Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/careerchain-ys/stddnpx agentmods add skills/careerchain-ys/stdd/run-e2eWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/careerchain-ys/stdd/run-e2e)<a href="https://agentmods.dev/skills/careerchain-ys/stdd/run-e2e"><img src="https://agentmods.dev/badge/skills/careerchain-ys/stdd/run-e2e/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/careerchain-ys/stdd/run-e2e"><img src="https://agentmods.dev/badge/skills/careerchain-ys/stdd/run-e2e.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00162 | $0.02140 |
| Opus 5.5 | $0.00065 | $0.00856 |
| Sonnet 5.5 | $0.00032 | $0.00428 |
| Haiku 4.5 | $0.00016 | $0.00214 |
Grade A, and why
run-e2e scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
devcontainer exec --workspace-folder <WORKTREE_DIR> --override-config <OVERRIDE_CONFIG> bash -c "curl -s -o /dev/null -w '%{http_code}' http://localhost:<APP_PORT> && echo ' <apps[].id> OK' || echo ' <apps[].id> NOT RUNN How it starts
The opening of the file, as written. The whole thing — 181 lines — stays where its author put it; the contents beside it link to each section on GitHub.
前提: 本スキルは git worktree + devcontainer を用いた並列実行フローを前提とする。
.stdd.config.ymlでworktreeプラグインを併せて有効化すること。
Worktree上でE2Eテストを実行するスキル
worktree環境のdevcontainer内でE2Eテストをセットアップ・実行する。 稼働中のdevcontainerがあればそれを使い、なければ新規作成する。
1. 稼働中のworktree devcontainerを検索
for i in 1 2 3 4; do
wt_dir="{{worktree.base_path}}/worktree-${i}"
container=$(docker ps --filter "label=devcontainer.local_folder=${wt_dir}" --format "{{.Names}}" 2>/dev/null)
if [ -n "$container" ]; then
echo "FOUND:${i}:${container}"
fi
done
判定ロジック
FOUND:Nが出力された場合 → そのINSTANCE_ID=Nのdevcontainerを使用。ステップ2へ進む- 何も出力されない場合 → 稼働中のworktree devcontainerなし。ステップ1-Bで新規作成する
1-B. Worktree+devcontainerを新規作成
稼働中のworktree devcontainerがなかった場合のみ実行する。
空きINSTANCE_IDを特定
for i in 1 2 3 4; do
wt_dir="{{worktree.base_path}}/worktree-${i}"
if [ ! -d "$wt_dir" ]; then
echo "AVAILABLE:${i}"
break
fi
done
最初に見つかった空きIDを使用する。空きがない場合はユーザーに不要なworktreeの破棄を案内して中断する。
worktree作成
./scripts/create-worktree.sh -b claude/e2e-run -i <INSTANCE_ID>
devcontainer起動
devcontainer up --workspace-folder {{worktree.base_path}}/worktree-<INSTANCE_ID> --override-config {{worktree.base_path}}/worktree-<INSTANCE_ID>/.devcontainer/devcontainer.override.json
起動完了を待ってからステップ2へ進む。
2. 環境変数の算出
特定したINSTANCE_IDから以下を算出する:
WORKTREE_DIR = {{worktree.base_path}}/worktree-<INSTANCE_ID>
OVERRIDE_CONFIG = ${WORKTREE_DIR}/.devcontainer/devcontainer.override.json
# ポート計算(INSTANCE_IDに応じてオフセット)
# 各アプリのベースポートは .stdd.config.yml の apps[].port を用いる(apps[] の数だけ繰り返す)
APP_PORT(<apps[].id>) = <apps[].port> + INSTANCE_ID * 100 (例: base=3000, ID=1 → 3100, ID=2 → 3200)
SUPABASE_API_PORT = 54321 + INSTANCE_ID * 1000
SUPABASE_DB_PORT = 54322 + INSTANCE_ID * 1000
以降のdevcontainer execコマンドはすべて以下の形式で実行する:
devcontainer exec --workspace-folder <WORKTREE_DIR> --override-config <OVERRIDE_CONFIG> bash -c "COMMAND"
重要: 変数代入をコマンドの先頭に置かないこと。必ず devcontainer exec からコマンドを始める(auto-approve対象パターンに合致させるため)。
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2mo ago First seen · 181 lines · 162 tokens per session scan A cdaa08682029
run-e2e is a skill published in the GitHub repository careerchain-ys/stdd (2 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 162 tokens to every session and 2,140 once invoked, about $0.0006 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
verify-omnigent
Drive Omnigent the way a user does and prove a behavior with recorded evidence, using an isolated server, runner, and mock model plus a feature map of every user entry point. Load before reproducing a user-facing bug, before claiming a fix works, or when reviewing whether a change covered every surface (session vs.…
test-master
Generates test files, creates mocking strategies, analyzes code coverage, designs test architectures, and produces test plans and defect reports across functional, performance, and security testing disciplines. Use when writing unit tests, integration tests, or E2E tests; creating test strategies or automation…
eval-dataset-design
A guide to creating reliable test sets for AI agents. Each evaluation task includes the request, the simulated user's behaviour, a reset starting state, and a success check that can be independently verified.
dogfood
Systematically explore and test a mobile app on iOS/Android with agent-device to find bugs, UX issues, and other problems. Use when asked to dogfood, QA, exploratory test, find issues, bug hunt, or test this app on mobile.
cua-driver
Use Cua Driver for desktop or browser tasks that are awkward or unavailable through Bash/APIs, or when the user explicitly wants GUI interaction: app testing, visual bug reproduction, form filling, calendar entry, screenshots, and demo recording. Also covers Cua setup; not OpenAI Codex Computer Use or web research.
test-warp-ui
Guides testing Warp UI features and changes using the computer use tool. Use this skill only when computer-use testing was requested (explicit request or accepted offer) and the computeruse tool is available to the agent. Covers launching Warp and verifying UI behavior.