「evaluate」の検索結果

Help entrepreneurs identify and evaluate communities for a minimalist business. Use when someone is exploring business ideas, seeking their target community, or determining where to start.

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。

Help founders evaluate business decisions through the lens of sustainable, profitable growth. Use when discussing spending, hiring, fundraising, pricing, or scaling decisions.

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 any business decision, plan, or strategy through the minimalist entrepreneur lens. Use to gut-check decisions, simplify approaches, or evaluate options using core principles.

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。

Score an Instagram creator's (or a list of creators') audience authenticity and engagement quality using Xpoz. Measures real engagement rate against views rather than follower count, the bot/spam share of their comments, and an overall 0-100 authenticity score for influencer vetting and partner selection. Use when asked to "vet this influencer", "are their followers real", "check engagement quality for @username", "rate these Instagram accounts", "compare creators before a sponsorship", or when the user pastes a list of Instagram handles to evaluate.

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, implement, or evaluate GEODE's GEO capability with source-grounded stage diagnosis, frozen measurement contracts, and no-model preflight before live engine 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。

Evaluate repeated code and consolidate duplication when a shared abstraction improves correctness or maintenance. Use after introducing similar logic or when repeated changes reveal a missing source of truth.

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。

Used by the Reviewer agent during Phase 9 (DesignVerify) and Phase 11 (Quality) to evaluate frontend code. Assesses visual hierarchy, spacing consistency, accessibility, and detects formulaic 'AI slop' design patterns, providing actionable feedback to the Developer.

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。

Evaluate error handling, i18n, text overflow, and edge cases.

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。

Drafts a BSA/AML Risk Assessment for U.S. financial institutions per FinCEN, FFIEC, and OCC standards. Evaluates inherent risks (customer, product, geographic, transaction, third-party), control adequacy, and residual risk. Use when preparing annual BSA compliance assessments, post-acquisition integration reviews, or when business changes trigger reassessment under 31 U.S.C. § 5318(h).

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。

Produces an enforcement-aware Animal Welfare Act compliance audit for USDA/APHIS-regulated facilities. Maps Form 7002 inspection citations to verified 9 C.F.R. Parts 1-3 requirements, analyzes NCI severity and recurrence patterns, evaluates core compliance programs (PVC, IACUC, housing, records, transport), and outputs a prioritized corrective action plan with proof artifacts. Use when auditing AWA compliance, reviewing USDA inspection reports, preparing for license renewal, conducting diligence, or assessing enforcement risk. Trigger: Animal Welfare Act, AWA, USDA, APHIS, Form 7002, IACUC, PVC, exhibitor, dealer, research facility.

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。

Produces litigation-grade case summaries for animal law disputes including cruelty/neglect prosecutions, ownership/custody, dangerous animal designations, veterinary malpractice, and service/assistance animal accommodations. Builds exhibit-cited factual timelines, maps evidence to statutory elements, evaluates expert evidence, and assesses remedies with jurisdiction-specific legal research. Use when summarizing animal law cases, analyzing animal cruelty charges, pet custody disputes, dangerous dog hearings, veterinary malpractice claims, or assistance animal accommodation matters. Also trigger for animal forfeiture, bite liability, wildlife possession, or animal-related court filings.

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。

AI compliance and policy engine — evaluate scan results against OWASP, NIST, SOC 2, ISO 27001, CMMC, EU AI Act, AISVS v1.0, and related frameworks. Generate SBOMs and compliance reports. Use when: "compliance report", "NIST", "SOC 2", "ISO 27001", "OWASP", "EU AI Act", "AISVS", "generate SBOM", "policy 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。

AI compliance analysis for EU AI Act, ISO 42001, NIST AI RMF, GDPR, OECD, financial services regulations (SEC, FCA, FINRA, DORA, MiFID II), and other frameworks. Use when asked to generate a compliance checklist for an AI tool or use case, determine if a risk assessment is required, score an AI tool's risk level, identify where an AI tool or use case could run afoul of regulatory requirements, perform a gap analysis, recommend remediation steps, assess a vendor, draft an acceptable use policy, map training requirements, or review jurisdiction-specific AI rules. Triggers on phrases like "compliance checklist", "risk assessment", "risk score", "does this comply", "EU AI Act", "ISO 42001", "NIST AI RMF", "AI governance", "gap analysis", "vendor assessment", "acceptable use policy", "MNPI", "AI training requirements", or any request to evaluate an AI tool or use case for regulatory compliance or risk.

