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Get Started Free →Design an auditable playbook when no narrower one fits: a large migration, an ambitious multi-part change, or work a human reviews after stepping away. Scales rigor to the task, runs a hypothesis loop, and logs decisions via show-me-your-work. Use for /figure-it-out, 'figure it out', a large migration, or when no narrower playbook applies.
.claude/skills/sediman-agent-figure-it-out/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-14 | ✗→✓ | ▲ Improved | 46% | 0% |
| case-11 | ✗→✓ | ▲ Improved | 69% | 0% |
| case-17 | ✗→✓ | ▲ Improved | -2% | 0% |
| case-21 | ✗→✓ | ▲ Improved | -19% | 0% |
| case-04 | ✓→✗ | ▼ Worse | 14% | 0% |
When the task matches no playbook, design one. The deliverable before any code is the workflow itself: a sequence of phases that scales rigor to the task, runs the scientific method, and leaves a decision trail a human can audit after stepping away. Bias toward more rigor. The cost of building the wrong thing dwarfs the cost of being careful.
Don't reinvent a playbook you already have. A focused single-unit task that matches Bug fix, Perf, Feature, Visual parity, Eval, or Multi-phase plan routes there. But a large or cross-cutting version of one (a migration across many call sites, an ambitious multi-part change), or work the user reviews after stepping away, belongs here even though a single-unit version would be a Feature. The rigor and the audit trail are the point.
Open a todolist whose first item is to read the Principles section of the poteto-mode skill. Then add the phases below as todos.
Ground first, then commit. Don't start the run until you can state:
Present the framing and tradeoffs before committing to a long run. Reversible work proceeds (the never-block-on-the-human principle skill), but a multi-hour run earns one checkpoint.
Decompose into atomic, independently-landable units. Sequence riskiest-unknown-first so option value stays high. Scaffold and verification come before features (the foundational-thinking principle skill).
Then put the design into motion. Add its steps to the todolist as concrete items, after the Phase C entry and before Phase D. Run each under the Phase C loop discipline, and weave the Phase D log through them, a row as each step lands, rather than saving the whole trail for the end.
Each unit is an experiment: state the hypothesis, make the smallest change, measure against the predicate on the real artifact, keep it if it advanced, revert it if it didn't.
Log the run via the show-me-your-work skill, one canonical TSV with a row per decision and per unit, evidence as links. figure-it-out's work is usually ambitious enough to commit the trail so the reviewer can read it in the PR; commit it when confidence has to be shown. Prefer evidence produced by committed scripts so a reviewer can re-run it. The trail plus the diff is what lets the human come back and trust the work.
Check the whole against the Phase A predicate on the real product, not just the harness. Encode any recurring correction as a gate, a lint rule, a check, or a script, so the win can't silently regress (the encode-lessons-in-structure principle skill).
Reply: the playbook you designed, the rigor level and why, the decision-trail path, what's verified against the predicate, and what's still open.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 39,827 | 5,632 | -86% | 1 | 1 | 0% | 5,893 | 1,394 | -76% | 0 | 0 | — |
case-02 | fail→fail | 36,734 | 4,695 | -87% | 1 | 1 | 0% | 5,894 | 1,309 | -78% | 0 | 0 | — |
case-03 | fail→fail | 32,208 | 8,276 | -74% | 1 | 1 | 0% | 5,416 | 1,556 | -71% | 0 | 0 | — |
case-04 | pass→fail | 6,200 | 7,478 | +21% | 1 | 1 | 0% | 1,290 | 1,473 | +14% | 0 | 0 | — |
case-05 | pass→fail | 11,845 | 6,653 | -44% | 1 | 1 | 0% | 2,285 | 1,506 | -34% | 0 | 0 | — |
case-06 | pass→fail | 12,684 | 34,158 | +169% | 1 | 1 | 0% | 1,650 | 2,763 | +67% | 0 | 0 | — |
case-07 | fail→fail | 14,513 | 32,394 | +123% | 1 | 1 | 0% | 2,096 | 6,246 | +198% | 0 | 0 | — |
case-08 | fail→fail | 21,993 | 32,153 | +46% | 1 | 1 | 0% | 2,955 | 6,020 | +104% | 0 | 0 | — |
case-14 | fail→pass | 18,331 | 18,435 | +1% | 1 | 1 | 0% | 2,694 | 3,940 | +46% | 0 | 0 | — |
case-09 | pass→pass | 11,975 | 6,572 | -45% | 1 | 1 | 0% | 1,770 | 2,078 | +17% | 0 | 0 | — |
case-10 | pass→pass | 17,017 | 29,706 | +75% | 1 | 1 | 0% | 2,483 | 5,125 | +106% | 0 | 0 | — |
case-11 | fail→pass | 17,108 | 22,136 | +29% | 1 | 1 | 0% | 2,525 | 4,264 | +69% | 0 | 0 | — |
case-12 | pass→pass | 9,508 | 5,352 | -44% | 1 | 1 | 0% | 1,358 | 1,858 | +37% | 0 | 0 | — |
case-13 | pass→pass | 14,944 | 29,259 | +96% | 1 | 1 | 0% | 2,114 | 5,296 | +151% | 0 | 0 | — |
case-15 | pass→pass | 10,424 | 8,459 | -19% | 1 | 1 | 0% | 1,533 | 2,182 | +42% | 0 | 0 | — |
case-16 | pass→pass | 14,567 | 25,097 | +72% | 1 | 1 | 0% | 2,070 | 4,216 | +104% | 0 | 0 | — |
case-17 | fail→pass | 18,961 | 12,060 | -36% | 1 | 1 | 0% | 2,765 | 2,723 | -2% | 0 | 0 | — |
case-18 | pass→pass | 17,165 | 22,401 | +31% | 1 | 1 | 0% | 2,507 | 4,184 | +67% | 0 | 0 | — |
case-19 | fail→fail | 13,161 | 4,762 | -64% | 1 | 1 | 0% | 1,908 | 1,790 | -6% | 0 | 0 | — |
case-20 | pass→fail | 21,000 | 28,749 | +37% | 1 | 1 | 0% | 3,088 | 4,408 | +43% | 0 | 0 | — |
case-21 | fail→pass | 11,752 | 2,125 | -82% | 1 | 1 | 0% | 1,676 | 1,361 | -19% | 0 | 0 | — |
case-22 | pass→pass | 14,848 | 15,718 | +6% | 1 | 1 | 0% | 2,184 | 3,377 | +55% | 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, and 16 counted toward the lift figure. The other 6 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of 0 percentage points is the difference between those two pass rates over the 16 comparable cases. 4 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.