This skill runs a fresh Codex request through a local OpenAI-compatible proxy, inspects the captured log, and summarizes what Codex actually sent to the provider.

Use it when you need to debug:

• request size and payload shape
• injected instructions or system_prompt
• exposed tools and provider or model overrides
• profile differences from ~/.codex/config.toml
• TTFT, warm generation speed, and near-context behavior

The skill is designed to execute the workflow end-to-end and return findings.

• It defaults to capture only when the request is underspecified.
• It inspects the resulting log before answering.
• It does not stop at printing commands unless the user explicitly asked for instructions only.
• Context dumping is opt-in because sanitized captures may still contain sensitive user content.

The diagnostic run uses the model named by the user, with exact or approximate matching against configured models and profiles.

• If the requested model is unavailable or unloaded, the default fallback is gpt-5.3-codex-spark.
• If Spark is unavailable, the next fallback is gpt-5.4 with medium reasoning.
• The final summary reports the requested model, resolved model, actual model, and any fallback reason.

• Capture request metadata: run a fresh proxied codex exec, inspect the log, and summarize request shape, tool surface, overrides, and errors.
• Capture sanitized context: use --dump-context to inspect system_prompt, instructions, and sanitized input.
• Benchmark throughput: run the bundled multi-phase benchmark to measure cold TTFT, warm speed, and near-context behavior.

• SKILL.md with the operational contract and workflow
• locales/metadata.json with localized user-facing metadata
• .skill_triggers/<locale>.md as the single source of truth for localized trigger catalogs
• scripts/codex_proxy.py for request capture and forwarding
• scripts/inspect_proxy_log.py for log inspection
• scripts/run_codex_benchmark.py for throughput benchmarking
• references/ with field semantics and benchmark guidance

Install or update the managed copy with:

make install MODE=global LOCALE=ru-en

This creates a managed runtime copy under ${XDG_DATA_HOME:-~/.local/share}/agents/skills/skill-debug-codex-requests, renders localized metadata plus trigger previews from .skill_triggers, and refreshes the symlinks in ~/.claude/skills/skill-debug-codex-requests and ~/.codex/skills/skill-debug-codex-requests.

For backward compatibility, ./setup.sh global --locale ... still works as a thin wrapper around the same install flow.

MIT