「verification」の検索結果

Build an evidence-backed working model of an existing codebase. Use when you must orient in a large or unfamiliar repository; explain architecture or a subsystem; trace a request, runtime, control, or data flow for a concrete question or bug; analyze a branch, commit, diff, or pull request for behavioral impact and blast radius; or assess AI-generated or changed code against requirements, contracts, invariants, tests, and runtime evidence. Default to read-only investigation. Do not trigger only because a coding task touches an existing repository; use when understanding or verification is the requested outcome or a substantial prerequisite.

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Use for complex coding requests that need a structured implementation plan with dependencies, checkpoints, and verification criteria.

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Use when implementation needs isolation from the current workspace; creates git worktrees with safe directory selection and baseline verification.

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Discover, compare, and compose existing open-source GitHub repositories for a user's goal, using GitHub MCP for live discovery and verification plus Context7 semantic retrieval over repository code and official documentation. Use when a user asks for GitHub repo recommendations, open-source alternatives, a multi-repository solution stack, project comparison, or help choosing an implementation to adopt.

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Audit a composer output against four hard checks before delivery — cite integrity, voice rules, word count, schema validity. Always invoked after the polisher (English) or humanizer-* (Arabic / Hebrew). Emits a VerificationReport Pydantic object; orchestrator branches on `overall_passed`. Target ≥0.9 score per run. Triggers automatically as the final composer sub-agent; not user-facing.

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Set up AI Runway on AKS — from bare cluster to running model. Covers cluster verification, controller install, GPU assessment, provider setup, and first deployment. WHEN: "setup AI Runway", "onboard AKS cluster", "install AI Runway", "airunway setup", "deploy model to AKS"...

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Runs a bounded spec-build-review development loop with explicit scope, stop conditions, and human approval gates for risky or ambiguous work.

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Use when a coding task should be driven end-to-end from issue intake through implementation, review, deployment, and acceptance verification with minimal human re-intervention.

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Detects AI-generated deepfake audio used in voice phishing (vishing) attacks by extracting spectral features (MFCC, spectral centroid, spectral contrast, zero-crossing rate) and classifying samples with machine learning models. Supports batch analysis of audio files, generates confidence scores, and produces forensic reports. Activates for requests involving deepfake voice detection, vishing investigation, AI-generated speech analysis, voice cloning detection, or audio authenticity verification.

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Configuring Google Cloud Identity-Aware Proxy (IAP) to enforce per-request identity verification for Compute Engine, App Engine, Cloud Run, and GKE services using access levels, context-aware policies, and programmatic access with service accounts.

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Run Tamanu pre-release verification checks: verify that hotfixes on the current release branch are merged forward to main, and that hotfixes from prior release branches are included in the current release. Use when the user wants to run "pre-release checks", verify hotfix integrity before cutting or shipping a Tamanu release, confirm a release/X.YY branch is safe to release, or check that no hotfix has been orphaned (left on neither main nor the current release). Handles Tamanu's train-release model (release/X.YY branches vs vX.YY.Z patch tags) and the cherry-pick model where the same logical fix has different SHAs on different branches. Purely git-based — no live database or Canopy access needed. Not for deciding which tests to run (see scope-tamanu-release-tests).

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Production-quality Bash scripts for Solana vanity generation, keypair verification, batch operations, dependency auditing, and test orchestration — with security-hardened patterns including file permissions, input validation, and cleanup traps.

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Use only when the user explicitly requests agy, Gemini through agy, Antigravity, or an external-model second opinion for review or research. Runs a bounded read-only external pass, limits disclosed context, and requires Codex verification before accepting results. 中文触发：明确要求 agy、通过 agy 调用 Gemini、Antigravity、外部模型复核或外部第二意见。

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Post-fix verification — re-scan the repo, gate on `--exploits weaponized --severity high`, recheck the specific CVE against the new installed version, write the verdict to `.vulnetix/memory.yaml`. Use when confirming a fix landed, validating a version bump did not introduce regressions, or producing a clean-scan attestation for compliance.

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Concrete remediation proposal with Safe Harbour confidence — version bump, inline as first-party code, patch, workaround, or advisory. Use when applying a fix for a triaged CVE, evaluating fix-type trade-offs across registry / source / distro patches, producing rollback-safe manifest edits with dry-run verification, or generating a CycloneDX SBOM after the fix lands.

