skills/ship-faster/skills/workflow-project-intake/SKILL.md
Use when you need to clarify requirements and route to the right workflow (idea → executable input). Project intake + routing: help the user brainstorm and clarify intent, persist goal/context artifacts, then dispatch to the right workflow or step skill. Default route is workflow-ship-faster (Next.js 16.1.1) for idea/prototype→launch. Triggers: project kickoff, requirements clarification, brainstorm, ideas, discovery, intake.
npx skillsauth add enuno/claude-command-and-control workflow-project-intakeInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
Treat Skills as "stateless execution units", externalize all state to the file system.
This skill is positioned as "project entry / requirements clarification":
This skill is not responsible for actual large-scale implementation (that's the job of execution skills like workflow-ship-faster / workflow-feature-shipper). It's responsible for converging "conversation" into "executable input", then handing off control.
..._path, let the executor read the file itself.tool-hooks-doctor once early; if hooks are missing, offer to install project-level hooks (continue either way).When user input is still "idea/direction/vague requirement", first use workflow-brainstorm module to refine it into an executable design, then enter Intake Checklist.
workflow-brainstorm module requirements:
Converge user input into the following information (ask for what's missing; if user already provided it, don't ask again).
Questioning Protocol (Required):
entry_type: idea | prototypeprototype: repo_root (local path or repo link)need_database: Whether database is needed (default Supabase)need_billing: Whether billing/payment is needed (default Stripe)need_auth: Whether authentication is needed (default: false)need_deploy: Whether deployment to production is needed (default GitHub + Vercel)need_seo: Whether SEO is needed (sitemap/robots/llms.txt etc.)Default (recommended): create in target project root: runs/<workflow>/active/<run_id>/
OpenSpec-compatible mode (when openspec/project.md exists, or user forces it via context.json):
openspec/changes/<change-id>/openspec/changes/archive/YYYY-MM-DD-<change-id>/Backend selection rule (deterministic):
context.json includes "artifact_store": "runs" or "openspec", follow it.openspec/project.md exists in repo_root, use OpenSpec mode.runs/.Recommended structure (OpenSpec-style, low ceremony):
proposal.md: User goal (original question + expected output + constraints + acceptance criteria + non-goals)tasks.md: Executable checklist (- [ ] → - [x]) + approval gatescontext.json: Key context (repo_root, need_* switches, risk preference, known IDs)evidence/ (optional): Evidence/analysis from each analyzer (keep big output out of chat)logs/ (optional):
events.jsonl: Structured event log (append one line per step)state.json: Optional machine-readable state (what’s next / what’s complete)Archive convention (after completion): move active/<run_id>/ → archive/YYYY-MM-DD-<run_id>/
One JSON per line:
{"ts":"2026-01-11T12:00:00Z","step":"evidence","action":"cloudflare.query_worker_observability","status":"ok","artifacts":["evidence/observability.md"],"note":"p95 latency spike"}
Don't log sensitive content (token/email/phone/secret). Write redacted versions when necessary.
Before executing write operations:
tasks.md under an Approvals section (object ID, impact scope, verification method)evidence/ (and optionally logs/events.jsonl)Before routing/execution, write clarification results to:
proposal.md: Goals, scope, acceptance criteria, non-goals, timelinecontext.json: repo_root, entry_type, need_* switches, risk preference, etc.If this Intake includes "design spec" phase (from workflow-brainstorm module), also persist:
evidence/YYYY-MM-DD-<topic>-design.md: Confirmed design and key decisions (for downstream plan/implementation reference)Notes:
context.json (recommended minimal structure):
{
"entry_type": "idea",
"repo_root": "",
"scope": "full",
"artifact_store": "auto",
"need_database": false,
"need_billing": false,
"need_auth": false,
"need_deploy": false,
"need_seo": false
}
Priority recommendations:
workflow-ship-fasterworkflow-ship-faster will persist to runs/ship-faster/active/<run_id>/ by default (or openspec/changes/<change-id>/ when OpenSpec mode is selected), and decide whether to execute DB/payment/deploy/SEO optional steps based on context.json switchestool-design-style-selectorworkflow-feature-shipperreview-react-best-practicessupabase skillstripe skillIf unsure: Complete Intake Checklist first → Write proposal.md/context.json → Then route (don't execute big actions upfront).
Before handing off control to downstream skill, end this skill with 3 paragraphs:
workflow-ship-faster, and explain why)workflow-ship-faster? (Will write artifacts to runs/ship-faster/active/<run_id>/ in your project by default, and require confirmation before critical write operations)"When any of these situations occur, split into parallel subtasks:
Parallel subtask conventions:
proposal_path, context_path, output_dir)evidence/parallel/<task-name>/, return list of artifact pathsWhen running again:
tasks.md first; use logs/state.json only if it exists and you need machine statetasks.md: Checklist reflects reality + execution summary + next stepslogs/events.jsonl for traceabilityAfter completion, do a skill-evolution Evolution checkpoint (3 questions); if user chooses "want to optimize", call skill-improver to review this run and propose minimal patch suggestions.
tools
MemPalace local-first AI memory system. Use when setting up persistent memory for Claude Code sessions, mining project files or conversation transcripts, querying past context, configuring MCP tools, managing the knowledge graph, or troubleshooting palace operations.
tools
LangSmith Python SDK — trace, evaluate, and monitor LLM applications. Covers @traceable decorator, trace context manager, Client API, evaluate() / aevaluate(), comparative evaluation, custom evaluators, dataset management, prompt caching, ASGI middleware, and pytest plugin.
development
LangGraph (Python) — build stateful, controllable agent graphs with checkpointing, streaming, persistence, interrupts, fault tolerance, and durable execution. Covers both Graph API (StateGraph) and Functional API (@entrypoint/@task).
development
LangGraph Graph API (Python) — build explicit DAG agent workflows with StateGraph, typed state, nodes, edges, Command routing, Send fan-out, checkpointers, interrupts, and streaming. Use when you need explicit control flow and graph topology.