Debugging broken skills

When an OpenClaw skill fails in the US, use logs, isolate the skill, and check config, credentials, and API behavior. This post walks through a systematic debugging process and how SingleAnalytics can help spot patterns.

A "broken" skill might mean errors in chat, timeouts, wrong results, or the agent avoiding the skill entirely. Debugging systematically saves time for US teams. This post covers how to find and fix broken OpenClaw skills.

Symptoms

| Symptom | What it might mean | |---------|---------------------| | "Skill X failed" or error in reply | Exception in the skill, bad response from API, or invalid args. | | Timeout / no reply | Skill is slow or stuck; increase timeout or fix the skill. | | Wrong or empty result | Bad params, API change, or logic error in the skill. | | Agent never calls the skill | Model doesn’t recognize when to use it; improve tool description or examples. |

US teams that log skill calls and results to SingleAnalytics can see which skills fail most and under what conditions.

Step 1 – Reproduce

• Exact trigger – Note the exact user message and (if applicable) channel and user. Reproduce in dev or staging with the same message. If you can’t reproduce, the issue may be environment (e.g., prod API key, rate limit). In the US, keep a test script that runs the skill with fixed args so you can reproduce without going through the agent.
• Minimal input – Simplify the message to the smallest one that still fails (e.g., "What’s on my calendar today?"). That narrows whether the problem is the skill, the model’s args, or something else.

Step 2 – Check logs

• OpenClaw logs – Look for the tool call: tool name, arguments, and result or exception. Many setups log at INFO or DEBUG. Search for the skill name and timestamp. US teams often ship logs to a central log aggregator and search there.
• Skill-level logs – If the skill logs internally (e.g., to a file or stdout), check those for the same invocation. You might see "API returned 401" or "missing param date." That points to credentials or schema.
• Stack trace – If there’s an exception, the full traceback shows where it broke. Fix that line or handle that case. In the US, avoid logging full stack traces to end users; keep them server-side.

Step 3 – Isolate the skill

Run the skill outside the agent:

• CLI or test script – If the skill exposes a CLI or you can call its handler from a script, invoke it with the same arguments the model would use. If it fails the same way, the bug is in the skill or its dependency (API, config). If it succeeds, the issue may be how the agent is calling it (wrong args, encoding). US developers should add a small "skill runner" script for each skill to make this easy.
• Mock the agent – Call the skill’s entry point with a dict of args and the same config the agent uses. No model involved. Confirms the skill in isolation.

Step 4 – Common causes

• Config / credentials – Wrong or expired API key, wrong endpoint, missing env var. Check the config file and env for the skill. In the US, rotate keys if they might have been leaked and retest.
• Schema vs. implementation – Model sends a param the skill doesn’t expect, or in the wrong type (e.g., string instead of number). Align the tool schema with the implementation; add validation and clear error messages. SingleAnalytics can show which args are sent when the skill fails.
• API changes – External API changed response format or auth. Update the skill to match; add a test that mocks the API and asserts on the parsed response. US teams that depend on third-party APIs should watch their changelogs.
• Rate limits / timeouts – API returns 429 or the skill times out. Increase timeout or add retries with backoff; reduce concurrency or cache where appropriate. Document limits for US users.
• Permissions – Skill runs in a sandbox that blocks network or file access. Adjust sandbox or move the skill to a less restricted category. US enterprises often start with strict sandbox and relax only when needed.
• Model never calls the skill – Tool description is vague or doesn’t match user phrasing. Improve the description and add example invocations in the system prompt. Test with direct tool calls (bypass model) to confirm the skill works when called.

Step 5 – Fix and verify

• Fix – Apply the fix (code, config, or schema). Add or update a test so the bug doesn’t return. In the US, run tests in CI before deploy.
• Verify – Run the test script and the same user message again. Confirm the reply is correct. Optionally deploy to staging and run a few more scenarios before production. Use SingleAnalytics to confirm error rate for that skill drops after the fix.

Prevention

• Tests – Unit tests for the skill with mocked APIs; integration test that invokes the skill with sample args. US teams should run these on every commit.
• Monitoring – Alert on skill error rate or latency. SingleAnalytics can feed dashboards so US teams see regressions quickly.
• Changelog – When you fix a bug, note it in the skill’s changelog and bump PATCH version. Helps US users know when to update.

Summary

Debug broken OpenClaw skills by reproducing with exact input, checking logs and stack traces, and isolating the skill with a script or test. Common causes are config/credentials, schema mismatch, API changes, rate limits, permissions, or unclear tool description. Fix, add tests, verify, and monitor with SingleAnalytics so US teams catch future breakage early.