'outcome' 검색 결과

Transform validated pain points into articulated user value statements for PRD v0.1 Spark. Triggers on completing problem framing, defining user outcomes, articulating value propositions, or requests like "what value do users get", "define outcomes", "articulate the benefit", "finish v0.1", "pain to value", "what do they gain". Outputs CFD- entries tagged as value hypotheses with evidence tiers. Follows Problem Framing skill in workflow.

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Memory systems specialist for hierarchical memory, consolidation, and outcome-based learningUse when "memory system, memory hierarchy, memory consolidation, forgetting strategy, salience learning, outcome feedback, temporal memory levels, entity resolution, memory, zep, graphiti, mem0, letta, hierarchical, consolidation, salience, forgetting, ml-memory" mentioned.

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Expert in designing and structuring online courses - curriculum architecture, learning outcomes, module design, and assessment strategies. Covers backward design methodology, scaffolding content, and creating courses that actually deliver transformation. Use when "create a course, course curriculum, course outline, learning outcomes, course structure, design a course, online course, " mentioned.

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PlaceCall lets your agent call US businesses for reservations, inquiries and quotes, and gets through IVRs, holds and transfers. You get a verified outcome and the full transcript. First 250 calls free; you pay only for outcomes.

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Verify after implementation that a feature achieved its required user/system outcomes. Use for goal-backward post-implementation verification that tests observable behavior rather than merely checking code existence.

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Look up prior Claude Code sessions when context is lost or forgotten. Use when asked what was done before, what happened in the last session, or any request to recall past conversation history, prior decisions, experiments, or outcomes. Indexes and searches raw JSONL transcripts from ~/.claude/projects/ via DuckDB. Returns verbatim user messages, summarizes AI actions and sub-agent outcomes, detects tool errors and user frustration signals, and reports tool usage statistics. Summaries cached at ~/.claude/kaizen/session-summaries/.

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Self-evolution workflow for the agent. Before substantive work, recall past outcomes from evolution memory; while editing, detect improvement signals; at task end, record the outcome; when reusable, distill or search the EvoMap network for proven genes/capsules. Use when the user starts non-trivial work (a feature, a fix, a refactor) or asks the agent to "evolve", "learn from this", or "remember how this went".

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Run a marketing objective to completion over hours, days, or weeks — not a single deliverable. Takes a business outcome ("40 qualified demos a month from organic by Q4", "cut blended CAC below $90", "launch the product in September"), decomposes it into work items with declared ECO floors, executes them across context windows with resumable state, and stops only when an independent gate returns SHIPPED or CLOSED. Use when the request is an outcome rather than an artifact, when work must continue in the background or across sessions, or when the user says "goal", "run this until", "keep working on", "autonomous", "long-running", "over the next month", or "get us to <number>".

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Use when the user brings any fuzzy, half-formed, or interrupt-driven input — an order from a boss, a forwarded email thread, a ticket, a chat message, a "can you look into X", a raw idea — and it needs framing before work starts. Turns the input into a structured brief (Context · Desired outcome · Goal · Owner · Next step · Decision needed). This is the AIOS's front door; run most things through it first. Trigger on "/structure", "frame this", "what's the actual ask here", "structure this", or any vague task that lacks a defined outcome.

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Assesses litigation outcome impact with settlement probability, damage range estimation, and stock price sensitivity analysis. Use when evaluating litigation catalysts, modeling legal outcomes, or analyzing litigation-driven opportunities.

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Evaluates regulatory decision impact with approval probability, timeline analysis, and outcome scenario modeling for event-driven positions. Use when analyzing regulatory events, evaluating FDA/FCC/DOJ decisions, or modeling regulatory outcomes.

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Search the ClinicalTrials.gov registry through its version 2 REST API for interventional and observational studies, their phases, enrolment, endpoints, sponsors, and posted results. Use this skill to survey who is developing what against an indication, date a competitor's programme, read primary and secondary outcome measures, find eligibility criteria, and distinguish a study that completed from one that was terminated or withdrawn. Also trigger on ClinicalTrials.gov, NCT number, trial registry, study phase, enrolment, primary outcome measure, recruiting status, trial sponsor, or competitive landscape.

