「tests」の検索結果

Make an AI agent or automation reliable enough to trust — the tests, checks, and guardrails that catch its failures before they reach anything real. Use when asked how do I test my AI agent, make my automation reliable, my agent works sometimes, or how do I trust an AI workflow in production. Produces a map of where the agent can fail (bad input, hallucination, wrong tool call, edge cases, silent errors), the checks that catch each (validation, evals on real cases, human-in-the-loop gates, monitoring), a right-sized reliability plan scaled to the stakes, and a rollout that earns trust incrementally.

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Update tests when changing APIs — no exceptions

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。

Interactive workflow partner for creating, testing, and improving AI agent skills with waza. USE FOR: run my evals, check my skill, compare models, create eval suite, debug failing tests, is my skill ready, ship readiness, interpret results, improve score. DO NOT USE FOR: general coding, non-skill work, writing skill content (use skill-authoring), improving frontmatter only (use sensei).

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Behavioral guardrails for Codex coding work based on common user complaints. Use when Codex is asked to implement, modify, debug, review, test, or operate on a codebase and should avoid unsafe scope expansion, stale edits, fake completion, brittle edits, shallow debugging, superficial patch-on fixes, one-off special-case code, short-term design choices that hurt maintainability, blindly following incorrect user assumptions, poor dependency choices, generic product or UI output, over-mocked tests, noisy approvals, verbose status reports, or leaking internal reasoning into project artifacts or user-facing UI.

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Pre-launch and pre-commit audit for vibe coding projects. Use when asked to check whether a project is ready to ship, deploy, merge, or commit, especially for common AI-built app mistakes: broken project structure, committed secrets or cache files, environment variable hygiene, database migrations, ORM/schema drift, unsafe raw SQL, unused legacy code, dead routes/components, weak auth, missing tests, build failures, and deployment footguns.

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Scan application source, agent skills, and MCP components; run penetration tests against scoped web test deployments, validate findings, and configure scan gates. Use oma-qa for broad quality reviews and domain skills for remediation.

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Bug diagnosis and fixing specialist - analyzes errors, identifies root causes, provides fixes, and writes regression tests. Use for bug, debug, error, crash, traceback, exception, and regression work.

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。

Restructure existing code while preserving observable behavior. Use for targeted technical debt or hotspot work with characterization tests.

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。

Diagnose a reproducible failure, fix its cause, and verify the regression. Use for crashes, incorrect behavior, and failing tests.

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。

Plan commits as reviewable work units. Trigger: implementation, commit splitting, chained PRs, or keeping tests and docs with code.

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。

Records visual proof while testing UI behavior — the agent tests the app hands-on via computer use while a screen recording with structured test/assertion annotations captures the session — then posts the video and a results summary to the requested destination. Use when changed runtime behavior needs observable verification, including headless environments (scripted screenshots and probes) and non-UI changes (measured numbers, output pairs).

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。

Change or repair code, config or services without weakening tests; report what ran and what did not. Use when: implementing a change or fixing a defect.

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 when you run tests

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Generate test scenarios, write the test file, and run it for a specific source file using the tailtest R1-R15 rule layer. When the agent needs to (1) cover a file the Stop hook skipped, (2) regenerate tests after refactoring, (3) test a legacy file Codex did not modify this session, or (4) explicitly run tailtest on a named file.

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Rules as hypotheses: falsifiable tests, confidence updates, graduate or kill. Triggers: experiment, hypothesis, prove, test rule, validate methodology, evidence.

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 when you add tests for Julia packages

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Execute a single Todo task through In Progress to Review, meeting every acceptance criterion with tests and vibe-lint checks. Refuses Planning-status tasks. Invoked as /agiflow:run-task <task>. Uses get_task, update_task, create_task_comment.

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Audit an existing Comet expression for correctness and test coverage. Studies the Spark implementation across versions 3.4.3, 3.5.8, 4.0.1, and 4.1.1, reviews the Comet and DataFusion implementations, identifies missing test coverage, and offers to implement additional tests.

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Use when wiring an existing DataFusion or datafusion-spark function into Comet for a Spark expression. Identifies the right wiring pattern (one-line passthrough, datafusion-spark UDF registration, or custom serde with input massaging / restrictions), applies the Scala serde, registers the UDF in jni_api when needed, and adds SQL file tests. Assumes the function already exists upstream — if not, switch to `implement-comet-expression`.

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Review generated or changed production code before it ships, using Clean Code, SOLID, DRY, KISS, YAGNI, and LLM-specific failure-mode checks in any programming language. Best used reactively after an agent writes, edits, refactors, or fixes code, before presenting, committing, or merging the result. Use when the user asks "review this PR", "is this safe to merge?", "make this cleaner", "audit this code", "refactor this", "fix this bug", or after a coding agent produced implementation code. Can also guide writing when explicitly invoked before a risky edit. Invoke it on your own initiative the moment you finish writing, editing, or refactoring non-trivial production code, before presenting or committing — don't wait to be asked. DO NOT USE for factual/conceptual questions, CI/tooling config, git workflow, running/debugging tests, pure architecture discussion, prose writing, data analysis, or test-code review (use test-guard).

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 when designing or auditing AISTATS experiments, simulations, baselines, statistical tests, uncertainty estimates, ablations, random seeds, hyperparameters, compute, dataset handling, and claim-to-evidence fit, with emphasis on experiments that validate theorems rather than chase leaderboards.

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Use when deciding whether a macro paper fits the American Economic Journal: Macroeconomics (AEJ: Macro) rather than AER, J. Monetary Economics, RED, or AEJ: Applied. Tests the broad-interest macro bar and venue fit; it does not write the paper or fix identification.

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Use when the question is what makes the result tight or what the data identify for an American Economic Journal: Microeconomics (AEJ: Micro) manuscript — covering both (a) structural/empirical-IO and experimental identification and (b) for pure theory, which assumptions drive the result and how robust the mechanism is. Stress-tests credibility; it does not build the model (see aejmic-theory-model).

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 when the identification argument is the bottleneck for an American Economic Review: Insights (AER: Insights) short-format manuscript — causal identification in an empirical design, parameter identification in a structural model, or treatment-effect identification in an experiment. Stress-tests the strategy so it is clean enough to defend in a few pages, before exhibits are finalized.

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。