「cis」の検索結果

Use when a user asks for a plan for a coding task, to generate a clear, actionable, and atomic checklist.

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Clawdbot documentation expert with decision tree navigation, search scripts, doc fetching, version tracking, and config snippets for all Clawdbot features

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Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance. The challenge isn't making them capable - it's making them reliable. Every extra decision multiplies failure probability. This skill covers agent loops (ReAct, Plan-Execute), goal decomposition, reflection patterns, and production reliability. Key insight: compounding error rates kill autonomous agents. A 95% success rate per step drops to 60% b

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API design principles and decision-making. REST vs GraphQL vs tRPC selection, response formats, versioning, pagination.

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。

A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).

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Complete methodology for building production-grade React applications with architecture decisions, component design, state management, performance optimization, testing, and deployment.

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Complete engineering management system — team building, 1:1s, performance, hiring, architecture decisions, incident management, and scaling. From IC-to-manager transition through director-level operations.

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-optimized web search via AIsa's Tavily API proxy. Returns concise, relevant results for AI agents through AIsa's unified API gateway.

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Guide users through a structured workflow for co-authoring documentation. Use when user wants to write documentation, proposals, technical specs, decision docs, or similar structured content. This workflow helps users efficiently transfer context, refine content through iteration, and verify the doc works for readers. Trigger when user mentions writing docs, creating proposals, drafting specs, or similar documentation tasks.

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Generates comprehension questions and language exercises based on the analyzed content to create a complete learning loop.

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Requires OFOX_API_KEY — create one at https://app.ofox.ai. Generate a clean, catalog-style e-commerce product video from a real product photo (or, for a generic or fictional product, a text description) using the Ofox video API (Seedance 2.5) — runs a short creative brief (product photo, target platform and aspect ratio, background, camera orbit or turntable) when the request leaves them open, writes a plain-background, literal-accuracy prompt (precise product description, a simple camera orbit or turntable motion, no dramatic cinematography), shows a cost estimate, then calls ofox-video-core to submit, poll, download, and report the real cost. Use when a user asks to turn a product photo into catalog/listing footage, e.g. "make this product photo a 360-degree white-background showcase", "turn this photo into a white-background product video", "make a clean turntable video of this item", or "give me a 5-second white-background rotation video of this product for my listing". Do not use for cinematic brand/mood advertising (see seedance-ad-creative) or for anything involving people/dialogue (see seedance-short-drama).

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Takes raw leads and enriches them to outreach-ready status. Chains company-researcher, cro-auditor, decision-maker-finder, apollo-lead-finder, email-validator, and personalization-enricher.

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Enriches inbound leads with firmographic and technographic data. Takes minimal lead info and enriches via WebSearch, Apollo, or ZoomInfo to produce a complete lead record with company size, revenue, tech stack, social profiles, and decision-maker details.

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Find phone numbers for decision makers via Lemlist using Chrome DevTools MCP. Takes LinkedIn profile URLs and returns phone numbers where available.

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Frontend stack decisions, Cloudflare deployment patterns, component systems, and internal tools architecture. Use for framework selection, deployment strategy, design system bridging, shadcn setup. Activate on "frontend architecture", "tech stack", "Cloudflare Pages", "component library", "internal tools", "shadcn setup". NOT for writing CSS (use frontend-developer), design critique (use design-critic), or backend APIs.

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An innate exploratory drive that guarantees no graph node remains permanently invisible to an agent population. After a warm-up period (step > 3), each agent fires a coverage scan with probability |unseen_nodes| / |all_nodes|. When triggered, the agent teleports directly to the globally unseen node with the highest belief uncertainty, works it, and returns control to the normal action-selection loop. Probability is self-annealing: it starts near 1.0 when almost everything is unseen and decays toward 0.0 as coverage saturates, requiring no manual schedule. The mechanism is "innate" (hard-coded into the agent step loop) as distinct from the "adaptive" EFE path, which requires local neighbors and learned precision weights. Together they form a two-layer exploration architecture: adaptive for locally-guided foraging, innate for topological completeness.

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Principled action selection for autonomous agents using Expected Free Energy (EFE) under the Active Inference framework. Each candidate action is scored as G(n) = w_prag * (-E[p_n] * damping) + w_epist * (-Var[p_n] * novelty(n)), where agent beliefs are maintained as per-node Beta distributions and updated via Bayesian observation. Selection is a softmax over -gamma * G, making exploration an automatic consequence of uncertainty rather than a hardcoded epsilon. Precision weights w_prag and w_epist self-adapt via surprise history, shifting the agent between exploitation and exploration regimes without any manually tuned schedule.

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Implementing distributed tracing with W3C Trace Context — the byte-precise `traceparent` and `tracestate` header formats, OpenTelemetry's W3CTraceContextPropagator as the modern default (replacing X-B3-* and X-Datadog-*), head vs tail sampling, and HTTP→SQL trace propagation via sqlcommenter. Grounded in the W3C REC and OpenTelemetry specs. NOT for OTel metrics/logs pipelines, vendor backend setup (Datadog/Honeycomb/Jaeger/Tempo), or browser RUM tracing.

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Schema design, normalization decisions, indexing strategies, and migration safety for relational databases. Use when designing tables, choosing between normalize vs denormalize, selecting index types, planning zero-downtime migrations, or setting up connection pooling. Activate on "schema design", "normalization", "denormalization", "foreign key", "index strategy", "migration", "connection pooling", "composite key", "surrogate key", "soft delete", "polymorphic association". NOT for PostgreSQL-specific query tuning or EXPLAIN analysis (use postgresql-optimization), NoSQL or document database design, or cloud database provisioning.

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Investigates DAG incidents with a precise top event, timeline, competing hypotheses, barriers, propagation, and bounded recovery. NOT for automated root-cause classification, trace collection, or performance profiling.

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Debugs recorded DAG lineage through historical inspection, deterministic decision replay when prerequisites hold, and clearly labeled counterfactual or live re-execution. Activate on "debug DAG", "replay execution", "inspect node state", "compare runs", or "execution diff". NOT for live monitoring (use dag-runtime + websocket-streaming), failure analysis (use dag-ops), or general code debugging.

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。

Methodology for eliciting expert knowledge about cognitive processes, decisions, and mental models in complex tasks

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Tracks cumulative LLM costs across DAG execution and makes real-time decisions to stay within budget. Downgrades models, skips optional nodes, or stops early when cost exceeds thresholds. Use when managing execution budgets, analyzing cost breakdowns, or optimizing model routing for cost. Activate on "cost budget", "too expensive", "reduce cost", "cost optimization", "model downgrade", "budget exceeded". NOT for LLM model selection logic (use llm-router), pricing comparisons across providers, or billing/invoicing.

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Architecture-level decisions for putting a CDN in front of a web app's static assets, API responses, and file/media delivery: provider selection (hyperscaler vs. specialist $/GB), signed-URL / tokenized access for gating private content at the edge, cache invalidation strategy (versioned URLs vs. purge APIs), origin shield / tiered caching for high-miss-rate content, and CDN-layer geo-restriction. Use when deciding which CDN to put in front of a new or growing service, when private/paywalled files need edge delivery without an origin round-trip per request, or when purge/invalidation behavior is being designed rather than debugged. NOT for HTTP cache-control header syntax, Vary, stale-while-revalidate, or surrogate-key purge mechanics (see `cdn-cache-control-headers`); NOT for Cloudflare-specific product configuration once Cloudflare is already chosen (see `cloudflare`); NOT for video-specific transcoding, adaptive bitrate ladders, or streaming-protocol packaging (see `managed-video-streaming-pipeline`).

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。