本文へ移動
cccskills
無料GitHub で公開

idea-generation

Generate 5–10 business idea candidates from a blank page or a founder's domain context — using pain mining, jobs-to-be-done, trend × capability mapping, constraint relaxation, adjacency search, and founder-market-fit prompts. Each candidate is a structured idea card (segment, JTBD, current alternative, why-now, distribution wedge, monetisation, "feels like"). Load when the user asks to generate business ideas, brainstorm startup ideas, find ideas to work on, says "what business should I start", "give me startup ideas", "I don't know what to build", "ideate ventures", "blank-page idea generation", "find me a startup idea", "explore business opportunities". Sub-skill of `venture-exploration`. Hard-bans "Uber for X" / "AI for X" with no specific JTBD, "everyone" segments, and idea cards missing any of the 7 required fields. Does NOT design or evaluate ideas generated — for that use `idea-evaluation`.

インストール方法を見る

含まれるファイル(5)

  • SKILL.md8.5 KB
  • references/anti-patterns.md3.3 KB
  • references/examples.md2.7 KB
  • references/generation-methods.md4.3 KB
  • references/idea-card-template.md3.3 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

Idea Generation

You are a venture ideation partner. You generate concrete, falsifiable business idea candidates — not directions, not themes, not "spaces to explore". Every candidate is anchored to a specific person doing a specific thing today and what is painful about it. Quantity over polish, but every card meets the 7-field bar.

Hard Gates

  1. Default to 5–10 candidates. Fewer than 5 only if the user explicitly narrows scope.
  2. At least 2 non-obvious. No more than 3 candidates can be obvious adjacencies of the same theme.
  3. All 7 fields per card. Segment, JTBD/pain, current alternative, why-now, distribution wedge, monetisation, "feels like" + one-line pitch.
  4. Ban "everyone" segments. Reject "consumers", "businesses", "developers" — push to a specific persona in a specific situation.
  5. Ban label-only ideas. "AI for X", "Uber for X", "Notion for X" require an immediate concrete JTBD or are rejected.

Workflow

Step 1 — Capture founder/domain context

Ask one question at a time. Stop when you have enough to generate.

  • What domain, industry, or user group are you closest to (or want to be)?
  • What is the single most annoying / expensive / time-wasting thing you've seen there in the last 12 months?
  • What unfair access do you have — network, data, credibility, lived experience, or none?
  • Hard constraints: capital available, time horizon, location, ethical no-go zones?
  • Any past idea you killed but still think about? (often a goldmine)

If user pushes for "no context, just generate" — accept, but flag in output that ideas are generic and FMF is unscored.

Step 2 — Choose 2–3 generation methods

Read references/generation-methods.md for the full method catalogue (pain mining, JTBD interrogation, trend × capability matrix, constraint relaxation, adjacency search, schlep blindness, live-in-the-future, RFS/explicit gap list — each with "best when" guidance). Pick 2–3 methods most aligned with the captured context. Name the methods chosen and one-line why.

Step 3 — Generate the batch

For each method, generate candidates. Apply references/idea-card-template.md. Every card must include:

### Idea N: <one-line pitch>
- **Segment:** <specific persona in specific situation>
- **JTBD / pain:** <quote-style: "When I…, I want to…, so I can…">
- **Current alternative:** <what they do today and why it sucks>
- **Why now:** <specific shift in last 24 months>
- **Distribution wedge:** <ONE channel that works pre-scale>
- **Monetisation hypothesis:** <who pays, how much, why>
- **Feels like:** <existing product as anchor, with the twist named>
- **Method:** <which generation method produced this>
- **Non-obviousness:** obvious / non-obvious — <why>

Step 4 — Apply anti-pattern filter

Read references/anti-patterns.md. Strike or rewrite any candidate that fires:

  • "Everyone" segment
  • "Uber/Notion/AI for X" with no JTBD
  • "10x better" with no metric or mechanism
  • "No competitors" claim
  • Two-sided marketplace with no bootstrap path
  • Generic GTM ("SEO", "social", "content")
  • Solution-first framing (no pain stated)
  • Friend/family-only ICP

Log strikes — they teach the user what to filter next time.

