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Post design review

Skill tykisgod/quick-question/skills/post-design-review

Review a game design document from an implementer's perspective — check self-consistency, playability, buildability, and codebase gaps. Use after writing a design doc, or when you want to validate an existing design against the current codebase.From its SKILL.md

Install
npx -y skills add tykisgod/quick-question --skill post-design-review

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 1 command, including `qq-execute-checkpoint.py pipeline-advance --project . --completed-skill "/qq:post-design-review" --next-skill "/qq:plan"`.

SKILL.md

2.0 KB, 420 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).

Review a game design document from an implementer's perspective.

Arguments: $ARGUMENTS (path to a design document, or empty to use the most recent design doc in Docs/qq/)

Process

  1. Find the document: if a path is given, read it. Otherwise, find the most recent *_design.md in Docs/qq/.
  2. Spawn a review subagent using the Agent tool. The subagent should:
    • Read design-reviewer-prompt.md for the full review checklist
    • Read the design document
    • Independently explore the codebase (Services, configs, existing design docs) to verify claims — do not rely solely on what the document says exists
    • Output the review in the format specified in the reviewer prompt
  3. Verify the findings: as the main agent, independently spot-check the subagent's claims against the codebase. The subagent may misread code or reference stale information. For each finding, confirm or reject it with your own evidence before presenting to the user.
  4. Present the verified review to the user — only include findings you confirmed. Note any subagent findings you rejected and why.
  5. If verdict is HAS GAPS or NEEDS REWORK: revise the document together with the user. Re-run the subagent review after revisions. Loop until SOLID or the user explicitly accepts the gaps.
  6. If verdict is SOLID: confirm and recommend /qq:plan. If invoked with --auto (or called from a skill running in --auto mode): run qq-execute-checkpoint.py pipeline-advance --project . --completed-skill "/qq:post-design-review" --next-skill "/qq:plan", then invoke /qq:plan --auto <document-path>.

What ships with it: 1 file

2.7 KB alongside SKILL.md

Gives 0 of the 12 instructions most docs writing skills give in 420 tokens

Counted across 1,951 of the 3,904 authors here whose files we hold, read 2026-09-06

  • Use third-person for skill descriptionsin 54 of 1951, across 35 files
  • Start descriptions with Use whenin 43 of 1951, across 29 files
  • Run baseline scenarios before writing any skillin 40 of 1951, across 26 files
  • Use active voicein 40 of 1951, across 36 files
  • Map file responsibilities before defining tasksin 36 of 1951, across 29 files
  • Use checkbox syntax for tracking stepsin 35 of 1951, across 27 files
  • Ask one question at a timein 35 of 1951
  • Offer execution options after saving the planin 33 of 1951, across 24 files
  • Include complete code in every stepin 33 of 1951, across 27 files
  • Design units with clear boundaries and interfacesin 31 of 1951, across 23 files
  • Announce the skill usage at the startin 30 of 1951
  • Verify agent compliance after adding the skillin 29 of 1951, across 17 files

Said here and by no other author read

  • Independently explore the codebase
  • Present only confirmed findings to the user
  • Revise document if gaps are found
  • Recommend planning if verdict is solid

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.