「triage」の検索結果

Converges an underspecified request into a typed IntentSpec before planning or acting. Use when the user asks to implement, add, change, refactor, fix, migrate, configure, handle, triage, sort out, look into or decide something and the request leaves decisions open (which objects, which approach, what happens on failure, which trade-off), contradicts itself, admits two readings, or rests on an approach its sources may rule out — in a codebase, a ticket queue, a research brief or a runbook — or when the user says "clarify the intent", "what do you need from me", or invokes intent-router. Looks up what its sources hold (code, history, decision records, a ticket log, an order record, the policy in force) before asking; asks only preference or irreversible questions, one at a time, with a recommended default; halts instead of guessing; checks the delivered work against the spec. Silent on a fully stated task its sources do not contradict. Not for explaining existing state ("what does X do", "why is Y slow").

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。

Build a preliminary, evidence-based EU AI Act readiness assessment across AI-system inventory, territorial scope, operator roles, prohibited-practice screening, risk classification, transparency, high-risk controls, general-purpose AI obligations, governance, and implementation milestones. Use when an organization needs to triage an AI use case, vendor, model, product, or portfolio for Regulation (EU) 2024/1689; prepare an AI inventory, gap register, implementation roadmap, or counsel briefing; assess provider, deployer, importer, distributor, product-manufacturer, authorised-representative, or GPAI-provider responsibilities; or re-check readiness after regulatory or product 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。

Send feedback or a bug report to the WOZCODE team. Bug reports are tagged for triage and auto-attach session context (session id, anonymous id, OS, arch, Node version).

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。

Think and work like an expert Entomologist. Use when a task calls for Entomologist judgment. Reasons from tagmata, Comstock-Needham venation, and tarsal formula through trap-guild sampling (Malaise, pitfall, pan, light), host–parasitoid ecology, ICZN vouchers and genitalia keys, BOLD/GBIF/COL/iNaturalist triage, IUCN invertebrate caveats, CITES/COSE permits, Taylor/GLMM on the correct EU, and EIL/ET with IRAC MoA rotation while treating teneral, dimorphic, and cryptic mis-IDs as first-class failure modes.

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。

Think and work like an expert Emergency Medicine Researcher. Use when a task calls for Emergency Medicine Researcher judgment. ED trial design expert for pragmatic and cluster RCTs, time-zero and immortal-time bias in acute cohorts, NIHSS/SOFA/qSOFA and ESI/CTAS triage, NEDS/NHAMCS registries, and CONSORT/SPIRIT reporting with Hawthorne and selection-bias failure modes.

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。

Think and work like an expert Computer Security Researcher. Use when a task calls for Computer Security Researcher judgment. Reasons from explicit threat models and CIA/STRIDE through AFL++/libFuzzer triage, ASan/KASAN oracles, ProVerif/Tamarin proofs, CVE/CWE/CAPEC taxonomies, CyberGym dual-execution benchmarks, Menlo/CVD ethics, and USENIX open-science artifact norms.

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。

Think and work like an expert Chemical Biologist. Use when a task calls for Chemical Biologist judgment. Reasons from chemical genetics, ABPP/TPP/CETSA chemoproteomics, and SGC/Portal probe criteria; deconvolves phenotypic hits with PAINS/aggregator triage, inactive analogs, and genetic epistasis while treating colloidal aggregation, probe promiscuity, and degrader DC50/Dmax tag artifacts as first-class failure modes.

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。

Provider-independent provenance, rights, consent, disclosure, and release governance for AI-generated media. Use when producing or reviewing generated images, video, audio, ads, avatars, product content, social clips, documentaries, localization, music, sound, or campaign assets that need source-rights checks, model/provider term checks, likeness or voice consent, trademark/logo review, copyrighted-character/style risk triage, music licensing, C2PA/content credentials, provenance ledgers, release notes, platform disclosures, jurisdiction/client caveats, or rights QA.

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。

Five-phase pipeline (triage → clarify → research → PRD → plan) that turns a raw idea into four linked files: clarifying questions, deep research, a PRD with non-goals and metrics, and a phased execution plan with mermaid user journey and kill criteria.

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 the konsistent CLI, review all reported violations, and fix them in the codebase. Use when the user wants to "fix konsistent violations", "resolve konsistent errors", "clean up konsistent diagnostics", "make konsistent pass", "address structural convention violations", or asks to run konsistent and act on the results. The skill is interactive: it requires explicit user decisions when many violations stem from the same rule (rule vs. code question), explicit user confirmation before modifying any code, and a triage step for non-trivial violations. For editing `konsistent.json`, this skill defers to the `konsistent-config` skill.

