API Quality Check

Check coding-model API quality, capability fit, and drift with LT-lite and B3IT-lite. Use when Codex needs to verify whether an OpenAI/OpenAI-compatible/Anth...

Use the bundled script to run headless API-quality checks. Treat this skill as script-first: do not recreate LT-lite/B3IT-lite logic inline unless the script is clearly insufficient.

Provider names such as Ark/Volcengine, GLM, DeepSeek, Kimi, SiliconFlow, and similar services are examples only. The primary decision is the endpoint protocol type: OpenAI, OpenAI-Compatible, or Anthropic.

Quick start

Set the path once:

bash

export CODEX_HOME="${CODEX_HOME:-$HOME/.codex}"
export APIQ="$CODEX_HOME/skills/api-quality-check/scripts/api_quality_check.py"
export APIQ_BATCH="$CODEX_HOME/skills/api-quality-check/scripts/run_batch_checks.sh"
export APIQ_DAILY="$CODEX_HOME/skills/api-quality-check/scripts/run_daily_check.sh"

Run a capability smoke test first:

bash

python "$APIQ" smoke \
  --provider "OpenAI-Compatible" \
  --base-url "https://ark.cn-beijing.volces.com/api/coding/v3" \
  --api-key "$API_KEY" \
  --model-id "ark-code-latest" \
  --html-output ./smoke.html

For many OpenAI-compatible endpoints, the same command also works if the user pastes the full .../chat/completions URL. The script will normalize it back to the API root automatically.

If you want a ready-to-run provider.json first, generate it with:

bash

python "$APIQ" init-config \
  --provider "OpenAI-Compatible" \
  --base-url "https://api.siliconflow.cn/v1/chat/completions" \
  --api-key "$API_KEY" \
  --model-id "deepseek-ai/DeepSeek-V3.2" \
  --name "siliconflow-v3-2" \
  --config-output ./provider.json

If an endpoint requires client-specific headers, put them in the config JSON as a headers object or pass them with --headers-json. For Kimi coding endpoints, use {"User-Agent":"KimiCLI/2.0.0"} only when the address is under https://api.kimi.com/coding; for the OpenAI-compatible Kimi path, use https://api.kimi.com/coding/v1.

If you already have multiple raw endpoint entries, normalize them into providers.json with:

bash

python "$APIQ" init-batch-config \
  --configs ./raw-providers.json \
  --config-output ./providers.json

Or run the full batch pipeline:

bash

"$APIQ_BATCH" ./providers.json ./api-quality-out

That command also creates ./api-quality-out/index.html as the landing page for all generated reports.

For one endpoint that you want to check every day and archive by date:

bash

bash "$APIQ_DAILY" ./provider.json ./daily-out my-endpoint

Workflow

1. Run smoke before any baseline or detect run.
2. If you have many endpoints, run batch-smoke with a config list before choosing which ones deserve deeper LT/B3IT work.
3. Read the result:
   • b3it_supported=true: the endpoint can return normal first-token text at max_tokens=1
   • lt_supported=true: the endpoint also returns logprobs, so LT-lite can run
   • recommended_detector: the script's direct recommendation for the next step
4. If lt_supported=false, do not force LT-lite; pivot to B3IT-lite or report that LT is unavailable.
5. Save baselines to explicit JSON files and reuse them for later detection.
6. Keep outputs file-based for coding CLIs and OpenClaw. Do not depend on GUI state.
7. For noisy endpoints, prefer the built-in B3IT defaults before tightening or loosening thresholds manually.

Endpoint Types

• OpenAI: use this for official OpenAI-style endpoints.
• OpenAI-Compatible: use this for third-party endpoints that follow OpenAI request and response shapes; vendor-specific headers may be required.
• Anthropic: use this for /v1/messages style endpoints; in this skill it is B3IT-only.

Commands

Capability smoke

bash

python "$APIQ" smoke --config ./provider.json --output ./smoke.json

Generate a provider config template

bash

python "$APIQ" init-config \
  --provider "OpenAI-Compatible" \
  --base-url "https://api.siliconflow.cn/v1/chat/completions" \
  --api-key "$API_KEY" \
  --model-id "deepseek-ai/DeepSeek-V3.2" \
  --config-output ./provider.json

Generate a batch providers.json template

bash

python "$APIQ" init-batch-config \
  --configs ./raw-providers.json \
  --config-output ./providers.json

Batch capability smoke

bash

python "$APIQ" batch-smoke --configs ./providers.json --output ./batch-smoke.json --html-output ./batch-smoke.html

