ソース情報
- リポジトリ
- JPeetz/agent-skills
- ソースの最終更新活動
- 2026年8月31日 06:31
- 検出された SKILL.md の言語
- 英語
- スター
- 5
- フォーク
- 1
インストール方法
デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。
ソースファイルを確認
インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
メニュー
デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。
インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
直接コマンドでは確認用 Prompt が省略されます。実行前にソースを確認してください。
npx skills add https://github.com/JPeetz/agent-skills --skill model-routing-cost-optimizerコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
ローカルで確認しますか?SkillsMP が現在取得できるファイルをダウンロードできます。
Design-first API development skill. Generates OpenAPI 3.1 specifications, enforces REST design best practices, validates endpoints, handles versioning, pagination, error formatting, authentication patterns, rate limiting, and idempotency. Activates when users say "design an API", "create OpenAPI spec", "API endpoint", "REST API design", "API contract", "Swagger/OpenAPI doc", "API versioning", "rate limiting", or "API security". Covers REST, GraphQL schema design, and gRPC proto generation with cross-protocol consistency.
AI-powered GraphQL API design, implementation, and optimization. Covers schema-first design, resolver architecture, query optimization with DataLoader for N+1 prevention, mutation patterns with idempotency, real-time subscriptions, Apollo Federation for distributed graphs, security hardening (depth limiting, rate limiting, authz), and production performance (persisted queries, caching, CDN integration). Primary keyword clusters: GraphQL schema design best practices, Apollo Federation subgraph patterns, DataLoader N+1 query optimization, GraphQL security depth limiting rate limiting, GraphQL persisted queries performance, GraphQL subscription real-time patterns, GraphQL error handling union types, GraphQL pagination relay cursor connection, GraphQL caching strategies production, GraphQL resolver architecture patterns. Designed for agentic platforms — Claude Code, Codex, Cursor, Gemini CLI, OpenClaw, GitHub Copilot, Windsurf, and OpenCode.
Use this skill when an agent needs to call a website's hidden or undocumented API: capture real browser requests into a HAR file, derive the exact request shape, build a clean replayable client, verify it outside the browser, and reuse the verified client. Activates when a backend has no documented API, or when you must reproduce the precise request (method, URL, headers, JSON body) of an action you can only perform in a browser. Authorized use only — never to bypass auth or bot detection.
SKILL.md を表示中
| name | model-routing-cost-optimizer |
| description | Use when routing tasks to the cheapest fitting model tier. |
| version | 1.0.0 |
| license | MIT |
| author | Skill Foundry |
| platforms | ["linux","macos"] |
| metadata | {"tags":["model-routing","cost-optimization","model-tiering","llm-cost","subagent-routing","provider-agnostic"],"complexity_level":"intermediate","similar_skills":["model-hierarchy"],"related_workflows":["classify_task_complexity","select_cost_appropriate_model","avoid_routing_anti_patterns"]} |
Route every agent task to the cheapest model that can still do it well. This skill
establishes a three-tier model hierarchy (cheap / mid / premium), a task classifier
that buckets work into ROUTINE / MODERATE / COMPLEX, and the decision rules that
turn a description into a model pick. It does not enforce any one provider — the
tier prices live in references/model-pricing.md so the body stays provider-
neutral — and it does not cover local quantized inference.
The core belief is blunt: most agent work is routine, and routing routine work to a premium model is pure waste. Down-tier by default; escalate on evidence, never on habit.
Use this skill when you want to:
Don't use for: fine-tuning a model, model evaluation or benchmarking, serving infrastructure (vLLM / TGI), or any task where correctness risk is high and you need the best possible reasoning regardless of price. If the work is irrecoverable or safety-critical, stop tiering and use the premium model.
model config of OpenClaw / Claude Code / Codex).references/model-pricing.md is illustrative, so refresh it against provider
docs before relying on a number.scripts/classify_task.py is stdlib-only.debug, architect,
design, security, adversarial, ambiguous push to Tier 3. Routine words
like read, fetch, check, format, status, list push to Tier 1.| Bucket | Heuristic | Typical Examples |
|---|---|---|
| ROUTINE → Tier 1 | Single-step, deterministic, no judgment | file I/O, heartbeat, status check, lookup, formatting, URL fetch |
| MODERATE → Tier 2 | Multi-step but well-scoped | code-gen on known patterns, summarization, draft writing, data transforms |
| COMPLEX → Tier 3 | Ambiguous, multi-approach, high-stakes | multi-step debugging, architecture, security review, long-context reasoning |
| Concern | Move |
|---|---|
| Task is routine | Tier 1 |
| Task is moderate | Tier 2 |
| Task needs image / vision | vision-capable model (never a text-only tier-1) |
| Task already failed on cheap | move up (escalation), never down |
| Heartbeat / cron / monitoring | always Tier 1 |
| Sub-agent spawn | default Tier 1 unless clearly moderate+ |
Each step ends with a checkable completion criterion.
State the task. Write the task in one line, including whether it needs images and whether a cheaper model already tried. Completion: you have a task statement with its vision and prior-failure flags.
Classify. Run python3 scripts/classify_task.py "<task>" in the terminal,
or read the bucket heuristics above by hand.
Completion: you have a label of ROUTINE, MODERATE, or COMPLEX.
Apply the vision override. If the task is vision-requiring, restrict the model choice to vision-capable options, ignoring any text-only option entirely — a text-only model cannot take image input at any price. Completion: the chosen model can actually accept image input.
Apply escalation and signal overrides. If a prior failure exists, go one tier up from the classifier result. If explicit complex signals appear, go to Tier 3. Completion: the final tier is never lower than the classifier would have produced.
Name a concrete model. From the provider's resolvable list, pick the cheapest model of the final tier that meets that tier's bar. Completion: you can name a concrete model, or a route to find one.
Run the anti-pattern sweep. If this is a sub-agent, heartbeat, cron, file I/O, or routine batch, drop it to Tier 1 unless the classifier returned MODERATE or above. Completion: no heartbeat/cron work remains on a premium tier.
scripts/classify_task.py returns Tier 1 for a routine phrase,
Tier 2 for a moderate phrase, and Tier 3 for a complex phrase.references/model-pricing.md exists and contains the pricing table and the
"prices change, check provider docs" caveat.