「evaluate」の検索結果

Use after receiving WebGPT or another LLM review on a Formax todo and before sending the todo back for another pass. Produce a concise handoff response that says what we adopted, what we reject or question, and what the reviewer should specifically re-evaluate in `docs/todolist.md`.

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Research products across the web with Firecrawl and produce a shopping recommendation or cart-ready summary. Use when the user wants to compare products, find the best option, evaluate reviews, respect budget/preferences, or shop with a saved browser session.

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Use when prompting a learner to complete hands-on MATLAB coding exercises, guided practice, debugging drills, code tracing, small MATLAB projects, or MATLAB-script assessment during tutoring. Use when the tutor should create a complete runnable MATLAB script, execute it through MATLAB tools, compare the produced outputs with expected outputs, and evaluate MATLAB programming style.

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Use when reviewing, auditing, scoring, or improving a real or synthetic MATLAB AI tutor transcript, tutoring prompt, generated lesson, exercise, feedback sequence, or skill behavior for MATLAB accuracy, active learning, assignment guardrails, feedback quality, debugging support, transfer prompts, and instructor-facing quality recommendations.

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Evaluates a property across three investment strategies — Buy & Hold, BRRRR, and Fix & Flip — with feasibility scores, multi-year projections, tax benefits, and break-even analysis

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Reviews and adjusts PRD plans based on research findings. Use after completing research to evaluate story modifications. Triggers on: review plan, adjust stories, update prd based on research.

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Make technology decisions for every product capability by discovering existing assets, evaluating vendor-aligned options, and categorizing as Reuse/Extend/Build/Buy/Integrate/Research during PRD v0.5 Red Team Review. Handles both greenfield and brownfield contexts. Triggers on "tech stack", "build or buy?", "what technologies?", "technical decisions", "what do we reuse?", "existing stack", "vendor constraint", "IBM-first", "what tools do we need?", "evaluate solutions", "select tech stack". Consumes FEA- (features), SCR- (screens), RISK- (constraints). Outputs TECH- entries with decisions, rationale, and cross-references. Feeds v0.6 Architecture Design.

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Create, edit, evaluate, and package agent skills. Use when building a new skill from scratch, improving an existing skill, fixing a skill that never triggers or fires unreliably, running evals to test a skill, benchmarking skill performance, optimizing a skill's description, reviewing third-party skills for quality, or packaging skills for distribution — even if the user doesn't explicitly say "skill" (e.g. "teach Claude to do X", "make the agent always follow Y"). Not for using skills or general coding tasks.

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This skill should be used when the user asks to "analyze session", "evaluate skill execution", "check session logs", provides a session ID with a skill path, or wants to verify that a skill executed correctly in a past session. Post-hoc analysis of Claude Code sessions to validate skill/agent/hook behavior against SKILL.md specifications.

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Analyze contracts for risks, check completeness, and provide actionable recommendations. Use when a user asks to review a contract, check an agreement for red flags, analyze contract terms, find risky clauses, review an NDA, evaluate a service agreement, or audit employment contract terms. Supports employment contracts, NDAs, service agreements, and vendor contracts.

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Screen job applications against requirements and score candidates objectively. Use when a user asks to review applications, evaluate candidates, screen resumes, rank applicants, assess qualifications against a job description, shortlist candidates, or build a hiring scorecard.

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Run AI agent and LLM evaluations in CI/CD pipelines — automated quality gates that fail the build when AI output quality drops. Use when someone asks to "test my AI agent", "add evals to CI", "catch prompt regressions", "compare models", "evaluate LLM output quality", "set up AI quality gates", or "benchmark my agent before deploying". Covers eval frameworks (Cobalt, Promptfoo, Braintrust), LLM-as-judge scoring, threshold-based assertions, and GitHub Actions integration.

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Technical decision-making frameworks - trade-off evaluation, reversibility analysis, and second-order thinking for better engineering choicesUse when "should we, which is better, trade-off, decision, choose between, versus, pros and cons, what if we, is it worth, evaluate options, decisions, trade-offs, architecture, adr, reversibility, strategy, planning, risk" mentioned.

