brainstorm
Skill2 starsChanged 16 days agoMITView on GitHub
Brainstorm the intervention — for a named problem in any domain, spread ~10 candidate interventions from a fix for this afternoon to a quarter-long bet without judging them, then size the ones the user picks. Use on "brainstorm interventions", "show me options", or when a problem is named with no approach chosen. NOT for variants of one design — use prototypes. Clear to both sides: no skill or method.
189 other projects use a version of this.
Adopted by
Most recently taken up by
- nielsmadan/skillstheir own version, first seen Nov 8, 2026
- phuthuycoding/kanban-flowtheir own version, first seen Nov 8, 2026
- baethon/skillstheir own version, first seen Nov 8, 2026
- CloudChef/atlasclaw-providerstheir own version, first seen Nov 8, 2026
- nisarth/claude-skills-storetheir own version, first seen Nov 8, 2026
- LongJohnSilver1504/claude-skillstheir own version, first seen Nov 8, 2026
Used with
Projects with it also use playtest-report, team-polish and 10 more.
Other files people use alongside this one.
Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
CoralGameSkillplaytest-report
Generates a structured playtest report template or analyzes existing playtest notes into a structured format. Use this to standardize playtest feedback collection and analysis.
Also in 9 projects that use this
CoralGameSkillteam-polish
Orchestrate the polish team: coordinates performance-analyst, technical-artist, sound-designer, and qa-tester to optimize, polish, and harden a feature or area for release quality.
Also in 11 projects that use this
lilangMaxSkillsoak-test
Generate a soak test protocol for extended play sessions. Defines what to observe, measure, and log during long play sessions to surface slow leaks, fatigue effects, and edge cases that only appear after sustained play. Primarily used in Polish and Release phases.
Also in 6 projects that use this
lilangMaxSkillteam-live-ops
Orchestrate the live-ops team for post-launch content planning: coordinates live-ops-designer, economy-designer, analytics-engineer, community-manager, writer, and narrative-director to design and plan a season, event, or live content update.
Also in 6 projects that use this
- +8 more from CoralGame
Other versions189 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 189
- Donchitos/Claude-Code-Game-Studios★ 27ksame name
- codeaholicguy/ai-devkit★ 1.7ksame name
- jtydhr88/ComfyTV★ 1.2ksame name
- event-driven-io/emmett★ 562same name
- zaxbysauce/opencode-swarm★ 519same name
- ZaxbyHub/opencode-swarm★ 518same name
- automagik-dev/genie★ 351same name
- namastexlabs/automagik-genie★ 351same name
- bkywksj/knowledge-base★ 340same name
- pixel-cellar/Claude-Code-Game-Studios★ 324same name
179 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository12 other files it gives its agents.
- AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- blindspotSkill
- buy-inSkill
- deviation-logSkill
- interviewSkill
- prototypesSkill
- quizSkill
- referenceSkill
- teach-meSkill
- tweakable-planSkill
- unknowns-loopSkill
The file
We split this skill into the rules it gives an agent
3 rules. 0 are common, 3 are distinctive, and 0 are very specific to this project.
0
Common
Most of the 156 edited versions we read keep these.
3
Distinctive
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 30
When you ask back for the problem statement, put these in the same round.
- Distinctive: line 31
Before generating candidates in step 3, look for what already exists but is not used for this problem: in a codebase, machinery that sits …
- Distinctive: line 61
After the user picks interventions, hand off by what is still open:
“Common” is measured: kept by at least 6 in 10 of the 156 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Brainstorm — Solution-Space Map (Brainstorm the Intervention)
This skill serves what neither the user nor you know yet, and surfaces both sides' unknowns.
When you and the user both seem to understand the request, do it without this method: if its result carries weight (it ships, is sent or is hard to undo), first read back in 2–3 lines the request as you understand it, the assumptions you are making and what outcome counts as done, as the whole reply, and do it once the user confirms; a self-evident request (fix a typo) needs no read-back. When the read-back exposes a mismatch, route by who does not know: the user → the teach-me skill; you → the interview skill; neither of you → the blindspot skill. A misread weighty request costs the work built on it; a read-back costs one exchange.
Jumping on the first solution leaves the rest of the solution space unknown. The user picks; you draw the space.
Iron Rules
- The output is a map of solutions, not a solution. It ends at the user's assembled selection.
- Spread candidates across time horizons and approaches, not 10 near-duplicates.
Procedure
Get the problem definition and constraints (timeline, people, cost) from the text the user gave with the request or after the skill name and the conversation. If they are empty or vague, ask back once for a measurable statement: which metric, how bad, since when, for whom. One round only. If the answer is still vague or the user skips, generate the candidates anyway and state the assumed metric and constraints above them.
Your own unknowns: say what you do not know about the user's situation, what you are assuming, and what you cannot check, and ask the user to fill or correct them.
When you ask back for the problem statement, put these in the same round.
Before generating candidates in step 3, look for what already exists but is not used for this problem: in a codebase, machinery that sits disconnected; elsewhere, resources, programs, people or partnerships already in reach.
a. Search the material yourself or use the
unknowns:unknowns-scoutagent.b. When you spawn the scout, the packet overrides the scout's default output (blindspot cards and an improved prompt). The packet gives the scout the problem statement, the constraints and where to search, and asks it to return an inventory, one row per existing mechanism that bears on the problem, with the columns where it is / what it does today / what is missing to put it to use / evidence / status (confirmed / inferred); the scout is done when its procedure is done and every mechanism it found has a row. No cards, no prompt draft.
Without a tool, do the step in the conversation and say what could not be checked.
The cheapest candidates are usually connecting what exists, not building.
Generate ~10 candidate interventions with judgment deferred, spread across the time axis (this afternoon / short-term (up to a month) / mid-term (1–3 months) / quarter-long bets (longer)) and across approaches, the unlikely ones included. Give each only:
- name
- one-line description: what it would change
Already exists — <where>, when the candidate builds on something step 2 found
Ranking, pruning, sizing and any recommendation wait for the user's picks (steps 4–5). Judging while generating keeps the first safe ideas and stops the list before the unexpected ones (Osborn: defer judgment, go for quantity).
- End the reply by asking which candidate numbers resonate; those are the picks. The picks show the user's priorities, which you do not know yet; a recommendation added after the list makes the pick for them.
Once the user names the picks, attach these fields to each picked candidate:
- expected impact: what improves, by how much
- effort size: S/M/L/XL
- key risks
- how to measure success: the sign that it moved the stated problem, not only that it was done
The picked numbers assemble into a copyable "proceed with these, in this order"; a missing pick is undecided.
Output
Layout: numbered candidates on a time axis (this afternoon → quarter-long bet).
Related
After the user picks interventions, hand off by what is still open:
| Still open | Skill |
| The form is in question | prototypes |
| Decisions only the user can make | interview |
| The plan for the work | tweakable-plan |
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Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

