 3's provider
configuration is sufficient evidence the pipeline is wired.
Render the preflight progress panel and print it. MCP-mode: call
render_status(phase="SETUP") and print its rendered field. CLI-mode:
python3 script.py status --phase SETUP. (Fallback if the renderer is
unavailable — emit a compact one-block status:)
Setup:
task-master: installed (<version>)
project: initialized (.taskmaster/)
provider: <main-provider> (main) / <research-provider> (research)
pipeline: verified
After Steps 1–5 report green:
mcp__plugin_prd_go__advance_phase(expected_current="SETUP", target="DISCOVER", evidence={"validate_setup": <Step 4 result dict>, "provider_configured": True}).The call atomically transitions pipeline.json from SETUP to DISCOVER.
The expected_current field is the compare-and-swap guard;
evidence is stored under phase_evidence[DISCOVER] for audit.
prd-taskmaster skill). Do NOT invokeDISCOVER directly — the orchestrator re-reads current_phase and routes.
run before DISCOVER so later backend ops resolve consistently.
this skill only reports that installation unlocks the TaskMaster backend.
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.
Integration with protocols.io API for managing scientific protocols. This skill should be used when working with protocols.io to search, create, update, or publish protocols; manage protocol steps and materials; handle discussions and comments; organize workspaces; upload and manage files; or integrate protocols.io functionality into workflows. Applicable for protocol discovery, collaborative protocol development, experiment tracking, lab protocol management, and scientific documentation.
Analyzes job descriptions and generates tailored resumes that highlight relevant experience, skills, and achievements to maximize interview chances
Generate Excalidraw diagrams from natural language descriptions. Use when asked to "create a diagram", "make a flowchart", "visualize a process", "draw a system architecture", "create a mind map", or "generate an Excalidraw file". Supports flowcharts, relationship diagrams, mind maps, and system architecture diagrams. Outputs .excalidraw JSON files that can be opened directly in Excalidraw.
Build and distribute Expo development clients locally or via TestFlight
Use when you have a written implementation plan to execute in a separate session with review checkpoints
Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.
Benchling R&D platform integration. Access registry (DNA, proteins), inventory, ELN entries, workflows via API, build Benchling Apps, query Data Warehouse, for lab data management automation.
Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use bioservices.
Take anombyte93/setup from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.