Web・iOS・Androidの画面を、読み上げやキーボード操作に対応させ、ラベル、配色、操作対象の大きさなどをWCAG 2.2に沿って設計・点検するスキル。
- アイコンボタンの説明を付けたいとき
- キーボード操作とモーダルの点検
- コントラストや操作対象の大きさの確認
Itôで予約済みのGPUを使ったモデル配信の相談に対し、現状の未対応範囲と将来必要な確認事項を整理するスキル。現在は配信を開始せず、制約を案内します。
原文Inspect the availability of model serving on a completed Itô compute booking and, when the canonical backend becomes available, hand off an explicitly confirmed serving manifest. Use after ito-compute has booked GPU nodes and the user asks for an OpenAI-compatible endpoint, ito-serve, hosted Kimi, or self-hosted open-weights inference. ECC implements no serving stack of its own.
インストール方法を見るItôでGPUを予約した後、モデルを動かしてAPI経由で利用する「モデル配信」の対応状況と必要条件を整理します。資料上は配信機能が未実装のため、その不足を案内し、元のエージェントへ処理を戻す役割です。将来の引き渡しに必要な設定と確認手順も記載されています。
ito-computeで予約を済ませ、OpenAI互換の接続先やKimiの配信、公開されたモデル重みを使う推論環境を相談するときに向いています。モデルの版、実行エンジン、公開範囲、保存容量、実行時間、追加費用の上限など、将来レビューすべき項目を把握できます。
現状は認証やコマンド実行の前に停止します。SSH接続、モデル重みの取得、エンジン起動、接続先の公開、予約や支払いは行いません。将来の実行には、サーバーで検証した予約資格と、設定・費用に結び付いた個別の確認が必要です。記載された配信コマンドや運用手順は将来の仕様です。
この紹介文は、公開されている SKILL.md をもとに AI(Claude Haiku)が作成しました。正確な仕様は下の原文を確認してください。
インストールする前に、エージェントに与えられる指示の中身を確認できます。
ito-inference is the sole canonical ECC skill for inference serving on Itô
compute. Requests naming ito-serve route here; do not create or install a
second ito-serve skill. ECC never SSHes to nodes, downloads weights, launches
an engine, or exposes an endpoint; it never books, reserves, or spends.
Managed serving is unavailable today. The ECC bridge exposes only login,
auth, find, status, and explicitly gated evals. It has no serve verb.
The canonical runtime documents inference only as an unsupported compatibility
probe; ECC does not invoke or depend on it. The MCP surface exposes only auth,
find, and status. The locally enforceable guarantee is that ECC rejects serve
before resolving or spawning the credential-bearing canonical client.
Therefore stop before authentication or any command invocation. Report the
missing capability and return to the originating agent. Never substitute a
local runner, SSH helper, browser workflow, purchase endpoint, or any untracked
local ito-serve draft.
When serving is implemented, its first gate is a server-verified completed booking. Harness memory, an RFQ, a quote, node IPs, or SSH access are not proof of entitlement. The backend must return fresh serving eligibility bound to the authenticated account, booking, GPU topology, region, fabric, term, and model policy. Expired, revoked, mismatched, incomplete, or already-released bookings fail closed before confirmation.
The intended command name is serve; inference may remain only as an
explicitly deprecated compatibility alias after the production contract lands.
The future handoff must be equivalent to:
ecc ito serve \
--booking <server-verified-booking-id> \
--manifest <absolute-reviewed-json-file> \
--confirmation-ref <opaque-non-authorizing-reference> \
--idempotency-key <stable-retry-key> \
--json
The reviewed manifest must identify the model revision, engine and version, quantization, tensor/pipeline topology, endpoint exposure policy, artifact checksums, storage ceiling, runtime limits, optional TTFT/TPOT objectives, and maximum incremental cost. No raw API key, SSH key, node password, or bearer token belongs in arguments, manifests, logs, MCP results, or chat.
The client must canonicalize the manifest path, reject symlinks, open a regular file without following links, require appropriate ownership and restrictive permissions, enforce a bounded size, and hash bytes from the opened descriptor. That digest must exactly equal the digest bound into confirmation before any workload mutation. A path swap, digest mismatch, oversized file, or mutable unsafe file fails closed.
The canonical API—not ECC—must own workload creation and return structured JSON
with ok, live_api_contacted, notice, and either data or error. Serving
data must include stable booking, workload, manifest, and idempotency IDs plus a
state enum; it must not claim an endpoint is live until health and model checks
pass. Errors must include a stable code and safe message without secrets.
Before workload creation, require all of the following:
Authentication is identity, not workload authority. A login, API key, quote, or completed booking never substitutes for the serving confirmation. Inspection and plan generation must not create a workload. Cancel and cleanup are separate mutations with their own scoped confirmation and idempotency boundaries.
The production surface is incomplete until the same canonical client exposes tenant-scoped status, logs, metrics, cancel, and cleanup operations. Every operation needs bounded connect and overall timeouts, revocation-aware errors, and structured output. After an ambiguous transport failure, query status by the idempotency key before retrying; never create a second workload merely because the first response was lost. A revoked credential stops polling and returns control to the originating agent without starting login automatically.
Only report ready after endpoint health, model identity, and canary inference
all pass. Report intermediate and terminal failure states honestly. Cleanup must
be observable and must not release or modify the underlying booking unless that
separate economic action was explicitly authorized.
These stages describe the future backend, not code that exists in ECC:
Until every gate and lifecycle operation above exists in the canonical runtime, this skill remains a fail-closed availability check and documentation handoff.
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Web・iOS・Androidの画面を、読み上げやキーボード操作に対応させ、ラベル、配色、操作対象の大きさなどをWCAG 2.2に沿って設計・点検するスキル。
AIエージェントの不調を、指示・記憶・ツール実行・画面表示など12の層から調べるスキル。コードやログを根拠に原因を整理し、重要度順の指摘と修正案をまとめます。
実際の開発課題で複数のコーディングエージェントを比較するスキル。成功率、取得可能なAPI費用、所要時間、繰り返し実行の安定性を測り、選定や更新後の評価に使えます。
AIエージェントが使うツールの種類や入出力、エラーからの復帰手順を設計・見直します。文脈の情報量も整理し、作業完了率や再試行回数で改善を評価します。
AIエージェントが失敗や同じ操作を繰り返す原因を、エラーと実行状況から整理します。小さな復旧操作を試し、結果と根拠を引き継げる報告にまとめるスキルです。
AIエージェントの失敗や同じ操作の繰り返しを記録し、原因の切り分け、小さな復旧操作、結果の報告まで進める手順を示して、根拠のある再試行につなげるスキル。