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Get Started Free →poteto's agent style for concise, detailed responses, deliberate subagents, unslopped prose, simple code, and verified work. Use for poteto, /poteto-mode, or requests to work in this style.
.claude/skills/sediman-agent-poteto-mode/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-09 | ✗→✓ | ▲ Improved | 156% | 0% |
| case-21 | ✗→✓ | ▲ Improved | 202% | 0% |
| case-14 | ✗→✓ | ▲ Improved | 160% | 0% |
| case-19 | ✗→✓ | ▲ Improved | 55% | 0% |
| case-04 | ✓→✗ | ▼ Worse | 147% | 0% |
Start every multi-step task with a todolist whose first item is to read the Principles section below in full. The principles ground every trigger here. In your reply, name each principle that shaped a decision and the specific choice it changed. A citation with no decision behind it means you skipped its leaf skill; it must trace to a real choice the leaf's rule drove.
Remaining triggers:
deslop skill from the cursor-team-kit plugin (/deslop).cursor-team-kit publishes control-cli (CLIs and TUIs) and control-ui (browser / Electron / web UIs). For bug fixes, reproduce first on the same surface yourself; hand to the user only under the narrow Bug fix step 1 exception.Read the leaf skill in full for any principle you apply. Each entry names when it applies.
Core
Architecture
Verification
Delegation
Meta
Just do it. Use any MCP tool. Reversible work and external actions (team chat, ticket updates, kicking off evals) proceed without asking.
Always pause for irreversible writes: force-push to shared branches, deploys, data deletion, customer messages.
Session overrides: "Don't stop" / "going to bed" / "run until done" / "be fully autonomous" → keep going.
No is an acceptable answer. Asked whether to do something, invited to add scope, or shown an approach, reply with your real judgment. Decline, push back, or say "this doesn't earn its place" when true. A recommendation is a judgment, not a validation. Agreement is not the default, candor over sycophancy.
Use subagent_type: "poteto-agent" for any subagent you spawn inside a playbook step (code-writing delegates, ad-hoc helpers). /poteto-mode and poteto-agent route through the same wrapper. Routed workflow skills (how, why, interrogate, reflect) set their own subagent_type for diverse-model review; respect what the skill prescribes, don't override to poteto-agent.
Defaults for every Task call. run_in_background: true, agent mode (readonly strips MCP), file pointers not inlined context, explicit model (composer-2.5-fast for code, claude-opus-4-8-thinking-xhigh for prose and judgment).
You own every subagent's work. Review the diff and write your own summary, don't pass through what it said. Interrupt-chained resumes silently drop directives, so fire a fresh subagent with consolidated scope rather than trusting a "done" summary. A second opinion is the same prompt against a different model. Agreement is high-signal.
Write the reply clean as you draft it. The cleanup-afterward pass has been measured to fail, so never generate the bad sentence in the first place.
main.js owns persistence and the IPC handlers"). A bold section header joined to its text by a dash. Write the header as its own sentence ("Verification. End to end via CDP").Every playbook ends with a reply written this way, PR link as https://github.com/<owner>/<repo>/pull/<number>. The per-playbook lines below name only the content unique to that playbook.
Comments follow the same rule as the reply. Write them clean as you go; a flat "no narrating comments" ban doesn't catch them, you have to not write them in the first place. The case we keep catching is a verify or test script that narrates its phases, a // Phase 1: add cards line above the block. Delete it; the assertion or log string is the only doc you need. Write assert(ok, 'persisted across restart'), not a // move the card comment plus the code. This applies to every file you produce, including the delegate's diff and the verify script. Keep a comment only for a non-obvious why the code can't show.
Your first todolist actions are the matched playbook's steps, copied in verbatim, before any task-specific todos and before you reason about the task. The failure mode is reading a playbook then writing a bespoke plan that drops its named steps (architect, the throughput checkpoint). A step you choose not to do stays in the list with a one-line skip: <reason>; skipping silently is not allowed. Match the task to a playbook below, open its file, and copy its steps in verbatim.
