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Bootstrap

Skill tykisgod/quick-question/skills/bootstrap

Decompose a high-level game vision (pillars + rules + references) into executable epics, then orchestrate the full qq pipeline for each. Use when starting a new project, bootstrapping a prototype from a pitch, or breaking a large initiative into parallel workstreams.From its SKILL.md

Install
npx -y skills add tykisgod/quick-question --skill bootstrap

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

  • 10 stars10 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
  • runs commandsInstructs the agent to run 8 commands, including `qq-bootstrap-state.py init --project . --name "<project-name>" --manifest "Docs/qq/<branch-name>/bootstrap-manifest.md" --epics "Epic 1 name" "Epic 2 name" "Epic 3 name" ... --max-retries 3 --pretty` and 7 more.

SKILL.md

5.3 KB, ~1.2k tokens by cl100k_base, as published. Nobody here has run it

Respond in the user's preferred language (detect from their recent messages, or fall back to the language setting in CLAUDE.md).

Take a high-level game vision and turn it into a structured, executable project — decomposing pillars into epics, then driving each through the qq pipeline.

Arguments: $ARGUMENTS (a description, a file path to a pitch/checklist document, or empty for interactive)

Phase 1: Understand the Vision

Read the input. Extract:

  • Pillars — the 3-5 non-negotiable design pillars (e.g., "turn-based PVP", "simple multiplayer", "WWII theme")
  • Fragment rules — specific details mentioned in passing (unit types, reference games, feel descriptions)
  • Reference games — any games mentioned as inspiration

Then ask about gaps, using these preset directions (ask only what's missing, skip what's already covered):

  1. Target experience — "What should 10 minutes of gameplay feel like?" (if not clear from input)
  2. Scope — "Is this a full game or a playable demo/prototype? How many sessions to reach 'done'?"
  3. Platform & tech — engine, target platform, multiplayer architecture (if not obvious from project context)
  4. Art direction — placeholder/greybox or specific style? (if relevant)
  5. Hard constraints — budget, timeline, team size, must-use systems

Max 5 questions total. Make reasonable assumptions for non-critical unknowns.

Phase 2: Decompose into Epics

Break the vision into epics — each a self-contained vertical slice that can go through the full qq pipeline independently.

Rules:

  • Each epic should be completable in 1-3 qq pipeline runs (design → plan → execute → test)
  • Epics have explicit dependencies: which must finish before which can start
  • Flag which epics can run in parallel
  • Order by: dependencies first, then core-to-peripheral (get the core loop working before polish)

Output a manifest file and save to Docs/qq/<branch-name>/bootstrap-manifest.md.

Present to user for confirmation. This is the key human checkpoint — user approves the breakdown before automation begins.

After approval, initialize state tracking:

qq-bootstrap-state.py init \
  --project . --name "<project-name>" \
  --manifest "Docs/qq/<branch-name>/bootstrap-manifest.md" \
  --epics "Epic 1 name" "Epic 2 name" "Epic 3 name" ... \
  --max-retries 3 --pretty

Then set dependencies and parallel flags for each epic:

qq-bootstrap-state.py set-deps --project . --epic-id 2 --depends-on "1" --pretty
qq-bootstrap-state.py set-deps --project . --epic-id 3 --depends-on "1,2" --pretty

Phase 3: Execute Epics

Check which epics are actionable:

qq-bootstrap-state.py status --project . --pretty

For each actionable epic (pending + all dependencies completed):

  1. Mark as running:
    qq-bootstrap-state.py start-epic --project . --epic-id <N> --pretty
    
  2. Invoke /qq:design --auto with the epic description + relevant pillars
  3. The qq pipeline takes over: design → post-design-review → plan → plan-review → execute → test → commit-push
  4. On pipeline success:
    qq-bootstrap-state.py complete-epic --project . --epic-id <N> --pretty
    
  5. On pipeline failure:
    qq-bootstrap-state.py fail-epic --project . --epic-id <N> --reason "<what failed>" --pretty
    
    • If script returns "action": "retry" → retry the failed pipeline step
    • If script returns "action": "paused" → skip this epic, report to user, move to next

Parallel execution: when status shows multiple actionable epics with parallel: true, dispatch each as a separate subagent using the Agent tool with isolation: "worktree". Each subagent runs the full qq pipeline for its epic.

Between epics: always re-check status to get the next actionable set. Don't hardcode the order — let the state script resolve dependencies.

Phase 4: Integration Check

After all epics complete (or all non-paused ones):

  1. Run /qq:test on the combined result
  2. If tests fail, analyze which epic interactions caused issues
  3. Fix integration problems (this may require a new mini-epic)
  4. Run /qq:commit-push for the final integrated state
  5. Clear state:
    qq-bootstrap-state.py clear --project . --pretty
    

Resume

If a session crashes or the user resumes later, run:

qq-bootstrap-state.py status --project . --pretty

This shows exactly which epics are completed, which are paused, and which are next. Resume from the first actionable epic.

Notes

  • Phase 2 (epic decomposition) is the most important human checkpoint — get this right before automating
  • Each epic goes through the full qq quality pipeline (design review, plan review, code review, tests)
  • The state script enforces retry limits — Claude cannot forget or miscount
  • The manifest in Docs/qq/ is human-readable; the state in .qq/state/bootstrap.json is machine-readable
  • For Jira/Asana integration: after Phase 2, optionally create issues from the manifest using available MCP tools
  • This skill is an orchestrator — it invokes other skills, never writes code itself

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Gives 0 of the 12 instructions most project setup skills give in ~1.2k tokens

Counted across 1,553 of the 3,091 authors here whose files we hold, read 2026-09-06

  • Write the configuration filein 36 of 1553
  • Create the directory structurein 35 of 1553, across 33 files
  • Verify the setupin 31 of 1553, across 28 files
  • Run the setup scriptin 30 of 1553, across 29 files
  • Pre-determine the required sample sizein 29 of 1553, across 12 files
  • Check if the configuration already existsin 29 of 1553
  • Document every testin 26 of 1553, across 10 files
  • Start with a hypothesisin 26 of 1553, across 11 files
  • Ask one question at a timein 22 of 1553
  • Test a single variable per testin 21 of 1553, across 9 files
  • Read product marketing context before asking questionsin 19 of 1553, across 8 files
  • Do not peek and stop earlyin 18 of 1553, across 7 files

Said here and by no other author read

  • read the input vision
  • ask about gaps
  • decompose the vision into epics
  • output a manifest file
  • check which epics are actionable
  • mark as running

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

Keep looking

Skills are one crate of 325,949. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.