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-pass structural code review with adversarial personas. Input: branch diff (default), PR #123, commit, --pending. Use for 'review this PR/diff'. NOT for behavioral verification (mk:evaluate).

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。

Evaluate Jira ticket complexity and inconsistencies via jira-evaluator agent, read-only. NOT estimation (mk:jira-estimator) or full RCA (mk:jira-analyst).

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。

Experimental behavioral evaluator. Drives a running artifact via browser/curl/CLI and records rubric evidence; the runner is not yet a fully implemented automated evaluation system.

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 autonomously improving a measurable scalar metric through bounded, git-tracked iterations: modify one scoped change, verify, keep or revert. Triggers on /mk:loop, 'optimize coverage/bundle size/lint count', 'iterate until the metric improves'. NOT for subjective cleanup (see mk:cook), known-root-cause bugs (see mk:fix), behavioral grading (see mk:evaluate), or shipping (see mk:ship).

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-pass structural code review with adversarial analysis, scope-aware dispatch, adversarial persona passes, and forced-finding protocol. Supports input modes: branch diff (default), PR number (#123), commit hash, pending changes (--pending). Use when asked to "review this PR", "code review", "pre-landing review", "check my diff", or "review #123". Proactively suggest when the user is about to merge or land code changes. NOT for behavioral verification against a running build (see mk:evaluate); NOT for post-implementation simplification (see mk:simplify).

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 a decision needs deep multi-source technical research with cited evidence — technology evaluation, ecosystem comparison, standards/spec fact-finding, "how do others solve X". Triggers on "research X", "deep dive on X", "evaluate X vs Y", "find best practices for X". NOT for library/API doc lookup (see mk:docs-finder); NOT for one-shot URL fetch (see mk:web-to-markdown); NOT for internal codebase discovery (see mk:scout); NOT for project-only Q&A (see mk:ask-me); NOT for comparing solution designs (see mk:brainstorming); NOT for root-cause debugging (see mk:investigate); NOT for wiki ingestion of sources (see mk:wiki-research).

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 external URL to evaluate fit with oh-my-customcode project and auto-create GitHub issue with verdict

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。

Runnable end-to-end validation playbook for the Rembric TUI installer — catch breakage before merging/deploying, AND a sandboxed manual walkthrough to evaluate/iterate its UI/UX without polluting your real env. Apply before shipping any change touching `apps/plugin/install.sh`, the repo-root `install.sh` shim, any per-client `install.sh`/`uninstall.sh`, or distribution docs, or when you want to preview/try/demo the installer and polish how it looks and feels. Layered: CI-safe headless (vitest + `sh -n`), local install round-trips, operator interactive pty smoke, an optional full Docker `up` smoke, plus a sandboxed manual UX walkthrough. For the installer contract/what-not-to-break, see `rembric-tui-installer`.

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。

Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.

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。

Assess whether a hospitalized adult with diabetes on insulin pump therapy remains a candidate for continued inpatient pump use. Consider triggers such as the clinician asking "Is this patient appropriate to continue insulin pump in hospital?" and evaluate for impaired consciousness, inability to adjust pump settings, critical illness, DKA, or hyperosmolar hyperglycemic state.

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。

Assesses Tanner stage to determine if physical changes of puberty have begun (stage G2/B2 or higher) for considering pubertal suppression. Triggered when a clinician evaluates pubertal development in an adolescent patient.

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。