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Flagship intra-paper self-consistency forensics: does the paper contradict ITSELF across abstract/intro/tables/body/appendix, and does the method DESCRIBED match the method EVALUATED? Needs no external ground truth — works PDF-only (L0). Runs a deterministic arithmetic pass + a fresh cross-model semantic pass, every finding span-anchored to the evidence ledger (claims.json), reviewer≠adjudicator. Emits consistency-audit.findings.json; NEVER computes the verdict. Triggers: "consistency audit", "check the paper against itself", "self-consistency", "内部自洽".

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Audit experiment integrity against the evidence ledger. At L2 (repo + result files present) a fresh cross-model reviewer reads the eval code line-by-line for fake/derived ground truth, score self-normalization, phantom results (a paper number with no backing file/key), dead/uncalled metric code, verified-scope inflation, method-described ≠ method-evaluated drift, synthesized-looking results, placeholder/fake data still wired into a released result, code-output ≠ reported-number mismatch, and missing reproducibility artifacts (an empirical/agent/LLM paper shipping neither code nor the prompts/configs its results need) — every finding span-anchored to a ledger claim_id. At L0/L1 (PDF / source only) the same patterns are surfaced as info-level 'could-not-verify' signals where the ledger gives an anchor (observability_level_required:2) — NEVER a fraud verdict from a PDF. The reviewer PROPOSES findings; tools/adjudicate_findings.py computes the verdict. Detect-only. Triggers: "experiment forensics", "audit the results", "check the eval code", "实验诚实度".

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 third-party vendors for engineering fit. Use when a senior developer needs a structured vendor 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。

Assess a system for growth bottlenecks. Use when a senior developer needs to evaluate scaling limits.

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。

Finds, evaluates, fills, submits, and tracks a candidate's own job applications using a verified resume, evidence-based targeting, secure local profile storage, and browser automation. Use for onboarding or migrating a job-search profile, searching active roles, assessing a posting, applying to an authorized URL or batch, recording outcomes, or reviewing application effectiveness.

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 proposed repository harness changes with pinned Darwin tools and explicit evidence limits.

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。

Exhaustive problem space exploration using the MIT Synthetic Neurobiology "tiling tree" method. Partitions a problem into MECE (Mutually Exclusive, Collectively Exhaustive) subsets recursively via parallel subagents, then evaluates leaf ideas against specified criteria. Use when users say "tiling tree", "tile the solution space", "exhaustively explore approaches to", "what are all the ways to", or request a MECE breakdown of a problem. Requires orchestrating-chat-agents 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。

Use when the user explicitly asks to use the informed-patient skill to prepare for a medical appointment, organize symptoms before seeing a doctor, or evaluate the evidence behind a diagnosis or treatment. Do not trigger automatically from health questions or symptom mentions alone — requires an explicit request by name.

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Break out of a locked problem frame by picking one disciplined move — reframe, provocation (Po), random stimulus, SCAMPER, inversion, structured analogy, constraint play, or family traversal — and committing to it before evaluating. Use when stuck, when options feel narrow or obvious, when iterations produce variations of the same idea, or when the user says "widen this", "break out of", "think differently", "I'm stuck", "feels too obvious", "stress-test the framing", "what am I missing", or holds two related examples and asks what lies between or beyond them. Complements challenging (which evaluates) and convening-experts (which synthesizes viewpoints); this skill generates distance, not judgment.

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 proposed repository harness changes with pinned Darwin tools and explicit evidence limits.

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。

This skill should be used when the user asks to "design system architecture", "evaluate microservices vs monolith", "create architecture diagrams", "analyze dependencies", "choose a database", "plan for scalability", "make technical decisions", or "review system design". Use for architecture decision records (ADRs), tech stack evaluation, system design reviews, dependency analysis, and generating architecture diagrams in Mermaid, PlantUML, or ASCII format.

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。