Export Codex rollout/session JSONL as a self-contained HTML report that can be opened offline. Use when a user provides a complete session file path or Codex session ID, or asks to export a Codex session to HTML, generate a session report, or convert a rollout to HTML. Supports exact full-ID lookup in the CODEX_HOME sessions and archived_sessions directories.
インストール方法を見る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。
Use the bundled script for path resolution and HTML export. Do not rewrite the parser ad hoc.
codex-session-<session-id>.html in the current working directory.python3 "$SKILL_DIR/scripts/export_session.py" "$SESSION_OR_ID" \
--output "$OUTPUT_HTML"
Session source and
HTML report exported.SKILL_DIR is the current skill's real directory. Do not assume the caller runs from the
repository root.
Accept only a complete UUID, not a prefix or fuzzy ID. Search these locations in order by default:
$CODEX_HOME/sessions/**/rollout-*-<session-id>.jsonl$CODEX_HOME/archived_sessions/**/rollout-*-<session-id>.jsonl~/.codex when CODEX_HOME is unsetIf the session is under an isolated benchmark agent-home/.codex, pass that home explicitly:
python3 "$SKILL_DIR/scripts/export_session.py" "$SESSION_ID" \
--codex-home "/absolute/path/to/agent-home/.codex" \
--output "$OUTPUT_HTML"
If no session is found, report the searched CODEX_HOME. If multiple files match, do not choose one; ask the user for a complete path.
codex-session-analyzer when the user needs an explanation of failures, timing, or tool paths.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
Author or expand browser-agent benchmark tasks in data/real_world_bench.json (Odysseys schema, per-rubric graded). Use when creating, adding, or expanding real-world browser tasks, writing rubrics, or picking bot-friendly sites — every task MUST be verified runnable through ego-browser before it is written.
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。
Analyze Codex provider rollout JSONL and ego-benchmark-harness run artifacts to identify slow or failed individual tasks, tool retries, token/cost anomalies, and differences in agent execution paths. Use when users ask to analyze a Codex session or rollout, explain why a Codex task was slow or failed, inspect a Codex provider execution trace, compare Codex runs, count function_call or tool output events, or investigate agent-home/.codex/sessions.
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。
Verify a tested web agent's real behavior from its recorded Pi or Codex session and selected screenshots before scoring. The judge prompt provides absolute artifact paths; use targeted queries to compare actual outputs, errors, and the final answer with the recorded behavior, then return only the verdict JSON. Only for ego-bench judging—never to drive a browser or rerun the agent.
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。
Generate self-contained HTML/CSS benchmark and comparison charts in an ego-inspired Browser-Native Editorial style. Use for blue-white product metrics, model benchmarks, A/B comparisons, efficiency reports, difficulty breakdowns, and compact technical data panels.
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。
ego-browser (ego-lite) is a Chromium-based browser that gives AI agents a CLI-accessible Node.js runtime for driving a real browser. Use this skill whenever the user needs to interact with a website opening pages, filling forms, clicking buttons, taking screenshots, extracting page data, testing web apps, logging into sites, automating browser operations, or any other browser automation task. Triggers include requests to "open a website", "visit a URL", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "extract content from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction. Also used for exploratory testing, dogfooding, QA, bug hunting, or reviewing app quality. Prefer ego-browser over any built-in browser automation, web fetch, or other web tools.
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。
Architecture design and governance protocol for system structure, boundaries, contracts, APIs, schemas, migrations, rewrites, and long-lived codebase health. Use when designing or reviewing architecture, planning structural refactors or migrations, governing tech debt, or containing rewrite risk. Keeps the load-bearing spine small, owned, checked, and deletable over time.
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。