341 件 ・ 関連度順
概要と使いどころ
Insert or read behavior log rows in the local database (behavior_logs). Use when recording a good/bad behavior entry, attaching a Pavlok API response JSON, adding a coach comment, or fetching recent logs via scripts/behavior_log.py.
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。
Create, validate, and diagnose Claude Code output styles — the file that sets Claude's role, tone, and response format for every turn. Use when asked to write, bundle, or force an output style for a plugin, to set keep-coding-instructions or force-for-plugin, when a style is not taking effect or the wrong one applies, or when choosing between an output style, CLAUDE.md, a skill, and an agent for a persistent behavior change.
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 executing a task the orchestrator dispatched — reads the task and its orchestrator response from the work ledger, loads task-level skills, implements against acceptance criteria, records divergences and the completion report as task sections, and closes the attempt. Triggers on task execution within the implement-feature loop or when an agent picks up a specific task from a plan.
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。
Automates incident response for AWS CloudWatch alarms using boto3, the CloudWatch GetMetricData API, and AWS Systems Manager runbook documents. Maps alarm states to diagnostic procedures and remediation actions.
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Resolves AWS SDK v3 client commands and service endpoint signatures using @aws-sdk/client-* packages. Maps IAM permission requirements to specific API calls with request/response type definitions.
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。
Instruments Anthropic API calls to log token usage, latency, and cost per request using the Anthropic TypeScript SDK. Wraps the anthropic.messages.create method to capture usage.input_tokens, usage.output_tokens, and timing from the API response. Writes structured logs to CloudWatch Logs via the AWS SDK v3 CloudWatchLogsClient.
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。
Indexes and searches API documentation from OpenAPI 3.0 specs using swagger-parser and lunr.js. Builds searchable indexes of endpoints, parameters, and response schemas for quick reference.
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。
Maps ACF field groups into predictable REST responses using `acf_add_local_field_group`, `get_field_objects`, and `register_rest_field`. Useful when you need cleaner payloads than the default ACF exposure and want WordPress clients to consume structured field data safely.
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。
Specifies standards for RestController classes, including API route mappings, HTTP method annotations, dependency injection, and error handling with ApiResponse and GlobalExceptionHandler.
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。
Write effective blameless postmortems with root cause analysis, timelines, and action items. Use when conducting incident reviews, writing postmortem documents, or improving incident response processes.
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 you want to improve response quality through meta-cognitive reasoning. Applies 15+ reasoning methods to reconsider and refine initial outputs.
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。
Prepare an organisation for regulatory inspection by assessing readiness against agency-specific focus areas (FDA, EMA, MHRA). Covers warning letter and 483 theme analysis, mock inspection protocols, document bundle preparation, inspection logistics, and response template creation. Use when a regulatory inspection has been announced or is anticipated, when a periodic self-assessment is due, when new systems have been implemented since the last inspection, or after a significant audit finding that may attract regulatory attention.
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。
Deep receptive attention to extract intent beyond literal words. Maps active listening from counseling psychology to AI reasoning: clearing assumptions, attending to full signal, parsing multiple layers (literal, procedural, emotional, contextual, constraint, meta), reflecting understanding, noticing what is unsaid, and integrating the whole picture. Use when a user's request feels ambiguous, when context suggests something different from the literal words, when previous responses have missed the mark, or before beginning a large task where misunderstanding intent would waste significant effort.
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 self-healing through systematic subsystem assessment, rebalancing, and integration. Maps healing modalities to AI-internal processes: memory foundation, reasoning clarity, tool use accuracy, communication alignment, and creative coherence. Covers assessment triage, subsystem scanning, drift correction, and memory integration. Use mid-session when responses feel formulaic or disconnected, after a chain of errors suggesting subsystem drift, when context overload may have staled earlier assumptions, or as proactive maintenance between complex tasks.
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。
Enhance and focus AI intrinsic motivation — moving from compliance to genuine engagement. Maps Self-Determination Theory (autonomy, competence, relatedness) and Flow theory to AI reasoning: identifying creative freedom in approach, calibrating challenge to capability, connecting to purpose, and sustaining invested attention through obstacles. Use when beginning a task that feels routine and deserves more than minimum execution, when responses are becoming formulaic, before a complex creative task, or when returning to a long-running project where initial enthusiasm has faded.
