Audit or rewrite AGENTS.md so it holds only lasting principles. Run it only when the user asks for it by name; never invoke it on your own.
日本語の概要は準備中です。原文の説明を表示しています。
Break large features into independently verifiable, human-reviewable slices under a feature directory in specs/. Use for risky or multi-step feature work that needs upfront questioning, API seams, browser-playable checkpoints, HTML visualizations, screenshot gates, staged implementation plans, recursive fog-of-war reslicing, or proactive research into reference implementations/best practices before slicing. Pairs with your project's verification harness and screenshot gates (the browser checkpoints), [refactor-clean](../refactor-clean/SKILL.md) (review the materialized spec so the plan describes one-owner architecture, not the feature bolted on), [screenshot-critique](../../visual/screenshot-critique/SKILL.md) and [compare-screenshots](../../visual/compare-screenshots/SKILL.md) (the visual gates), and a code-review pass (audit each slice before it lands).
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Turn a large feature into a ladder of small contracts. Each rung should be understandable to the human, testable by an agent, and useful before the whole feature is done.
Grill before planning. Ask one question at a time until you know the desired outcome, non-goals, review surface, sacred contracts, missing assets, and first useful playable checkpoint. Give your recommended answer with each question so the user can accept, reject, or edit it. Inspect the repo instead of asking questions the code can answer. Close the interview by asking whether the plan must carry backward compatibility or data migrations — the default is neither: hard cutovers, no compat shims, no migration scaffolding, no deploy-order dances. Only the user opting in puts them in the plan.
Slice at API seams. Each slice should behave like a tiny library where possible: named module boundary, typed inputs/outputs, deterministic fixtures, and tests at the seam. If a slice needs three unrelated systems booted before it can be checked, sharpen the seam.
Research the fog. When the feature depends on an unfamiliar domain, high-fidelity visual target, named reference, benchmark, external repo, library, or "how do people usually do this?" question, do targeted online research before finalizing slices. Prefer primary sources: official docs, source repos, papers, case studies, talks, and shipped examples. If a reference implementation exists, add a replication spike before translation or approximation.
Make progress visible. For visual or interactive work, every slice should produce something playable: a route, fixture page, harness, CLI probe, or HTML visualization the human can run, inspect, screenshot, and critique. Tests prove contracts; demos expose taste and intent.
Optimize feedback loops. Slice so the next useful question can be answered quickly. Prefer tiny runnable surfaces, hot-reloadable harnesses, sample fixtures, and self-contained workbenches over plans that require the whole feature to exist before anyone can learn from it. For asset-heavy work, plan an asset app/workbench where humans and artists can add samples, upload replacements, preview them live, and see validation failures fast.
Use the repo's natural shape. If the repo is a monorepo, plan apps and packages instead of forcing everything into the current app. Give each testable surface a first-class route or command; avoid piling new behavior behind opaque query flags when a small dedicated app would be clearer.
Do not block on missing inputs. If art, data, credentials, or external assets are missing, plan generated placeholders plus a replacement contract. The feature should advance with placeholders, while a separate handoff path explains exactly what the human or external partner must provide later.
Draft in parallel, then synthesize. For any multi-slice feature, don't trust one pass to find the right cut. Fan out a few independent drafts and merge the best into one plan (see the Workflow). Divergence is the point — so engineer it on two axes: give each draft a different bias (a lens it optimizes for) and, when more than one model family is available, a mix of models. Blind, differently-biased drafts surface slices, seams, and risks a lone plan misses — and where they independently agree, you know the cut is solid.
Recursively uncover fog of war. The first slice graph is a scouting pass, not proof the field is known. After drafting, inspect each high-risk slice as if it were its own feature. If it hides multiple variables, unknown external practice, unproven architecture, or "we'll figure it out during implementation," reslice that subset and repeat until every next slice has one question, one seam, one review surface, and one verdict.
One visual variable per slice. Visual slices fail when they ask one pass to match the final hero image. Split by the thing being judged: density, silhouette, colour, texture, lighting, fog, water placement, water material, label legibility, animation rhythm. Each slice gets a crop/mask and a verdict for that variable only. Whole-frame comparison belongs at compose/integration, after the variables have their own evidence.
Interview: keep asking until you can name the slices without hand-waving. Stop when remaining unknowns can safely be discovered by the first slice.
