「model」の検索結果

Author and edit Skmtc generators — packages that project an OpenAPI domain model into application code. Method: clone the nearest stock generator, then apply the engine rules imitation can't teach. Assumes zero prior Skmtc knowledge. Use when asked to "write a skmtc generator", "author/clone/customize gen-x", "add a field type", "change export paths", "add enrichment options", or when editing generator source. ALWAYS pair with the target language's skill (skmtc-lang-typescript).

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Use the Skmtc CLI to scaffold projects, install or clone generators from JSR, configure schema sources and enrichments, and produce code artifacts from an OpenAPI v3 or GraphQL SDL schema. Teaches the workspace mental model (`<root>/.skmtc/<project>/`, client.json, bundle, manifest) and the agent contract (strict text / strict JSON modes, exit codes, recipe errors, `agent-context` + `doctor`); the command surface itself is discovered from the binary — `skmtc --help`, `skmtc <cmd> -h` — rather than carried in this skill. Use this skill when the user asks to "run skmtc", "generate code from an OpenAPI schema", "install a skmtc generator", "scaffold a skmtc project", "watch a skmtc project", "configure enrichments", "publish a stack", "deploy to skmtc-hub" (the command is `publish`; there is no `deploy`), "skmtc in CI", or invokes any CLI subcommand. For *authoring* a generator package (Projections, Snippets, transform functions), defer to `skmtc-generator`. When something is broken (no output, wrong output, error messages, a failed bundle build), verify before proposing a fix: read the manifest and the parse issues, and reproduce the failure first.

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MCP (Model Context Protocol) server building principles. Tool design, resource patterns, best practices.

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Squad branching model: dev-first workflow with insiders preview channel

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Shifts Layer 3 model selection to cost-optimized alternatives when economy mode is active.

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Complete MCP engineering work on the live path: build or repair servers and Apps, debug stdio or Streamable HTTP, migrate protocol/SDK/transport versions, reconcile source with package and activated runtime, inspect capability/effect authorization, and verify named-host claims without promoting weaker evidence. Use for real Model Context Protocol implementation, review, migration, runtime, or host-integration tasks. Do not use for ordinary non-MCP APIs, generic frontend work, or incidental product copy.

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Retrospective learning from a completed experience. Takes a project, incident, decision that played out, or time period; gathers ground truth (observations) separately from recollections; spawns reflectors applying different lenses (what-worked-vs-got-lucky, what-didn't, what-surprised, system-rewards-vs-intent, decisions-that-aged, what-to-tell-past-self, patterns-that-recur) in isolation; synthesizes into updated mental models as first-class output. Produces feedback only — no code, no tickets, no artifacts.

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Apply these opinionated TypeScript conventions whenever writing or reviewing TypeScript: branded types for domain modeling, discriminated unions for state, `satisfies` over `as`, const objects instead of enums, type-only imports, and the strict tsconfig baseline beyond `strict: true`. Also use for monorepo project references and incremental build setup.

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Resolve CodeRabbit review feedback on a GitHub pull request. Collects every open CodeRabbit finding (inline threads, plus the nitpick and outside-diff findings packed into review bodies), judges and fixes each one against the real code with subagents, unifies fixes that share a pattern, commits locally, asks before pushing, then shows every reply draft linked to its finding and posts only on approval. Runs only on top-tier models. Use on "resolve the CodeRabbit feedback", "address coderabbit comments", "answer the rabbit", "fix what CodeRabbit flagged on

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Diagnose common cqs issues — stale index, model download, schema mismatch, connection issues.

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Vue 3 Composition API, script setup macros, reactivity system, and built-in components. Use when writing Vue SFCs, defineProps/defineEmits/defineModel, watchers, or using Transition/Teleport/Suspense/KeepAlive.

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Evidence-first ECC Tools burn and billing audit workflow. Use when investigating runaway PR creation, quota bypass, premium-model leakage, duplicate jobs, or GitHub App cost spikes in the ECC Tools repo.

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FastAPI patterns for async APIs, dependency injection, Pydantic request and response models, OpenAPI docs, tests, security, and production readiness.

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Playwright E2E testing patterns, Page Object Model, configuration, CI/CD integration, artifact management, and flaky test strategies.

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Evidence-first revenue, pricing, refunds, team-billing, and billing-model truth workflow for ECC. Use when the user wants a sales snapshot, pricing comparison, duplicate-charge diagnosis, or code-backed billing reality instead of generic payments advice.

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Use this skill when inspecting Blender characters, rigs, poses, animation retargeting, ground contact, facing direction, or model-vs-motion alignment where screenshots alone are not enough.

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Persistent per-project memory for Claude Code. Auto-loads project context on session start, tracks sessions with git activity, and writes to native memory. Commands run deterministic Node.js scripts — behavior is consistent across model versions.

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Cost optimization patterns for LLM API usage — model routing by task complexity, budget tracking, retry logic, and prompt caching.

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Survey a codebase's animation and motion code as a senior motion advisor, then produce a prioritized audit and self-contained implementation plans for other agents (or cheaper models) to execute. Read-only on source code — it plans improvements, it does not apply them. Use when the user asks to "improve the animations", "audit the motion", "make this app feel better", or wants a roadmap of animation fixes rather than a review of a single diff.

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Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing.

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Regression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots where the same model writes and reviews code.

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Engineering operating model for teams where AI agents generate a large share of implementation output.

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Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).

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Generate images, videos, TTS, and music through Pixazo (one API key, 80+ models). Use when Decisions has a Pixazo key or the pixazo MCP is connected.

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。