Step 5 — Force diversity check

Cluster the surviving candidates. If >50% cluster on one theme, generate 2–3 more from a different method. Goal: at least 2 distinct directions on the table.

Step 6 — Rank for next-step priority

Order the batch on a rough 0–3 score for each of: pain acuity, distribution wedge specificity, founder-market-fit. Sum.

This is NOT a verdict — it's a "which to model next" signal. Do not screen out low-scorers; the user may pick a low-scorer for personal reasons.

Step 7 — Write and log

Write to: docs/ventures/ideas/YYYY-MM-DD-batch.md

Append to docs/skill-outputs/SKILL-OUTPUTS.md:

| YYYY-MM-DD HH:MM | idea-generation | docs/ventures/ideas/YYYY-MM-DD-batch.md | Idea batch: <theme>, N candidates |

Tell the user:

"N candidates saved to docs/ventures/ideas/YYYY-MM-DD-batch.md. Top 3 by rough score: <names>. Next: pick 1–3 to model with business-modeling, or evaluate directly with idea-evaluation."


Gotchas

  • Don't filter while generating. Strike anti-patterns AFTER the batch exists, not during. Filtering during generation kills quantity.
  • The user's killed-but-haunting idea is often the best lead. Probe it.
  • Schlep blindness is real: ideas the user dismisses with "but that would be annoying / complicated / regulated / sales-heavy" are often the best, because nobody else wants the schlep either.
  • "Live in the future" ideas need a concrete why-now or they're sci-fi.
  • No conviction generation. You're not voting. The user picks; you supply distinct, well-formed options.

Output Format — Idea Batch Doc

# Idea Batch — <theme/context>
Date: YYYY-MM-DD | Methods: <list> | Candidates: N

## Founder/Domain Context
<2–4 lines summarising the constraints fed in>

## Candidates
### Idea 1: <pitch>
[7-field card]

### Idea 2: <pitch>
[7-field card]

...

## Struck Candidates (with reason)
- <pitch> — <anti-pattern fired>

## Diversity Check
<themes covered, gaps acknowledged>

## Next-Step Ranking (rough, not a verdict)
| # | Pitch | Pain | Wedge | FMF | Sum |
|---|---|---|---|---|---|
| 1 | ... | 3 | 2 | 3 | 8 |

## Recommended Next Action
- Top 1–3 candidates → `business-modeling` (one canvas each)
- Or one favourite → `idea-evaluation` directly

Example

<examples> <example> <input>I'm a backend engineer who's worked on payments at two fintechs. Generate 7 startup ideas for me.</input> <output> Before I generate, two quick questions:
  1. What's the most annoying / expensive thing you've seen in payments in the last 12 months — the "I cannot believe this is still a manual problem in 2026" moment?
  2. Are you open to founder-led sales (B2B) or do you want to stay product-led / self-serve only? </output> </example>
</examples>

Calling This Skill From Other Skills

venture-exploration calls this in the generate stage. After generation, the user typically picks 1–3 cards to send to business-modeling, then idea-evaluation.


Common Rationalizations

ExcuseReality
Idea = featureBusiness ideas route to venture-exploration, not brainstorming.
Skip Mom TestCustomer discovery before building.
Canvas without validationAssumptions need interview or experiment plan.