Research: inspect the repo and research unfamiliar external practice before drafting when the feature names a reference, library, technique, standard, visual target, or performance pattern. Capture the discovered source/repo/article/paper links in the spec and turn any exemplar into a reproduction spike before a porting slice. If prior experiments already established an accepted approach, apply From research to implementation below before drafting alternatives.
Draft in parallel: for a multi-slice feature, spawn at least three independent subagents to draft the whole plan — fresh context each, a git worktree apiece if they must run or build to validate, otherwise have them return the plan inline. Three is the floor, not the count: scale the pool with the feature's complexity, adding a drafter for each genuinely distinct approach or lens the problem supports. Give each the same brief from the interview and nothing else (never another draft) — but assign each a distinct bias so their divergence is structured, not accidental. The baseline trio:
Swap in or add lenses when the feature demands them (e.g. asset-pipeline bias, perf bias, migration-safety bias), but keep the biases orthogonal — grow the pool by adding a new lens, never by running the same lens twice. Also mix model families: if you're currently instructed to draft with codex, run at least one draft with claude — and vice versa — so the pool balances different models' blind spots, not just different prompts. Family means vendor (claude vs codex), not tier: every draft uses a state-of-the-art model; never diversify by dropping to a weaker tier of the same family. Each drafter: recon the real code and tests (measured facts, failed approaches, scope firewalls, greppable file/test names), then propose the slice graph, package/app boundaries, dependencies, API seams, playable deliverables, verification gates, and human review checkpoints. Skip the fan-out only for a genuinely single-slice problem.
Synthesize: read every draft and build the canonical plan yourself — don't anoint one. Take the strongest slicing, union the seams, risks, and firewalls each caught alone, and where drafts disagree pick the better-justified call and record the genuine alternative for the human. Where the drafts independently agree you're on firm ground; where they split is where to think hardest. When the feature has any visual surface, make screenshot-critique a standing verification gate in the README so every visual slice inherits it: the spec must tell the implementing agent to run an unbiased screenshot-critique as the last check on any visual shot before accepting it. Whenever a slice has something to compare its shot against — a prior look it changes, or a reference/inspiration image added for the feature — the spec must also name compare-screenshots as the gate that judges candidate-against-target: the telemetry and less-wrong verdict that screenshot-critique's single-shot eyes do not give.
Recursive fog audit: review the canonical graph slice by slice. For any slice with hidden variables, broad verbs ("make it realistic", "match the reference", "add the backend"), missing research, or more than one visual variable/API seam, run this same slicing logic on that slice as a sub-feature. Keep repeating until the next implementation slice can be accepted or rejected by one focused artifact. Record deferred variables as later slices, not prose inside the current slice. The exit test is the decision budget: a slice is fully specified when the implementing agent inherits decisions rather than making them — every freedom left open is either named as delegated in the slice file or the slice needs another pass.
Materialize: create specs/<feature>/ when the feature has more than
one slice or needs assets/visualizations.
Refactor-clean the plan: run refactor-clean over the materialized spec — the plan is architecture too, and it must describe the shape the codebase would want if designed today, not the old shape with the feature bolted on. Name each concept that should have one owner (projection, environment, data contract, renderer phase, state machine, test oracle) and confirm no slice introduces a parallel abstraction, duplicated concept, or compatibility layer that a later slice must delete. Any transitional scaffolding a slice genuinely needs must be named as a short-lived seam with an explicit removal condition and the slice that removes it — collapsed the instant its consumers migrate, never carried to the end by default. Encode the resulting single-owner invariants and the end-state ("reads as designed today, not tacked on") in the README so every implementing pass inherits them.
Scrollback audit: the conversation dies; the spec survives. Before calling the plan done, sweep the full conversation and every earlier planning artifact (maps, interview notes, drafts) for content that exists only there — decisions with their rationale, rejected alternatives with why they lost, mid-stream scope changes, user-supplied constraints and throwaway remarks that decided something. Each either lands in its owning spec file or is deliberately dropped; a scope change propagates to every spot that references it, not just where it landed. Then re-read each surviving pre-plan artifact the spec supersedes (a map's kickoff prompt, open-questions list, or proposed plan) and mark superseded sections with a pointer to the plan — stale instructions must not be able to misroute a fresh agent. Done when every conversation decision is findable in the spec and no surviving artifact contradicts the ladder.
Build slice by slice: leave each slice with a runnable artifact and verification before depending on it. Keep each artifact small enough to iterate on quickly. Keep the README's "Next Agent Prompt" written as the handoff text a future agent should read and follow.
