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Get Started Free →Generate diverse solution candidates with category-spanning ideation methods and rotation. Use when stuck on a design or fighting repetitive LLM output.
.claude/skills/athola-ideate/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 36% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 64% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 26% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 109% | 0% |
| case-11 | ✗→✓ | ▲ Improved | -16% | 0% |
Diversity is a selection problem, not a volume problem.
Generating ten ideas from one mental frame yields ten variations of the same idea. The fix is to ideate from different categories of method, then rotate methods across passes so the next round does not repeat the last. This is the documented lever against LLM mode collapse, not a brainstorming ritual.
tome:research)tome:triz)Structure the reasoning, not the output.
"The Price of Format" (arXiv 2505.18949) found that rigid output templates collapse output diversity, while reasoning scaffolds raise it. So each method hands you a reasoning prompt. Do not force the ideas into a fixed schema while generating; impose structure only when reporting the final set.
Seven methods that port to technical problem-solving, each tagged with an honest evidence grade (see Sources below):
| Method | Category | Evidence | |--------|----------|----------| | SCAMPER | transformation | weak | | SIT task unification | transformation | mixed | | Morphological analysis | decomposition | mixed | | SIT subtraction | decomposition | mixed | | Cross-domain analogy (TRIZ) | analogical | mixed | | Inversion (pre-mortem) | inversion | anecdotal | | Constraint provocation | perturbation | anecdotal |
The grades are deliberately conservative. Individual-method evidence is thin; the value is the diverse-selection-plus-rotation pattern, not any single method. For the structured analogical form, use Skill(tome:triz). For finding an implied-but-empty cell in a concept space, map the field and its hidden optimization axis first, then apply generation methods to fill the gap.
select_methods(n) from tome.channels.ideation returns methods from different categories by construction.
freely; do not normalize them into a schema yet.
select_methods(n, exclude=used_ids) orrotation_plan(passes, n_per_pass) so no method repeats until the catalog is exhausted.
score_idea (weighted criteria perSkill(leyline:evaluation-framework)). Novelty claimed without evidence is capped, so "novel" must be earned, not asserted.
score_idea(scores, novelty_evidence=False) applies weighted criteria (novelty 0.25, fit 0.20, feasibility 0.20, simplicity 0.15, reversibility 0.10, impact 0.10) and an anti-inflation rule: a novelty score of 8 or higher without supporting evidence is capped to 7. Pass novelty_evidence=True only when you have checked for prior art and found the idea genuinely new.
diversity.
purpose.
The diversity rationale and method evidence grades draw on:
1.6 to 2.1x diversity gains, training-free.
generation diversity. This is the basis for the reasoning-prompt-not-schema rule above.
chain-of-thought reduces fixation; diverse personas restore collective diversity.
literature underpins the per-method evidence grades.
analysis of 4M+ concepts found originality declined 31% from 1981-2016 as concept space expanded. Cognitive burden drives clustering near familiar terrain rather than the edge.
method in a distinct category (verifiable: the methods used span >= 3 categories from list_categories()).
exclude or rotation_plan so nomethod repeated before the catalog was exhausted.
score_idea, withany uncapped novelty backed by an explicit prior-art check.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-05 | pass→pass | 26,672 | 35,095 | +32% | 1 | 1 | 0% | 4,181 | 6,509 | +56% | 0 | 0 | — |
case-01 | fail→pass | 57,514 | 28,299 | -51% | 1 | 1 | 0% | 4,407 | 5,982 | +36% | 0 | 0 | — |
case-02 | fail→pass | 19,997 | 42,741 | +114% | 1 | 1 | 0% | 3,084 | 5,051 | +64% | 0 | 0 | — |
case-03 | fail→pass | 34,683 | 36,088 | +4% | 1 | 1 | 0% | 5,576 | 7,033 | +26% | 0 | 0 | — |
case-04 | pass→pass | 32,355 | 39,240 | +21% | 1 | 1 | 0% | 5,072 | 7,360 | +45% | 0 | 0 | — |
case-06 | fail→fail | 10,660 | 6,836 | -36% | 1 | 1 | 0% | 1,938 | 2,312 | +19% | 0 | 0 | — |
case-07 | fail→pass | 16,960 | 25,732 | +52% | 1 | 1 | 0% | 2,742 | 5,723 | +109% | 0 | 0 | — |
case-08 | pass→pass | 19,281 | 19,386 | +1% | 1 | 1 | 0% | 2,820 | 4,377 | +55% | 0 | 0 | — |
case-09 | pass→pass | 9,783 | 7,319 | -25% | 1 | 1 | 0% | 1,463 | 2,428 | +66% | 0 | 0 | — |
case-10 | pass→pass | 16,859 | 27,541 | +63% | 1 | 1 | 0% | 2,644 | 5,893 | +123% | 0 | 0 | — |
case-11 | fail→pass | 10,741 | 2,722 | -75% | 1 | 1 | 0% | 1,881 | 1,585 | -16% | 0 | 0 | — |
case-12 | pass→pass | 8,067 | 3,953 | -51% | 1 | 1 | 0% | 1,248 | 1,734 | +39% | 0 | 0 | — |
case-13 | fail→pass | 9,329 | 4,438 | -52% | 1 | 1 | 0% | 1,418 | 1,844 | +30% | 0 | 0 | — |
case-14 | fail→pass | 7,215 | 4,376 | -39% | 1 | 1 | 0% | 1,017 | 1,873 | +84% | 0 | 0 | — |
case-15 | fail→pass | 9,243 | 5,753 | -38% | 1 | 1 | 0% | 1,233 | 2,085 | +69% | 0 | 0 | — |
case-16 | pass→pass | 5,753 | 3,260 | -43% | 1 | 1 | 0% | 911 | 1,660 | +82% | 0 | 0 | — |
case-17 | pass→pass | 16,665 | 5,166 | -69% | 1 | 1 | 0% | 2,400 | 1,803 | -25% | 0 | 0 | — |
case-18 | pass→pass | 16,376 | 6,048 | -63% | 1 | 1 | 0% | 1,923 | 2,194 | +14% | 0 | 0 | — |
case-19 | fail→pass | 11,666 | 2,525 | -78% | 1 | 1 | 0% | 1,891 | 1,533 | -19% | 0 | 0 | — |
case-20 | fail→pass | 10,968 | 2,802 | -74% | 1 | 1 | 0% | 1,721 | 1,533 | -11% | 0 | 0 | — |
case-21 | pass→pass | 14,864 | 6,736 | -55% | 1 | 1 | 0% | 2,282 | 2,240 | -2% | 0 | 0 | — |
case-22 | pass→pass | 9,546 | 1,778 | -81% | 1 | 1 | 0% | 1,482 | 1,423 | -4% | 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. 22 cases were attempted. The headline lift of +45 percentage points is the difference between those two pass rates over the 22 comparable cases.
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.