Batch LT-lite baselines

bash

python "$APIQ" batch-lt-baseline \
  --configs ./providers.json \
  --output-dir ./lt-baselines \
  --output ./batch-lt-baselines.json \
  --html-output ./batch-lt-baselines.html

Batch LT-lite detect

bash

python "$APIQ" batch-lt-detect \
  --configs ./providers.json \
  --baseline-manifest ./batch-lt-baselines.json \
  --output ./batch-lt-report.json \
  --html-output ./batch-lt-report.html

Batch B3IT-lite baselines

bash

python "$APIQ" batch-b3it-baseline \
  --configs ./providers.json \
  --output-dir ./b3it-baselines \
  --output ./batch-b3it-baselines.json \
  --html-output ./batch-b3it-baselines.html

Batch B3IT-lite detect

bash

python "$APIQ" batch-b3it-detect \
  --configs ./providers.json \
  --baseline-manifest ./batch-b3it-baselines.json \
  --output ./batch-b3it-report.json \
  --html-output ./batch-b3it-report.html \
  --detection-repeats 5 \
  --min-stable-count 2 \
  --min-stable-ratio 0.35 \
  --confirm-passes 1

LT-lite baseline

bash

python "$APIQ" lt-baseline --config ./provider.json --output ./lt-baseline.json

LT-lite detect

bash

python "$APIQ" lt-detect \
  --config ./provider.json \
  --baseline ./lt-baseline.json \
  --output ./lt-report.json

B3IT-lite baseline

bash

python "$APIQ" b3it-baseline --config ./provider.json --output ./b3it-baseline.json

B3IT-lite detect

bash

python "$APIQ" b3it-detect \
  --config ./provider.json \
  --baseline ./b3it-baseline.json \
  --output ./b3it-report.json \
  --detection-repeats 5 \
  --min-stable-count 2 \
  --min-stable-ratio 0.35 \
  --confirm-passes 1

Daily single-endpoint drift run

bash

bash "$APIQ_DAILY" ./provider.json ./daily-out my-endpoint

Defaults and guardrails

• Default to non-streaming, timeout=60, and temperature values matched to the detector.
• Every command can additionally write a human-readable report with --html-output.
• OpenAI/OpenAI-compatible requests may include custom JSON headers, either from the config file or --headers-json.
• The Kimi-specific {"User-Agent":"KimiCLI/2.0.0"} header is not a general default. Use it only for https://api.kimi.com/coding endpoints; for the OpenAI-compatible Kimi path, use https://api.kimi.com/coding/v1.
• The script auto-disables thinking for common reasoning-first providers such as Ark, Doubao, GLM, and Zhipu unless extra_body is explicitly provided in the config JSON.
• For OpenAI/OpenAI-compatible endpoints that still return reasoning_content without normal text, the script will retry once with {"thinking":{"type":"disabled"}} before failing.
• OpenAI/OpenAI-compatible configs may use either the API root or a full .../chat/completions URL; the script normalizes the base URL internally.
• init-config writes the normalized config explicitly, including any auto-selected extra_body, so the saved file is portable across Codex, Claude Code, Gemini CLI, and OpenClaw runs.
• init-batch-config does the same normalization for a whole config list and writes a ready-to-run providers.json.
• Anthropic mode is treated as B3IT-only in this skill.
• If the endpoint returns reasoning/thinking blocks but no normal text, report that as a capability mismatch instead of fabricating a token result.
• If the endpoint does not return logprobs, report that LT-lite is unavailable instead of guessing.
• B3IT baseline discovery on OpenAI/OpenAI-compatible endpoints will automatically retry with a stronger candidate-search profile if an initial low-cost run finds no border inputs.
• B3IT detection defaults are tuned for lower false positives: detection-repeats=5, min-stable-count=2, min-stable-ratio=0.35, confirm-passes=1.
• Daily runs auto-refresh older B3IT baselines when they predate the stable-support filtering fields.

Resources

Open only what you need:

• Workflow examples and CLI usage: references/workflows.md
• Protocol-first playbook for OpenAI, OpenAI-Compatible, and Anthropic: references/endpoint-types-playbook.md
• Kimi coding endpoint quickstart: references/kimi-coding-quickstart.md
• Kimi Anthropic endpoint quickstart: references/kimi-anthropic-quickstart.md
• JSON config format: references/config-schema.md
• Example provider list: references/providers.example.json
• Main executable: scripts/api_quality_check.py
• One-shot batch wrapper: scripts/run_batch_checks.sh
• Daily single-endpoint wrapper: scripts/run_daily_check.sh