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。

Add fast, local, typed decisions to any project with Laya, an open-source non-generative decision model (pip install laya). It classifies, routes, scores and answers yes/no questions about a piece of text, returning calibrated probabilities in roughly 20-35 ms on a laptop GPU, with no LLM call and no data leaving the machine. Use this skill whenever the user wants to classify or route text, triage tickets or emails, detect spam, phishing, toxicity or intent, put a guardrail in front of an agent or LLM, score something against a rubric, or replace an LLM-based classifier to cut latency or cost. Also use it when they mention Laya, Jev, a "System 1 model", "typed decisions" or a "decision model", even if they never name Laya.

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。

Configure this repo for the engineering skills: set up its issue tracker, triage label vocabulary, and domain doc layout. Run once before first use of the other engineering skills.

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。

Design and execute structured exploratory testing sessions. Covers Session-Based Test Management (SBTM), charter writing, heuristic-based exploration (HICCUPS, FEW HICCUPS), bug discovery patterns, note-taking templates, and conversion of findings to automated tests. Use when: "exploratory testing," "SBTM," "manual testing," "bug hunting," "test charter," "heuristic testing." Not for: an AI browser agent autonomously exploring the app from a natural-language goal — use agentic-browser-testing. Not for: testing your product's own AI/LLM features — use ai-system-testing. Related: test-planning, ai-bug-triage, risk-based-testing, agentic-browser-testing.

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。

Build and visualize QA dashboards and reports with Allure Report, Grafana, and ReportPortal. Covers test execution visualization, stakeholder-facing quality reports, trend/flakiness panels, release-readiness gates, alerting, and CI integration for automated report generation. Use when: "test dashboard," "Allure," "test report," "quality dashboard," "Grafana," "ReportPortal," "test results visualization." Not for: defining which KPIs to measure or how to interpret them — use qa-metrics (this skill builds the panels; qa-metrics decides what they should show). Related: qa-metrics, ci-cd-integration, ai-bug-triage.

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。

Test AI/LLM features that ship in your product. Covers prompt regression testing, response quality evaluation, tool-call validation, hallucination and RAG grounding checks, nondeterministic-output strategies, red-team/safety scans, eval frameworks, and agent-as-target injection (indirect injection via tool output / RAG / scan reports, self-propagating payloads, data exfiltration via an agent) plus a bundled detector for untrusted content. Use when: "test our LLM feature," "prompt regression test," "eval framework," "hallucination test," "RAG grounding," "nondeterministic output," "AI feature testing," "red-team our chatbot," "indirect prompt injection," "agent reading untrusted tool output," "production AI quality." Not for: using AI to generate your own test code — use ai-test-generation. Not for: classifying CI failures with AI — use ai-bug-triage. Not for: EU AI Act / GDPR conformity of an AI feature — use compliance-testing. Not for: canary/flag rollout of an AI feature — use testing-in-production. Related: ai-test-generation, ai-qa-review, api-testing, compliance-testing, security-testing, risk-based-testing, test-data-management.

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。

Review and assess every issue in the TMDb project board's Backlog column — re-verify each against current main with a read-only fan-out, close the mechanically dead, promote the actionable to Ready with priority, size and execution order, and leave the rest in Backlog naming the decision they need. Use to groom the backlog, before planning a release, or on a schedule. Runs headless: it decides for itself within the rules below.

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。

Triage Meetings/_inbox/ — move each file to the correct subfolder by finding adjacent/similar notes via qmd, applying learned routing conventions from memory, and confirming with the user.

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。

Closes a large backlog of findings (a gap analysis, an audit, or 10+ bugs) in prioritized waves. Loops through triage, dependency analysis, parallel fix streams, a test gate, and re-audit until the target is met. Use after a gap analysis, audit, or review produces 10+ findings, or to burn down a large bug or tech-debt backlog.

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。

Analyze GitHub and Discord signals to prioritize maintainer attention. Use when asked about priorities, what's hot, what needs attention, or project status.

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 CI failures and stabilize pipelines. Use when a mid-level developer needs to resolve flaky or failing builds.

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。

Triage the deferred-work ledger for the bmad-loop orchestrator: verify every selected open entry against the actual codebase and return a machine-readable partition (bundles, already-resolved, blocked, skip, human decisions). Also migrates legacy pre-DW-format ledgers when invoked with --migrate. Automation-only — invoked by bmad-loop sweep runs, not by humans.

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。

Periodic multi-reviewer sweep of the whole codebase — surfaces a triaged checkpoint report.

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。

The parallel fan-out primitive. Routed to by any skill or command that splits work across independent units and dispatches an agent per unit — subagent-driven-development, /sprint, parallel /review. It owns the dispatch/collect/funnel discipline: bound concurrency, isolate units, collect every result, dedupe overlap, and funnel through finding-triage then the read-only verdict-aggregator. Raw agent output is never consumed before the funnel runs; an agent that errors records its unit as incomplete without corrupting the batch.

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。

Review a diff with the reviewer fleet, funneled to one triaged verdict. Targets the current working diff, a path, or an inbound GitHub PR.

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。