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xp-harness の skill / subagent / instruction が意図どおり振る舞えているか (発火するか・あるべき振る舞いができているか) を transcript で事実確認する手順。skill / subagent / instruction を改修した後に動作検証したいとき、または本番の実運用セッションを「あるべき振る舞い」に照らして分析したいとき、「検証したい」「発火するか確かめたい」「このセッションを分析したい」と言われたときに発火させる。核は発火・振る舞いができたかの事実確認で、本番セッションでは自走の良し悪し (止まり方・判断の質など) も見る。

Use when the user starts a non-trivial coding/building task in Claude Code (e.g. "帮我做个 X", "help me build Y", "add feature Z", "搭一个 Z"). Layers an AI-native pedagogical coat over normal coding work — surfaces implicit assumptions in user intent, annotates non-trivial design decisions inline with brief concept-scaffolding for terms the user may not know, and replaces "should I run it?" handoffs with rigorous AI self-verification plus a clean list of what only the user can verify. Skip for pure Q&A, single-line edits, read-only investigation, or refactoring along an existing pattern.

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Run a targeted fresh-eyes blunder pass over code, specs, plans, reviews, closure gates, or skill edits. Use when asked to reread with fresh eyes, find obvious bugs, catch mistakes/oversights/omissions, check for embarrassing misses, or do a second independent pass before closure. Use it as the final falsification/check pass for another workflow, not as a substitute for implementation or verification.

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Turn 'should never happen' into 'cannot happen' by defining owned inductive invariants and enforcing them at parse/construct/API/DB/lock/txn boundaries with a verification signal. Use when prompts mention invariants, impossible states, validation sprawl, cache/index drift, idempotency/versioning, retries/duplicates/out-of-order events, race/linearization bugs, loop correctness, or hardening another workflow (for example /tw-fix) with invariant checks first.

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Load when executing or resuming an approved plan from `dev/active/<task>/` or `.worktrees/<task>`, including phase commits, verification, PR creation, and handoff; not for plan authorship or review.

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Guidance on writing high-quality requirements: the requirement quality characteristics and the EARS (Easy Approach to Requirements Syntax) patterns — ubiquitous, event-driven, state-driven, optional-feature, unwanted-behaviour — for unambiguous, singular, verifiable requirements, plus defect/anti-pattern review and verification & traceability. Use when authoring or reviewing requirements. Original synthesis citing EARS (Mavin et al.) and NASA public-domain guidance; not a reproduction of ISO/IEC/IEEE 29148 or the EARS paper.

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Knowledge base from the NASA Systems Engineering Handbook (NASA/SP-2016-6105 Rev 2). Use for NASA's systems-engineering process model — the SE Engine and 17 common technical processes, the program/project life cycle (Pre-Phase A through F), Key Decision Points and technical reviews (MCR/SRR/PDR/CDR/…), stakeholder expectations, technical requirements, design solution, product verification and validation, and crosscutting technical management (planning, risk, configuration, interfaces, data, assessment, decision analysis). Strong for aerospace/spaceflight project SE and lifecycle gating; NASA-specific terminology (KDPs, NPR 7123.1).

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Knowledge base from the DoD M&S VV&A Recommended Practices Guide (RPG web edition). Use for verification, validation, and accreditation of models and simulations — role responsibilities (user, developer, M&S PM, V&V agent, accreditation agent), fidelity, validation fundamentals, data V&V, risk-informed accreditation, T&E/V&V integration, referent development, and conceptual-model validation. Complements nasa-ms-7009 and T&E packs; does not replace DoDI 5000.61 or Service VV&A policies.

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Knowledge base from NASA-STD-7009B (Standard for Models and Simulations) with implementation depth from NASA-HDBK-7009B. Use for M&S criticality and programmatics, development evidence and capability assessment, verification/validation domains, uncertainty and sensitivity, use-phase results assessment, risk-informed decision reporting, and handbook life-cycle products. Covers STD-7009B (2024-03-05) + HDBK-7009B (2026-02-03) synthesized notes only; does not replace program-specific TA direction, NPR 7150.2 software requirements detail, or full PRA methodology.

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。