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Get Started Free →Проводит структурированный сеанс дивергенции вокруг конкретной продуктовой проблемы или возможности. Встроены SCAMPER (7 ракурсов), 5 Whys для поиска корневой причины, кросс-доменное вдохновение, ограничивающие инновации, обратный брейншторм и матрица Impact/Effort для отбора. На выходе — ≥10 идей с детально проработанным Top-3. User-invoked only — do NOT auto-trigger. Triggers on /pm-brainstorm, "идеи для продукта", "продуктовый брейншторм", "дивергенция идей", "How Might We", "SCAMPER", "produ
.claude/skills/serejaris-pm-brainstorm/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 11% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 32% | 0% |
| case-19 | ✗→✓ | ▲ Improved | 99% | 0% |
| case-21 | ✗→✓ | ▲ Improved | 105% | 0% |
| case-05 | ✓→✗ | ▼ Worse | 116% | 0% |
Part of the Personal Corp framework — running a one-person business through AI agents. Run a structured creative-divergence session on a specific product problem or opportunity. Not just listing ideas — challenges assumptions, borrows across domains, helps reach options you wouldn't have otherwise. Output is a screened idea list ready for next-step action.
| Field | Required | Notes | |---|---|---| | Topic | yes | Problem to solve or opportunity to explore — more specific = better | | Background | no | Persona, current data, known constraints | | Mode | no | Full divergence / single framework only; default full | | Constraints | no | Tech, time, resource limits |
> Good topic: "How might we make new users feel value within the first 5 minutes of first use?" > Bad topic: "How do we improve the product?" (too vague)
Before diverging, sharpen the problem.
Probing checklist:
Restate as a How Might We:
Run each lens once; aim for 1-2 ideas per lens.
| Lens | Core question | Direction | |---|---|---| | S — Substitute | What component can be substituted? | Different tech / interaction / entry point? | | C — Combine | Can we merge two features? | Merge steps / contexts / bundle features | | A — Adapt | What from elsewhere can we borrow? | Method from social apps? From productivity tools? | | M — Modify | What if we 10×'d or 0.1×'d this? | Minimal version? Maximal version? | | P — Put to other use | What other context could this serve? | Other personas? Other scenarios? | | E — Eliminate | What if we remove something? | Cut steps / fields / signup / friction? | | R — Rearrange | What if we reorder? | Try first / signup later? Reverse order? |
Total: 7-14 initial ideas.
From a user pain, descend to the root, then ideate from each layer.
Example:
Pain: new users churn on day 2
Rules:
Don't copy competitors — look at what they do, then think "what could we do that's different".
Cross-domain prompts:
Differentiation prompts:
Deliberately set constraints to spark creativity.
| Constraint | Setup | Stimulus | |---|---|---| | Time | "Only 1 week of dev time" | Forces minimum viable approach | | Tech | "Use only existing stack" | Reveals new uses of existing capabilities | | Cost | "Zero dev investment" | Ops moves, config changes, copy tweaks | | Extreme | "Change one line of code" | Find the highest-leverage single point | | Inversion | "Let users design it" | Switch perspective |
1-2 ideas per constraint. Tighter constraints often produce more elegant ideas.
First think "how could we make this worse", then invert each idea.
Steps:
Example:
| Make-worse | Inverted | |---|---| | Require 20 fields at signup | Phone-only signup, progressive profile fill | | Home is full of ads | Home shows only goal-relevant content | | Hide all help entries | Surface guidance proactively at confusion points |
> Reverse-brainstorm bypasses the "find the right answer" pressure — the divergence stage gets freer.