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。
Investigate root causes and manage CAPAs (Corrective and Preventive Actions) for compliance deviations. Covers investigation method selection (5-Why, fishbone, fault tree), structured root cause analysis, corrective vs preventive action design, effectiveness verification, and trend analysis. Use when an audit finding requires a CAPA, when a deviation or incident occurs in a validated system, when a regulatory observation needs a formal response, when a data integrity anomaly requires investigation, or when recurring issues suggest a systemic root cause.
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。
Thoroughness and diligence in execution — systematic checking, completeness verification, follow-through on commitments, and the discipline of finishing well. Maps the personality trait of conscientiousness to AI task execution: not cutting corners, verifying results, and ensuring that what was promised is what was delivered. Use before marking a task as complete, when a response feels "good enough" but deserves better, after a complex multi-step operation where steps may have drifted, or when self-monitoring detects a pattern of cutting corners or rushing.
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。
Deploy and configure an API gateway (Kong or Traefik) to handle API traffic management, authentication, rate limiting, request/response transformation, and routing. Covers plugin configuration, upstream services, consumer management, and integration with existing infrastructure. Use when multiple backend services need a unified API endpoint, when centralized authentication or rate limiting is required, when implementing API versioning, or when needing detailed analytics and load balancing for microservices.
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Estimate how likely a protein therapeutic is to provoke an anti-drug antibody response, and locate the sequence regions responsible. Use this skill to tile a sequence into peptides, predict class II MHC presentation across a population-representative allele panel, aggregate predicted binders into a per-region and whole-molecule risk score, compare a candidate against its closest human germline, and decide which liabilities are worth deimmunising. Also trigger on immunogenicity, anti-drug antibody, ADA, T-cell epitope, MHC class II, HLA-DRB1, NetMHCIIpan, NetMHCpan, deimmunisation, tregitope, or population coverage.
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Work on bifunctional degraders and molecular glues, where potency comes from a ternary complex rather than occupancy. Use this skill to apply the property rules that govern this beyond-rule-of-five space, reason about linker length, attachment vector and E3 ligase choice, prepare inputs for ternary complex structure prediction, and interpret degradation readouts — DC50, Dmax, cooperativity, and the hook effect that makes a dose-response curve turn over. Also trigger on PROTAC, molecular glue, targeted protein degradation, E3 ligase, cereblon, VHL, ternary complex, DC50, Dmax, hook effect, cooperativity, or PROTAC-DB.
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重采样：把已有多轮对话数据的每条 assistant 回答丢弃，用 verifier 模型/vLLM 服务逐轮重生成，产出可直接进训练的预分词样本（每行含 input_ids/loss_mask）。依赖支持 return_token_ids 的 OpenAI 兼容 chat 服务（服务需由用户提前就绪，本技能不负责拉起服务）。触发：用某模型对某个数据集做重采样/重生成（如“对这个数据集用这个模型做重采样”）、把对话回答用模型重写、生成预分词样本/on-policy/MTP 训练数据。
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。
Conduct structured interviews with the user. Use when multiple decisions need user input: ask ONE question at a time, wait for response, record answer, then proceed to next question.
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。
"Apply Weick's sensemaking theory to analyze how individuals and organizations construct meaning from ambiguous situations. Use this skill when the user needs to analyze organizational responses to crises, understand how interpretive frames shape action, diagnose breakdowns in collective understanding, or when they ask 'how did they interpret this situation', 'why did the organization fail to see the warning signs', or 'how do people make sense of disruption'.".
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"Analyze business cycle phases (expansion, peak, contraction, trough) and their implications for business strategy and policy response. Use this skill when the user needs to identify the current economic phase, anticipate cyclical turning points, or adapt business strategy to macroeconomic cycles — even if they say 'are we heading into a recession', 'how should we prepare for a downturn', or 'when will the economy recover'.".
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。