Shapes turns action-first: action leads, steps numbered, state restated. Use for ADHD-friendly output. Do not use to trim tokens; use response-compression.
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。
Enforces validation and evidence before claiming work complete. Use before declaring implementation done, creating a PR, or submitting deliverables for review.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
"It looks correct" is not "I verified it works." Proof-of-work is the discipline of closing that gap: reproducible evidence before any claim that a task is done.
imbue:karpathy-principles)imbue:scope-guard)The "Proof of Work" methodology prevents premature completion claims by requiring technical verification before stating that a task is finished. For example, instead of assuming an LSP configuration functions after a restart, we verify that the server starts and that tools respond to queries. This approach confirms the solution works before the user attempts validation.
Before claiming completion, provide reproducible evidence of the solution's performance and address edge cases. All claims must be backed by actual command output captured in the current environment.
NO IMPLEMENTATION WITHOUT A FAILING TEST FIRST NO COMPLETION CLAIM WITHOUT EVIDENCE FIRST NO CODE WITHOUT UNDERSTANDING FIRST
The Iron Law prevents testing from becoming a perfunctory exercise. If an implementation is planned before tests are written, the RED phase fails to drive the design. Understand the technical rationale for an approach and its limitations before declaring it done. Before writing code, document evidence of the failure being addressed and confirm that tests are driving the implementation.
Verify the fundamentals of the implementation and the reasons for choosing it over alternatives. Identify where a solution might fail rather than stating it should always work. The TDD cycle follows these mandatory steps:
| Self-Check Question | If Answer Is Wrong | Action |
|---|---|---|
| Do I have documented evidence of failure/need? | No | STOP, document failure first |
| Am I testing pre-conceived implementation? | Yes | STOP, let test DRIVE design |
| Am I feeling design uncertainty? | No | STOP - uncertainty is GOOD |
| Did test drive implementation? | No | STOP - doing it backwards |
proof:iron-law-red: Failing test written before implementation.proof:iron-law-green: Minimal implementation passes test.proof:iron-law-refactor: Code improved without behavior change.proof:iron-law-coverage: Coverage gates passed (line, branch, and mutation).Confirm that work passes all line, branch, and mutation coverage gates. For detailed enforcement patterns, see iron-law-enforcement.md.
Apply this skill before stating that work is "done," "finished," or "ready." Use it before recommending solutions or stating that a configuration "should work." Stop if you find yourself assuming a configuration is correct without testing it or recommending a fix without first reproducing the problem. Red flags include thinking "this looks correct" without actual verification. If you cannot explain each line of a configuration or why a specific practice applies to the current context, the necessary validation steps have been skipped.
This holds wherever the work happened, and it binds hardest inside a Workflow script, because a script has no filesystem and no shell. Everything it knows, an agent told it. A stage that returns "the tests pass" is the same kind of statement as a session saying "should work," and it carries the same weight, which is none.
So a workflow may find, rank, structure and propose. The run that produces evidence happens where its output can be pasted. A workflow's return value feeds a gate. It is not one.
proof:problem-reproduced)Before proposing a solution, verify the current state. Use tools like
ps, echo, and cat to check running processes, environment
variables, and configuration files. Document the failure with command
output and error logs.
proof:solution-tested)Before claiming a solution works, execute it in the current environment. Capture the actual output and confirm that it matches expected behavior. Do not rely on assumed output.
proof:edge-cases-checked)Research known bugs and limitations related to the approach. Check GitHub issues, version compatibility, and official documentation to identify potential blockers or common pitfalls.
proof:evidence-captured)Use imbue:proof-of-work to document the commands executed, their
output, timestamps, and the conclusions drawn from each step.
proof:completion-proven)Define acceptance criteria and validate each item. If a blocker is identified, document the diagnosis with evidence and provide workaround options instead of claiming completion.
Use proof-of-work to validate improvement opportunities identified by
/update-plugins or /fix-workflow. Document the baseline metrics
(step count, failure rate, duration), test the proposed changes, and
capture the improved metrics to demonstrate quantitative impact.
Verify that the problem was reproduced with evidence and the solution was tested in the actual environment. Research known issues and consider edge cases. Capture evidence in a reproducible format and confirm that all acceptance criteria are met. The completion statement must detail the specific tests run and their results, citing evidence for each claim.
Before sending a completion message, confirm that you have run the recommended commands and captured their output. Verify that you have researched known issues and that the validation steps are reproducible by the user. Ensure you are proving rather than assuming.
imbue:karpathy-principles: the "Goal-Driven Execution" principle
wraps the Iron Law in a four-principle synthesis useful as a
pre-flight gatedocs/quality-gates.md#skill-level-quality-gate-composition for
the full gate-skill federation graphproof:iron-law-red, proof:iron-law-green) confirming
test-first execution.[E1]/[E2] evidence reference exists in the
response, backed by a real command invocation and its output.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
Shapes turns action-first: action leads, steps numbered, state restated. Use for ADHD-friendly output. Do not use to trim tokens; use response-compression.
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。
Inverts burden of proof for code additions. Use when reviewing PRs, planning refactors, or running unbloat to challenge every addition's necessity.
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。
Tracks per-agent token usage and flags waste in parallel dispatch. Use when evaluating parallel agent efficiency or after a multi-agent run.
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。
Coordinates Claude agent teams via filesystem protocol. Use when orchestrating parallel agents with task dependencies. Do not use for single-agent tasks.
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。
Evaluates API surface design, consistency, and exemplar alignment. Use when reviewing public API changes or before releasing a new API surface.
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。
Selects architecture paradigm via research before scaffolding. Use when architecture is undecided and the choice needs justification and documentation.
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。