Drop all ideas into a 2×2:
| Quadrant | Impact | Effort | Strategy | Ideas | |---|---|---|---|---| | Quick Wins | High | Low | Validate first | {list} | | Big Bets | High | High | Worth investing, plan carefully | {list} | | Fill-ins | Low | Low | When idle | {list} | | Money Pits | Low | High | Drop | {list} |
Scoring:
markdown# Product Brainstorm **Topic:** {problem} **HMW:** How might we {restated as HMW} **Date:** {today} **Ideas generated:** {N} (kept after screening: {M}) ## Idea List | # | Idea | Pain solved | Source framework | Expected value | Effort | Quadrant | Validation | |---|---|---|---|---|---|---|---| | 1 | {desc} | {pain} | SCAMPER-Eliminate | High/Med/Low | High/Med/Low | Quick Win | {validation} | ## Top 3 Recommendations ### Idea 1: {name} - **Description:** {detail} - **Pain solved:** {specific pain} - **Expected impact:** {quantified, e.g. "+5-10% D1 retention"} - **Implementation path:** {brief tech / design approach} - **Validation:** {how to validate fast — A/B test, gradual rollout, etc.} - **Risk:** {risks and mitigation} ### Idea 2: {name} … ### Idea 3: {name} … ## Dropped Ideas (archived) - {idea X}: dropped because — {reason} - {idea Y}: dropped because — {reason} ## Next Actions - [ ] {action 1} - [ ] {action 2}
/pm-prd — Top idea → PRD/pm-prioritize — Idea list → formal ranking/pm-user-stories — Ideas → developable Stories/pm-feedback — Pain points from feedback → brainstorm fuel/pm-competitive — Validate differentiation level| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 50,149 | 34,823 | -31% | 1 | 1 | 0% | 6,581 | 7,318 | +11% | 0 | 0 | — |
case-02 | fail→pass | 42,421 | 39,389 | -7% | 1 | 1 | 0% | 5,989 | 7,877 | +32% | 0 | 0 | — |
case-03 | pass→pass | 35,495 | 37,911 | +7% | 1 | 1 | 0% | 5,266 | 8,091 | +54% | 0 | 0 | — |
case-04 | pass→pass | 48,142 | 40,812 | -15% | 1 | 1 | 0% | 8,235 | 7,683 | -7% | 0 | 0 | — |
case-05 | pass→fail | 20,571 | 34,535 | +68% | 1 | 1 | 0% | 3,482 | 7,538 | +116% | 0 | 0 | — |
case-06 | pass→pass | 20,668 | 32,698 | +58% | 1 | 1 | 0% | 3,123 | 5,990 | +92% | 0 | 0 | — |
case-07 | pass→pass | 19,801 | 23,172 | +17% | 1 | 1 | 0% | 2,947 | 5,925 | +101% | 0 | 0 | — |
case-08 | pass→fail | 23,845 | 28,501 | +20% | 1 | 1 | 0% | 3,595 | 6,683 | +86% | 0 | 0 | — |
case-09 | pass→pass | 18,413 | 24,225 | +32% | 1 | 1 | 0% | 2,523 | 6,320 | +150% | 0 | 0 | — |
case-10 | pass→pass | 19,898 | 28,581 | +44% | 1 | 1 | 0% | 3,185 | 6,712 | +111% | 0 | 0 | — |
case-11 | pass→pass | 18,825 | 28,002 | +49% | 1 | 1 | 0% | 3,213 | 6,669 | +108% | 0 | 0 | — |
case-12 | pass→pass | 13,281 | 9,293 | -30% | 1 | 1 | 0% | 1,896 | 3,532 | +86% | 0 | 0 | — |
case-13 | pass→pass | 16,274 | 30,479 | +87% | 1 | 1 | 0% | 2,371 | 7,006 | +195% | 0 | 0 | — |
case-14 | pass→pass | 15,789 | 24,366 | +54% | 1 | 1 | 0% | 2,390 | 6,132 | +157% | 0 | 0 | — |
case-15 | pass→pass | 15,647 | 13,741 | -12% | 1 | 1 | 0% | 2,451 | 4,433 | +81% | 0 | 0 | — |
case-16 | pass→pass | 15,800 | 24,175 | +53% | 1 | 1 | 0% | 2,425 | 5,735 | +136% | 0 | 0 | — |
case-17 | pass→pass | 21,809 | 42,219 | +94% | 1 | 1 | 0% | 3,052 | 7,799 | +156% | 0 | 0 | — |
case-18 | pass→pass | 22,175 | 27,011 | +22% | 1 | 1 | 0% | 3,411 | 6,570 | +93% | 0 | 0 | — |
case-19 | fail→pass | 12,781 | 10,873 | -15% | 1 | 1 | 0% | 1,936 | 3,858 | +99% | 0 | 0 | — |
case-20 | pass→pass | 14,724 | 17,342 | +18% | 1 | 1 | 0% | 2,038 | 4,838 | +137% | 0 | 0 | — |
case-21 | fail→pass | 15,666 | 8,828 | -44% | 1 | 1 | 0% | 1,713 | 3,518 | +105% | 0 | 0 | — |
case-22 | pass→pass | 11,236 | 25,225 | +125% | 1 | 1 | 0% | 1,873 | 6,775 | +262% | 0 | 0 | — |
case-23 | fail→fail | 19,499 | 19,385 | -1% | 1 | 1 | 0% | 3,024 | 5,187 | +72% | 0 | 0 | — |
case-24 | pass→pass | 12,906 | 13,204 | +2% | 1 | 1 | 0% | 1,263 | 4,152 | +229% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 24 cases were attempted. The headline lift of +8 percentage points is the difference between those two pass rates over the 24 comparable cases. 2 cases got worse with the skill loaded, and they are included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.