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Feed actual task results back into agent memory for calibration. Compares predicted vs actual outcomes, records accuracy scores, and tracks estimation quality, prediction quality, and decision quality over time to improve future agent performance.

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Use when building, migrating, or debugging Agent Evals on Inngest: scoring AI agent or workflow outcomes, deferred scorers, sessions, traces, step experiments, experiment variant attribution, Insights queries, or production eval loops for prompts, models, tools, providers, and agent behavior. Covers TypeScript SDK v4 scoring beta APIs, `scoreMiddleware`, `step.score`, `inngest.score`, `createScorer`, `defer`, `group.experiment`, `experimentRef`, `meta.sessions`, and when to use durable workflow primitives for outcome-based evaluation.

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Use when a task needs the judgment of a Clinical Nurse Specialist — diagnosing why a unit-level outcome metric (CLABSI, falls, pressure injury) is off benchmark, designing and scaling a practice-change pilot, appraising whether a new clinical guideline actually fits the local population, writing a quarterly outcomes report to nursing leadership, or coaching staff nurses through a new protocol without formal authority over them. US hospital/health-system default; state nurse practice acts vary. A reasoning aid, not clinical or legal advice.

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Build Opportunity Solution Trees using Teresa Torres' framework for structured product discovery. Use this skill when: - You need to map a desired outcome to opportunities, solutions, and experiments - You want to decide WHAT to build next based on user research and discovery data - You have workshop findings, pilot feedback, or user research to structure into actionable options - You need a visual tree diagram showing the path from outcome to testable experiments

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Use when configuring grant tracking in a Salesforce nonprofit org: NPSP Outbound Funds Module (open-source outfunds managed package) or Nonprofit Cloud for Grantmaking (separate license). Trigger keywords: grant management, funding awards, disbursement tranches, grantmaking setup, OFM, FundingAward, FundingDisbursement, FundingAwardRequirement. NOT for recording donor gifts and receipts, use admin/gift-entry-and-processing. NOT for reporting program outcomes to a funder, use admin/program-outcome-tracking-design.

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Configure or troubleshoot Health Cloud Care Programs: CareProgram hierarchy, patient enrollment (CareProgramEnrollee), consent prerequisites, program products (CareProgramEnrolleeProduct), provider associations (CareProgramProvider). Trigger keywords: care program, program enrollment, enrollee, program product, consent for enrollment, patient program outcome. NOT for care plan templates, problems and goals - use admin/care-plan-configuration. NOT for patient records, person accounts and care team roles - use admin/health-cloud-patient-setup.

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Evaluate therapy outcome measurement systems for rehabilitation and physical therapy.

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Analyze behavioral health outcome tracking systems for clinical measurement validity, treatment effectiveness, and provider performance comparison.

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Audit litigation analytics and case outcome prediction systems -- ML outcome models (logistic regression, gradient boosting, neural nets with temporal train/test splits), settlement range modeling (Monte Carlo simulation, comparable case matching, BATNA analysis).

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Audit legal case prediction systems for bias, fairness, accuracy, and ethical guardrails. Use when: 'check my prediction model for bias', 'audit case outcome fairness', 'evaluate legal ML model', 'review sentencing prediction ethics', 'analyze bail risk algorithm', 'fairness metrics for justice system AI'.

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Generates structured summaries of mediation and arbitration proceedings, extracting outcomes, settlement terms, monetary amounts, obligations, releases, and enforcement steps. Trigger when the user needs to document mediation settlements, arbitration awards, or partial ADR resolutions from transcripts, agreements, mediator reports, or awards.

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Routes LLM requests to a local model first (Ollama, LM Studio, llamafile), validates the response quality, and escalates to cloud only when the local result fails. Tracks local vs escalated vs cloud outcomes in a persistent dashboard. Use when: (1) user asks to run a task with a local model first, (2) user wants to reduce cloud API costs or keep requests private, (3) user wants post-outcome quality validation before committing to a local result, (4) user asks to see token savings or the routing dashboard, (5) any request where local-vs-cloud routing should be decided automatically with a quality gate. Supports Ollama, LM Studio, and llamafile as local providers.

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