AIエージェントの作業時間を、進行中の作業なら実測した進捗と残作業から見積もり、これからの作業なら作業量や基準データから算出します。利用者向けの時刻も指定コマンドで確認します。
- 実行中の作業の残り時間を知りたいとき
- 新しい依頼の所要時間を見積もりたいとき
- 終了予定や現在時刻を利用者向けに示したいとき
E2Eテストで予期しない失敗が起きたとき、製品の不具合か環境の問題かを切り分け、原因を見える形にして再発確認用のチェックへつなげるスキルです。
Classify one unexpected end-to-end test failure as a product defect or an environment fault needing a new preflight check. MANUAL-ONLY: load when the suite runner names a specific failure. NEVER auto-load from context.
実行スコア 未検証
インストール方法を見る環境の事前チェックがすべて通っているのに、指定されたE2Eテストの行が失敗したり、null・空・ゼロなど理由の分からない結果になったりした場合に、原因を切り分けます。いきなり原因を推測せず、成功と到達、空と読み取り不能など、ひとつにまとめられた可能性のある結果を分けて考えます。まず製品の動作ではなく、空の結果に理由を示す情報を加え、再実行してから診断します。結果はsource、environment、stuck、harnessのいずれかに分類します。
事前チェックでは予測できなかったテスト失敗の原因を調べたいときに向いています。製品の不具合と、環境設定やテスト実行基盤の問題を区別したい場合や、「結果なし」が本当に対象なしなのか、処理自体が実行されなかったのか判別しづらい場合にも使えます。
テスト実行者が指定した失敗に対して手動で読み込むスキルです。環境の事前チェックが通っていない場合や、原因が明らかな通常の製品不具合では、この診断手順の対象外です。診断後はe2e-environment-preflightを使って新しいチェックを作成・記録します。
この紹介文は、公開されている SKILL.md をもとに AI(OpenAI Codex)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。
Terms are as in e2e-environment-preflight: a row is one acceptance check in the test plan, a red is a row that failed or gave no answer, a journey is one end-to-end scenario made of rows, the suite is all the rows of a journey, and preflight is the set of environment checks that must be green before the first row runs.
Never auto-triggered, and normally never needed. One caller — the agent running the suite — and one moment:
The
e2e-environment-preflightchecks were all green, and a row went red anyway — or produced a null, an empty, a zero, or a silence nobody can yet explain.
If preflight was not green, the suite should not have started. If a red is an ordinary product failure with a clear cause, fix it and move on; this is not the ceremony for an ordinary bug. This is the residual after the gate has done its work — a small fraction, and one that shrinks every time move 3 is honoured.
The preflight handles the failures somebody could enumerate in advance. What is left is the class where the failure produced nothing rather than an error, and the nothing appeared far from its cause. One null can mean five different things across five runs of the same suite; "no results" can mean the query matched nothing or the search never ran.
The hours such a red costs are spent attributing an absence — arguing about what a missing thing meant, from evidence that never contained the answer. Guessing feels faster than instrumenting and is not: a wrong guess costs a full suite run to disprove, and a plausible wrong guess is worse than an implausible one because it stops the search.
The deeper version, and the one worth carrying: the failure is usually a two-part outcome collapsed into one verdict. An HTTP 202 "accepted" means the request was queued, not that the work was done, and both caller and monitor read it as success until someone looks. A cleanly stopped service records an exit code that looks like a crash. In each case the evidence was there, in plain words, and nobody split it.
So the first question is never "why did it fail" — it is "what are the two halves of this result, and which half am I looking at?"
These are defaults; a project may choose otherwise and record it in its local binding.
Stop proposing causes. Write down first the two things this result could be a collapse of: succeeded versus reached, stopped versus crashed, empty versus unreadable, absent versus never-asked, zero-matches versus source-missing.
The first commit after this kind of red does not touch product behaviour. It changes whatever produced the empty so that it says why it is empty — in the artifact, as a field, not in a log line, which is read only by someone who already suspects the answer.
result: null
→ result: {value: null, because: "<the reason, as a code>"}
Then re-run. Only now diagnose. This inverts the usual instinct and that is the point: instrumenting first looks slower for one iteration and is faster from the second onward, because every future occurrence of this absence becomes a single read instead of an investigation.
Two shapes to watch for here, because both report health while broken:
Feed every new check a known-wrong input and watch it go red. Instruments fail toward "healthy".
A diagnosis is not the end. The finding becomes a new preflight check for that system and a line in that system's primer (see e2e-environment-preflight), or this red is not closed.
Invoke e2e-environment-preflight to write it, and record the red in the red scoreboard described in e2e-environment-preflight, with its bucket and the new check's id:
{"row":"<row-id>","bucket":"environment","note":"…","owed_check":"<check-id>"}
This is the only mechanism that makes the residual shrink. Without it the same class of absence is rediscovered every campaign and the gate never gets stronger — the process runs, everyone is diligent, and the hours are spent again. Hitting the wall is allowed; coming back with nothing durable is not.
Every red gets exactly one bucket — source, environment, stuck, harness. Be honest about
stuck: a run that produced no verdict at all is not a pass and not a skip, and burying it is how
a campaign reports coverage it never had.
The counts are the point. A journey whose reds are mostly source found bugs. One whose reds are
mostly environment or stuck measured its own setup, and the fix for that is upstream in the preflight — never another round here.
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。
ログインなしで書き込めます。使ってみた感想、質問、動かなかった環境などを気軽にどうぞ。
まだコメントはありません。最初のコメントを書いてみませんか。
AIエージェントの作業時間を、進行中の作業なら実測した進捗と残作業から見積もり、これからの作業なら作業量や基準データから算出します。利用者向けの時刻も指定コマンドで確認します。
成功と報告された処理が、意図したデータ変更や外部呼び出しを本当に起こしたかを、入口から出力先まで追い、実行箇所と観測できる数値で確かめます。
離席中に記録された完了作業、停滞中の項目、返答待ち、利用枠の変化を1ページに整理し、戻ったときの状況把握を助けます。
相互レビューとエージェント自身の自己点検で見つかった問題を記録から集計し、期間やモデル別に並べて確認できるようにします。
担当者が成果物や対象外の範囲、確定済みの判断、合否を確認できる方法を明文化した作業憲章を作成・改訂し、既存憲章の不整合も点検します。
Claude CodeからCodexへ実装作業を引き継ぎ、Shell、TypeScript、Python、Rustの言語に応じた手順でコードを書かせます。