doughnut: Skill for Claude Code

.agents/skills/slice-plan-refinement/SKILL.md

slice-plan-refinement is a skill for Claude Code, Codex from nerds-odd-e/doughnut. It costs 63 tokens per session (1,122 once invoked), scanned A, original, MIT.

A planning tool that breaks an existing software task plan into smaller pieces sized for individual commits. It works on an existing plan rather than creating a new one.

In plain words
What is it for?
It helps split work by behavior and code structure, clarify integration steps, and make oversized implementation slices easier to execute.
Why use it?
It helps when a task is too large, unclear, or likely to take longer than expected before producing a working result.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: installed under .agents/ (shared by several agents).

This is nerds-odd-e/doughnut's own configuration. It tells Claude Code and Codex how to work on doughnut itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything doughnut configures →

Reuse

Borrowing it

Nothing to install: this file belongs to nerds-odd-e/doughnut. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/nerds-odd-e/doughnut/main/.agents/skills/slice-plan-refinement/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/nerds-odd-e/doughnut

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for slice-plan-refinement

README.md
[![agentmods](https://agentmods.dev/badge/skills/nerds-odd-e/doughnut/slice-plan-refinement.svg)](https://agentmods.dev/skills/nerds-odd-e/doughnut/slice-plan-refinement)
Your own site
<a href="https://agentmods.dev/skills/nerds-odd-e/doughnut/slice-plan-refinement"><img src="https://agentmods.dev/badge/skills/nerds-odd-e/doughnut/slice-plan-refinement.svg" alt="Measured on agentmods" height="20"></a>
Per session 63 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,122 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00063 $0.01122
Opus 5 $0.00032 $0.00561
Sonnet 5 $0.00013 $0.00224
Haiku 4.5 $0.00006 $0.00112

Measured today against content hash 754f2e2deefc, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

slice-plan-refinement scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.agents/skills/slice-plan-refinement/SKILL.md · 137 lines

How it starts

The opening of the file, as written. The whole thing — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Apply .cursor/rules/problem-decomposition.mdc and .cursor/rules/planning.mdc. Do not create another plan file or change the selected story outcome.

<input_gate> Require an existing PLAN under .planning/phases/ or .planning/quick/.

  • No PLAN yet → use slice-planning.
  • Story understanding must change → use story-refinement; use story-decomposition for parent-problem or candidate-ordering changes.
  • Existing slices are already clear, cohesive, single-proof-loop, meet the target, and have no unexplained hard-limit path → execute directly; refinement is optional. </input_gate>

<refinement_triggers> Refine when any remaining slice:

  • contains multiple independent post-conditions or proof loops;
  • requires several separable implementation beats before any green result;
  • hides preparation not tied to the immediate next Behavior;
  • has low sizing confidence because the execution path or integration boundary is unclear;
  • is likely to exceed the target or could plausibly exceed the hard limit, excluding a focused test whose runtime alone explains the duration;
  • has already exceeded the target without converging; or
  • has exceeded the hard limit without a stated exception. </refinement_triggers>

Classify each remaining slice:

Result Decision
Ready One Behavior/Structure gate, one proof loop, cohesive change, meets the target
Refine Same story, but the leaf violates its gate, has multiple beats, has low confidence, or could exceed the budget
Escalate Learning escalation requires story review

Route Escalate using the input gate above. Refine every Refine slice here.

For every replacement leaf:

  1. Keep one Behavior, or one Structure immediately before its Behavior.
  2. Keep one outside-in proof loop and a CI-safe commit boundary.
  3. Include implementation, focused verification, and slice-local cleanup in the sizing hypothesis.
  4. Preserve value/learning order and genuine prerequisites.
  5. Split again when the leaf still has separable beats or a plausible hard-limit path.

Do not split tests from the Behavior they prove, end a leaf on red, or create horizontal layer slices.

  1. Record elapsed time, completed evidence, failure/thrash point, and the sizing assumption that proved false.
  2. Confirm attempt-owned WIP was safely parked or reverted before editing the PLAN. Preserve developer changes; if ownership is unclear, stop for human judgment.
  3. Replace with smaller leaves only if Learning escalation allows.
  4. Refine later slices only when the same disproved assumption applies to them.
  5. Keep a stated exception when elapsed time came from one focused test or external wait that decomposition cannot reduce.
  • Do not create a new file or directory.
  • Preserve completed-slice history needed for resume.
  • Replace obsolete planned detail rather than appending a second competing breakdown.
  • Reconcile promise ownership and observations against the original leaves under planning.mdc's Proof decisions before declaring the revised PLAN ready.
  • Record only learnings that changed the refinement.
  • Do not implement, commit, or push unless the invoking workflow separately authorizes those actions.

Read the full file on GitHub · 137 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. today Changed · +3 lines · -5 tokens per session 754f2e2deefc
  2. yesterday First seen · 134 lines · 68 tokens per session scan A b22e79ac34c6

Subscribe to this mod's changes

slice-plan-refinement is a skill published in the GitHub repository nerds-odd-e/doughnut (49 stars, last pushed today), licensed MIT. It adds 63 tokens to every session and 1,122 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-05.

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