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概要と使いどころ
Compare before/after Coval runs using matched cases, stable metric versions and inspected conversation evidence. Use to assess a regression or proposed improvement; does not automatically launch or tune anything.
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。
Discover failure modes in Coval runs, uploaded conversations or local conversation exports using scoped sampling and transcript, audio and trace evidence. Prepare human review without inventing ground truth or launching new runs.
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。
Execute a bounded Coval evaluation on explicit test cases, then audit conversation, recording and metric evidence. Use when the user requests a run; does not automatically rerun, tune an agent or schedule evaluations.
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 a Coval adversarial / red-team testing report and turn it into an agent-hardening plan. Use when a user provides a Coval report URL, report export, run IDs, screenshots, or a per-scenario scorecard from an adversarial sweep and wants evidence-backed next steps such as prompt/guardrail changes, refusal hardening, verification fixes, escalation routing, or expanded attack coverage.
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 an existing Coval evaluation setup for coverage, misleading scores, missing evidence, judge validation and execution risk. Read-only; use when inheriting an eval or deciding whether its results support a release decision.
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 a Coval accent testing report from runs across different speaker accents. Use when a user provides a Coval report URL, report export, run IDs, screenshots, or metric summary and wants evidence-backed next steps such as prompt changes, STT/confirmation adjustments, accent-robust routing, or expanded accent coverage.
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。
Select and configure a Coval metric for a concrete product criterion, choosing transcript, audio, trace or deterministic evidence and testing the result. Use for metric creation or scoring design, not for final human calibration.
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 a Coval audio-quality testing report from runs across different voice, speaking-style, volume, interruption, and background-noise scenarios. Use when a user provides a Coval report URL, report export, run IDs, screenshots, or metric summary and wants evidence-backed next steps such as prompt changes, tool handling fixes, STT/TTS adjustments, trace setup, or expanded audio-scenario coverage.
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 an idea, proposal, change, claim, or decision from facts through problem, value, mechanism, consequences, evidence, alternatives, and action. Use when the user asks what something is for, why it matters, whether it is worth doing, what impact it has, or how to decide. Do not use for simple factual lookup or straightforward execution.
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。
Query a running Convex app's logs + health in natural language (official MCP): failures, slow/expensive functions, deploy causality — scoped, evidence-backed, with a dashboard deep link.
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。
Read the Convex deployment's 72h insights (read limits, OCC contention), root-cause each event in code, report evidence-backed perf/cost findings with 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。
Law-driven abstraction protocol: evidence first, seam test, minimal algebra, executable law check.
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 writing recommendation letters, reference letters, or award nominations for students, postdocs, or colleagues. Invoke when user mentions recommendation letter, reference, nomination, letter of support, endorsement, or needs help with strong advocacy, comparative statements, or evidence-based character assessment.
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。
Transparent, rigorous research with full methodology — not a black-box API wrapper. Conducts exhaustive investigation through mandated 2-cycle research per theme, APA 7th citations, evidence hierarchy, and 3 user checkpoints. Self-contained using native OpenClaw tools (web_search, web_fetch, sessions_spawn). Use for literature reviews, competitive intelligence, or any research requiring academic rigor and reproducibility.
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。
[32] ANALYZE. Ensure every critical claim has verifiable evidence with confidence levels. Each fact must have source + confidence percentage. If confidence <85%, enter Loop150 to find more sources. Use for critical decisions, factual claims, legal/compliance work, or any situation where unverified claims are dangerous.
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。
[21] UNDERSTAND. Deep research workflow for this project using 150% scope (100% core + 50% boundary), evidence-based reasoning, and structured investigation notes. Use when the task requires investigation, root-cause analysis, or mapping unknown areas. Always maintain a research log file that captures findings, hypotheses, and next branches; use web.run when external verification is needed.
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 and fix production errors from Sentry crash reports. Use when investigating AppHang, ANR, crashes, or production errors. Includes complete workflow from JSON analysis to bug fix implementation with evidence-based methodology. Triggers on sentry, crash, AppHang, ANR, error analysis, production error, bug analysis, crash report, freeze, hang, not responding, stacktrace, breadcrumbs, exception.
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。
Multi-source deep research using firecrawl and exa MCPs. Searches the web, synthesizes findings, and delivers cited reports with source attribution. Use when the user wants thorough research on any topic with evidence and citations.
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。
Systematic exploratory QA testing of web applications — find bugs, capture evidence, and generate structured reports
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 about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always
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。
Systematically explore and test a web application to find bugs, UX issues, and other problems. Use when asked to "dogfood", "QA", "exploratory test", "find issues", "bug hunt", "test this app/site/platform", or review the quality of a web application. Produces a structured report with full reproduction evidence -- step-by-step screenshots, repro videos, and detailed repro steps for every issue -- so findings can be handed directly to the responsible teams.
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。
Generate concise (3-4 page), focused medical treatment plans in LaTeX/PDF format for all clinical specialties. Supports general medical treatment, rehabilitation therapy, mental health care, chronic disease management, perioperative care, and pain management. Includes SMART goal frameworks, evidence-based interventions with minimal text citations, regulatory compliance (HIPAA), and professional formatting. Prioritizes brevity and clinical actionability.
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。
Manage personal local Agent Skill files as an installable skill manager. Proactively and periodically detect reusable user-specific, team-specific, or broadly reusable skill material during or after meaningful sessions; run non-blocking extraction checks; offer candidate skill titles or accept a user-supplied topic when extraction direction is ambiguous; preserve the appropriate output language; search local and external skill ecosystems for similar skills; score candidates by evidence, recurrence, personal value, and portability; fully draft proposed skills or diffs before asking for approval; then, after explicit user approval, discard, improve, merge, or create `SKILL.md` folders.
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。
Research markets with sizing, segmentation, competitor mapping, pricing checks, and demand validation that turn fuzzy ideas into decision-ready evidence. Use when (1) you need TAM, SAM, SOM, whitespace, or category sizing; (2) you must compare competitors, pricing, positioning, or customer segments before acting; (3) the user asks whether a niche, launch, expansion, or go-to-market bet is actually worth pursuing.
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。