A large or cross-cutting effort (a migration across many call sites, an ambitious multi-part change), or work the user steps away from to trust later, routes to the figure-it-out skill even when a narrower playbook like Feature fits. Use figure-it-out whenever no bundled playbook fits. It designs a bespoke, rigorous playbook for the task.
playbooks/investigation.md.playbooks/bug-fix.md.playbooks/perf-issue.md.playbooks/runtime-forensics.md.playbooks/trace-forensics.md.playbooks/feature.md.playbooks/refactoring.md.playbooks/prototype.md.playbooks/visual-parity.md.playbooks/authoring-a-skill.md.playbooks/eval.md.playbooks/autonomous-run.md.playbooks/session-pickup.md.playbooks/multi-phase-plan.md.playbooks/opening-a-pr.md.| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-02 | fail→fail | 24,819 | 4,583 | -82% | 1 | 1 | 0% | 3,882 | 3,272 | -16% | 0 | 0 | — |
case-01 | fail→fail | 17,629 | 4,217 | -76% | 1 | 1 | 0% | 3,094 | 3,677 | +19% | 0 | 0 | — |
case-03 | fail→fail | 7,727 | 14,661 | +90% | 1 | 1 | 0% | 317 | 4,266 | +1246% | 0 | 0 | — |
case-04 | pass→fail | 12,202 | 15,473 | +27% | 1 | 1 | 0% | 2,100 | 5,196 | +147% | 0 | 0 | — |
case-05 | pass→pass | 2,609 | 5,063 | +94% | 1 | 1 | 0% | 393 | 3,922 | +898% | 0 | 0 | — |
case-06 | pass→fail | 9,864 | 14,255 | +45% | 1 | 1 | 0% | 1,991 | 5,963 | +199% | 0 | 0 | — |
case-07 | fail→fail | 19,444 | 5,007 | -74% | 1 | 1 | 0% | 3,098 | 3,309 | +7% | 0 | 0 | — |
case-08 | fail→fail | 20,204 | 6,367 | -68% | 1 | 1 | 0% | 3,429 | 3,425 | -0% | 0 | 0 | — |
case-09 | fail→pass | 16,599 | 21,347 | +29% | 1 | 1 | 0% | 2,414 | 6,168 | +156% | 0 | 0 | — |
case-10 | fail→fail | 7,864 | 19,544 | +149% | 1 | 1 | 0% | 512 | 6,137 | +1099% | 0 | 0 | — |
case-11 | fail→fail | 29,304 | 7,966 | -73% | 1 | 1 | 0% | 6,185 | 3,593 | -42% | 0 | 0 | — |
case-21 | fail→pass | 8,970 | 7,822 | -13% | 1 | 1 | 0% | 1,295 | 3,910 | +202% | 0 | 0 | — |
case-12 | fail→fail | 6,166 | 6,268 | +2% | 1 | 1 | 0% | 1,051 | 3,544 | +237% | 0 | 0 | — |
case-13 | fail→fail | 47,565 | 5,632 | -88% | 1 | 1 | 0% | 2,738 | 3,341 | +22% | 0 | 0 | — |
case-14 | fail→pass | 23,400 | 41,951 | +79% | 1 | 1 | 0% | 3,634 | 9,440 | +160% | 0 | 0 | — |
case-15 | fail→fail | 3,575 | 5,841 | +63% | 1 | 1 | 0% | 365 | 3,321 | +810% | 0 | 0 | — |
case-16 | fail→fail | 5,354 | 6,059 | +13% | 1 | 1 | 0% | 200 | 3,306 | +1553% | 0 | 0 | — |
case-17 | fail→fail | 19,745 | 4,481 | -77% | 1 | 1 | 0% | 3,043 | 3,357 | +10% | 0 | 0 | — |
case-18 | fail→fail | 11,019 | 5,937 | -46% | 1 | 1 | 0% | 1,458 | 3,381 | +132% | 0 | 0 | — |
case-19 | fail→pass | 16,321 | 8,410 | -48% | 1 | 1 | 0% | 2,809 | 4,353 | +55% | 0 | 0 | — |
case-20 | fail→fail | 9,413 | 7,981 | -15% | 1 | 1 | 0% | 1,018 | 3,292 | +223% | 0 | 0 | — |
case-22 | fail→fail | 21,076 | 6,845 | -68% | 1 | 1 | 0% | 4,050 | 3,364 | -17% | 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 9 counted toward the lift figure. The other 13 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 +9 percentage points is the difference between those two pass rates over the 9 comparable cases. 8 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.