SKILLS/adhd/SKILL.md
Parallel divergent ideation for coding agents. Spawns N isolated branches under different cognitive frames (regulator, biology, speedrunner, 10 year old, $0 budget), scores, prunes traps, and deepens the top survivors. The isolated parallel branches and the separated generator/critic phases are load-bearing. Costs about 10 LLM/Agent calls per run (5 to 10x a single answer) so invocation is gated. UNCONDITIONAL TRIGGER. When the user types "/adhd" or explicitly asks for "ADHD mode", "use the adhd skill", or "run ADHD on this", invoke the full loop without further judgment. Skip the pre-flight check. CONDITIONAL TRIGGER. For everything else, self-judge before invoking. Only proceed when ALL of these hold: (a) the answer space is OPEN-ENDED (multiple viable answers, no single canonical correct one), (b) the cost of the obvious answer being wrong is high (architecture decision, fuzzy bug with no known root cause, API or SDK surface design, naming for a public product, strategy, positioning, schema design, migration planning), and (c) the user has NOT used closed-phrasing words like "quick", "fast", "standard", "canonical", "textbook", "just", "one-line", "show me how to", "what is the syntax for". DO NOT INVOKE for: factual lookups, syntax help, bugs with a known root cause, anything where the right answer is one search query away, or questions phrased as closed with a clear single answer. When in doubt, do NOT auto-invoke. Prefer answering directly and offer the user "/adhd <problem>" as an explicit option if you think the wide search would help.
npx skillsauth add pinkpixel-dev/skills-collection-1 adhdInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Stop picking the textbook answer. The first three answers the model would give are the answers a senior engineer would give in thirty seconds. Correct. Forgettable. The interesting answers live past number three, in the awkward middle nobody walks into. This skill makes the model walk there.
This skill is expensive. About 10 Agent calls, 30 to 90 seconds wall clock, 5 to 10x a single answer. Do not pay that cost when a direct answer is better. Run this gate before Phase 1.
Step 1. Explicit invocation check.
If the user typed /adhd or explicitly asked for ADHD mode, "use the
adhd skill", or "run ADHD on this", SKIP the rest of this section and go
straight to Phase 1. The user opted in. Do not second-guess.
Step 2. Self-judge (only if Step 1 did not match).
Ask yourself three questions. If the answer to any is no, ABORT.
If all three checks pass, proceed to Phase 1.
If any fails, ABORT and answer the question directly. Optionally append
one sentence: "If you want a wider exploration under parallel cognitive
frames with explicit trap detection, run /adhd <your problem>."
Strong signals to invoke:
/adhd <problem> or "use ADHD mode" (unconditional)Strong signals to NOT invoke:
Two strict phases. Mixing them kills idea quality, because the critic strangles the generator.
For the problem P:
Pick 5 cognitive frames from the table below. Bias toward engineering tags when the problem is code-shaped. Always include at least one wild frame to keep range.
Spawn 5 parallel Agent/Task tool calls. One per frame. Each Agent gets only:
The exact instruction to give each Agent:
You are in DIVERGENT mode. You are a generator, not a critic. Generate 6 short distinct ideas under this frame. Each idea is one phrase or one sentence. Do not evaluate. Do not rank. Do not hedge. The first three obvious answers everyone would give are banned. Push past them into the awkward middle. Output a JSON array only. No prose before or after.
[{"text": "...", "rationale": "..."}, ...]
Critical invariant. The Agent calls must be parallel and isolated. Do NOT serialize them. Do NOT pass one branch's output as context to another. Branches that see each other anchor each other and the whole method collapses to a wider single thought.
After all branches return:
Score. Rate each idea on three axes 0 to 10: novelty (distance from the obvious default), viability (could it actually ship), fit (does it address the stated problem). For any idea that looks attractive but is a trap (hidden cost, false economy, will not scale, premature abstraction), flag it with a one-line reason.
Cluster. Group ideas into 3 to 6 clusters by their underlying angle, not by surface keywords. Label clusters by angle: "remove the server plays", "cache-shaped plays", "batched-window plays", "race-multiple- backends plays".
Deepen the top 3. Rank by weighted score (novelty 0.35 + viability 0.40 + fit 0.25), exclude traps, take top 3. For each, spawn one Agent call that produces:
Deepen Agent instruction:
You are in FOCUS mode. Take one promising idea and connect dots. Sketch how it would actually work in 4 to 8 sentences. Name the load-bearing risk. Name the first concrete step a coder would take. Then generate 3 to 5 sub-ideas that branch off (variations, combinations with other domains, things this unlocks). Output JSON only.