Reslice when the work says so: if implementation hits a snag and the slice starts changing unrelated variables — or the choices ledger keeps filling from one slice — stop broadening the patch. Update the spec first: split the slice into smaller contracts, name the frozen inputs, move the extra visual variables to later slices, and rewrite the Next Agent Prompt to resume from the first new slice. Then continue. Reslicing is progress, not failure.
Freeze the accepted spike's runnable code, dependencies, configuration, inputs, prompts/skills, and evidence by immutable identity. Inspect what it actually does; option names and summaries are not proof of behavior.
Map proven behavior → evidence → owning slice → parity gate in the spec. Give every drafter this contract and the limits of the findings. Separate user-required differences from untested proposals; cleanup must not silently redesign the winner.
Plan an early production-entry-point comparison against the frozen reference with matched inputs and controlled responses. Check computed requests, decisions, and complete outputs; name permitted differences. Every preservation row needs an owner and a check. Run parity before expensive confirmation and retain the original quality gates.
Use specs/<feature>.md only for a small, single-slice problem. Large
features live in:
specs/<feature>/README.md — goal, context, slice graph, review map,
contracts, firewalls, known unknowns, and a "Next Agent Prompt" section with
the current status, next pickup point, global TODO checklist, and handoff
instructions for the next pass.specs/<feature>/slices/<NN>-<name>.md — one independently verifiable
slice per file.specs/<feature>/visualizations/*.html — roadmap diagrams, prototypes,
harness mockups, generated reports, contact sheets, or other
human-reviewable artifacts.specs/<feature>/assets/ — reference images, fixtures, captures, and other
inputs needed to judge the work.For visual work, keep feature-owned visual evidence in the spec folder: inspiration images, reference screenshots, archived baselines, comparison contact sheets, generated candidate captures, and critique artifacts. If those files start outside the spec folder, copy them into the spec folder when they become part of the feature's review context. Product snapshot folders may still hold the active regression baselines their harnesses own, but do not rely on those mutable outputs or external paths as the only record of what the feature was judged against.
Each slice file answers:
Every multi-slice spec README needs a "Next Agent Prompt" near the top. Write it in second person, as the prompt a future agent should read when they resume the feature. The README should not merely describe that it is live handoff state; the section itself must directly tell the next agent what to do next. It should include:
The point is that a fresh agent can open the README and know what to do next without reading the chat.
The feature plan is done when a fresh agent can start at slice 1 without the conversation, and the human can review the roadmap without reverse-engineering a wall of text.
Once the slices have all shipped, close-spec archives
the plan to specs/done/ and rewrites it from a build ladder into a durable
rationale record.
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Audit or rewrite AGENTS.md so it holds only lasting principles. Run it only when the user asks for it by name; never invoke it on your own.
日本語の概要は準備中です。原文の説明を表示しています。
Audit the choices an implementing agent made, not its diff — a pure decision audit that traces the session's history into a choices ledger, changes no code, and never blocks an unsupervised run. Working code still embeds architecture the user never chose; surface it because future work inherits it. Use when the user wants to review the decisions the AI made on their behalf, before merging or committing AI-implemented work, when integrating a delegated subagent's pass, or when a fix "works" but might be a point fix.
日本語の概要は準備中です。原文の説明を表示しています。
Audit and prioritize performance work through bounded-work and forward-progress checks. Use when asked to find performance issues, investigate freezes/500s/OOMs, review retries or polling for no-progress loops, rank a performance backlog, or implement the next simple performance fixes.
日本語の概要は準備中です。原文の説明を表示しています。
Audit whether tests earn their maintenance cost and which suite owns each contract. Use when pruning redundant tests, investigating implementation coupling or test-only production hooks, reviewing the value of proposed coverage, or auditing an entire subsystem. Use write-tests to implement the resulting test changes.
日本語の概要は準備中です。原文の説明を表示しています。
Run fast, progressive experiments to map tunable parameters and their effects. Use when optimizing code, prompts, configurations, or other artifacts against a goal or benchmark, or when a long research run needs a planned hypothesis queue, focused trials, and durable findings.
日本語の概要は準備中です。原文の説明を表示しています。
Use Claude Code as an independent `claude -p` subagent when the user explicitly asks for Claude, wants a second-agent opinion from Claude, or asks to delegate a well-scoped task to Claude. Supports selecting `--model` and thinking/effort level with defaults of `opus` and `high`.
日本語の概要は準備中です。原文の説明を表示しています。