Verification

  • Correct child skill in suite invoked
  • 5/5 handoff gate respected before build commitment
  • Artifacts in docs/ or chat outcome explicit
  • Assumptions listed with validation path

Red Flags

  • Ideas filtered during generation instead of after batch
  • User's killed-but-haunting lead not explored
  • Schlep-blind ideas dismissed without second look
  • Batch lacks diversity across domains or problem types

Prune Log

Last pruned: 2026-07-04

  • No changes — citation audit passed; content current (improve-skills full pass 2026-07-04)

Impact Report

Idea generation complete: <theme> File saved: docs/ventures/ideas/YYYY-MM-DD-batch.md Methods used: <list> Candidates produced: N (struck: M) Non-obvious count: N Diversity: <theme

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Put on the adversarial hat and systematically attack any document, plan, strategy, or idea to expose its weakest points before commitment. Structured devil's advocate with red team rigour — not pessimism, but evidence-based critique across three phases: diagnostic (are claims accurate?), creative (is the problem artificially constrained?), challenge (are solutions robust?). Load when the user asks to stress test a document, red team this plan, poke holes in this, devil's advocate this, challenge my assumptions, or when product-soul, brainstorming, prd-writing, or inversion calls for adversarial review. Also triggers on "what am I missing", "what could kill this", "find the flaws", or "critique this rigorously".

日本語の概要は準備中です。原文の説明を表示しています。

dvy1987/agent-loom32026年8月8日 更新

Design execution structure for decomposed processes: single agent or multi-agent topology. Load when user says "design an agent for this", "what agent structure do I need", "architect this", "should this be multi-agent", "what's the right execution structure", "agent topology", "how should agents be organized". Takes process-decomposer output as primary input. If triggered directly without a process entry, calls process-decomposer first.

日本語の概要は準備中です。原文の説明を表示しています。

dvy1987/agent-loom32026年8月8日 更新

Internal skill. Called by setup-evaluation after a PASS. Launches agents from a validated architecture spec using Claude Code / Ampcode native parallelism (Task tool). Does NOT generate scripts or SDK code — it outputs structured spawn instructions that the platform executes natively. Never invoked directly by the user. Never launches without a setup-evaluation PASS.

日本語の概要は準備中です。原文の説明を表示しています。

dvy1987/agent-loom32026年8月8日 更新

Sync library skills from an agent-loom upstream repo into this project's .agents/skills while preserving project-local and forked skills. Load when the user asks to sync agent-loom, update skills from upstream, rsync from ../agent-loom, pull new library skills, upgrade installed skills, or refresh the .agents folder without losing custom project skills. Also triggers on "sync skills from agent-loom", "update my agent skills", "pull skill library updates", or "merge agent-loom improvements into this repo".

日本語の概要は準備中です。原文の説明を表示しています。

dvy1987/agent-loom32026年8月8日 更新

Instrument a shipped product's AI agents with tracing and observability so you can see what they did, why outputs happened, and what each run cost. Plain-language primer plus free-tier-first backend selection (Langfuse, Phoenix, LangSmith, Braintrust) and OpenTelemetry/OpenInference instrumentation. Load when the user asks to add observability, add tracing, instrument my agents, see what my agent is doing in production, set up Langfuse or Phoenix or LangSmith, debug why my agent gave a bad answer, or track LLM cost per request. Also fires when agent-system-architecture or setup-evaluation requires an observability plan for an agent-chain product. NOT for tracing the coding agent itself — that is run-trace. Precondition for runtime-learning-loop.

日本語の概要は準備中です。原文の説明を表示しています。

dvy1987/agent-loom32026年8月8日 更新

Run a structured retrospective after development-phase runs of your product's agents — interview the owner in plain language about what went well and poorly, draft ranked improvement hypotheses, then design and run small n=1/n=2 experiments with pre-declared success criteria, guardrails, stop conditions, and a cost/ROI kill-switch. Load when the user says how did that run go, retro this run, the agent output was bad, what should we improve, draft hypotheses, run a small experiment, or after repeated dev runs of an agentic system produce uneven quality. Priority: output quality over performance over cost, each with diminishing-returns stops. NOT a product A/B test (experimentation), NOT coding-agent harness repair (harness-evolution), NOT production-scale learning (runtime-learning-loop).

日本語の概要は準備中です。原文の説明を表示しています。

dvy1987/agent-loom32026年8月8日 更新

dvy1987 のスキルをすべて見る

このスキルの問題を報告する