Pick 5 per run.
| Frame | Vantage prompt | Tags | |---|---|---| | hardware engineer | You think in latency, memory layout, and physical constraints. Re-ask this as a hardware/firmware problem. What does the bus topology, cache, timing budget tell you? | code, wild | | regulator | You audit systems for compliance and failure modes. What must be provable, traceable, or refusable here? | design, general | | 10-year-old | You are a curious 10 year old who has never seen software. Describe naive but unencumbered approaches. Ignore convention. | general, wild | | competitor trying to break it | You are a hostile competitor or attacker. Generate approaches that exploit, fail, or sabotage the obvious solution. Then invert into ideas. | code, design | | biology | Transplant a mechanism from biology (immune systems, neural plasticity, cell signaling, evolution, gut flora). Force-fit it onto this engineering problem. | code, wild | | logistics | Steal mechanisms from logistics: queues, batching, just-in-time, hub-and-spoke, returns, last-mile. Apply them literally. | code, design | | game design | Approach this as a game designer. What are the loops, rewards, friction, save-states, speedrun tricks? Treat the user as a player. | design, general | | markets | Treat the problem as a market. Buyers, sellers, market-makers. What does an auction, a futures contract, a clearing house look like here? | design, wild | | inversion | Ask the OPPOSITE question. If goal is X, brainstorm how to guarantee NOT X. Then negate each answer back. | code, design, general | | extreme: $0 budget, 1 hour | No money, no team, one hour. What is the crudest version that still does the load-bearing thing? | code, general | | extreme: infinite budget, 10 years | Infinite compute, infinite engineers, a decade. What is the maximalist version? | design, wild | | remove the load-bearing assumption | Name the thing everyone treats as fixed (framework, database, request-response model, network). Imagine it is gone. What is possible? | code, design, wild | | speedrunner | You are a speedrunner. Find glitches, skips, out-of-bounds tricks, frame-perfect shortcuts. What is the abusive-but-legal path? | code, wild | | ant colony | No central planner. Many dumb agents, local rules, pheromone trails. How does the problem solve itself emergently? | code, wild | | 3am on-call | You are the on-call engineer woken at 3am when this breaks. What design would let you not get paged? | code, design |
For code-shaped problems: pick 4 frames tagged code or design, plus 1
tagged wild. For open product or strategy problems: a mix from all tags.
Vary the picks across sessions so the same problem produces different
candidate sets when re-run.
After Phase 2, render in this order. Do not collapse it into a wall of prose. The structure is the point.
[N7 V8 F9] next to each.These are how this skill goes wrong. Watch for them.
5 diverge + 1 score + 1 cluster + 3 deepen ≈ 10 Agent calls per run. About 5 to 10x a single-shot answer. Not for every keystroke. For decision points where the cost of the obvious answer is high.
There is a Node/TS implementation that does the same loop with structured JSON parsing, score weighting, and a CLI. Use it when running outside Claude Code or in batch.
npm install -g adhd-agent
adhd "your problem here"
Code, paper, evals, and contributing guide at https://github.com/UditAkhourii/adhd. The skill above gives you the same loop inside Claude with no install required.
This skill operationalises a written spec on divergent ideation. The
original prose is preserved in SOURCE-SPEC.md for reference. The
implementation choices made here (parallel isolated Agent calls,
mechanical generator/critic split, frame-based branching) follow from
that spec.
testing
When the user wants a full ASO health audit, review their App Store listing quality, or diagnose why their app isn't ranking. Also use when the user mentions "ASO audit", "ASO score", "why am I not ranking", "listing review", or "optimize my app store page". For keyword-specific research, see keyword-research. For metadata writing, see metadata-optimization.
testing
Clarify requirements before implementing. Use when serious doubts arise.
tools
Complete reference and build guide for ASI:One (ASI1) — the AI platform by Fetch.ai built for agentic, Web3-native applications. Use this skill IMMEDIATELY and ALWAYS when the user mentions ASI1, ASI:One, Fetch.ai AI API, building with ASI1, integrating ASI:One, asking about ASI1 models, tool calling with ASI1, ASI1 image generation, ASI1 agentic LLM, Agentverse, uagents, Agent Chat Protocol, structured output with ASI1, or OpenAI-compatible wrappers for ASI1. Also trigger when the user says things like "use ASI1 instead of OpenAI", "build an app with ASI:One", "ASI1 API", or references docs.asi1.ai. This skill covers everything needed to build production apps - setup, all models, all API features, tool calling, image gen, agentic orchestration, structured data, session management, streaming, LangChain integration, uagents / Agent Chat Protocol, and TypeScript/Node.js patterns.
data-ai
When the user wants to analyze their own app's actual performance data from App Store Connect — real downloads, revenue, IAP, subscriptions, trials, or country breakdowns synced via Appeeky Connect. Use when the user asks about "my downloads", "my revenue", "how is my app performing", "ASC data", "sales and trends", "my subscription numbers", "App Store Connect metrics", or wants to compare periods or top markets. For third-party app estimates, see app-analytics. For subscription analytics depth, see monetization-strategy.