Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/floomhq/moto/debugnpx skills add floomhq/moto --skill debuggit clone --depth 1 https://github.com/floomhq/motoWhat 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 | $0.00066 | $0.00819 |
| Opus 5 | $0.00033 | $0.00409 |
| Sonnet 5 | $0.00013 | $0.00164 |
| Haiku 4.5 | $0.00007 | $0.00082 |
Grade A, and why
debug scanned grade A with 0 findings 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 2d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Debug Skill
Principle: Root Cause, Not Quick Fix
The goal is to find WHY something is broken, not to patch the symptom. A quick fix that doesn't address the root cause will resurface.
Step 1: Understand the failure
Collect the full picture:
- What is the exact error message? (copy verbatim, don't paraphrase)
- What was the expected behavior?
- What is the actual behavior?
- When did it start failing? (after which change?)
- Does it fail consistently or intermittently?
- What environment? (local / staging / prod, OS, Node/Python version)
Step 2: Read the error carefully
Before touching any code:
- Read the FULL stack trace, not just the first line
- Note the file and line number where it originated
- Note any "caused by" or nested exceptions
- Check if there are warning messages before the error
Step 3: Form a hypothesis
Based on the error, form a hypothesis about what's wrong. Common categories:
- Data issue: null/undefined where not expected, wrong type, out of range
- Logic issue: wrong condition, off-by-one, state mutation
- Dependency issue: wrong version, missing module, API change
- Environment issue: missing env var, wrong config, permission denied
- Timing issue: race condition, async/await missing, order of operations
- Network issue: timeout, DNS, CORS, SSL
Step 4: Verify the hypothesis
Verify, don't assume. For each hypothesis:
# Add targeted logging to confirm
console.log('DEBUG:', variableName, typeof variableName);
# Check values at the point of failure
# Read the actual code at the error location
# Check the data flowing in
Step 5: Find the root cause
Trace backwards from the failure:
- Where does the error originate?
- What called that code?
- What data was passed?
- Where was that data set?
- Is the data wrong, or is the code wrong?
Use git log and git blame to understand when and why code was written a certain way.
Step 6: Fix and verify
Once root cause is confirmed:
- Make the minimal fix
- Verify the original error is gone
- Check no new errors were introduced
- Add a test to prevent regression
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.
- 2d ago First seen · 111 lines · 66 tokens per session scan A 710eaa2d9444
debug is a skill published in the GitHub repository floomhq/moto (32 stars, last pushed 2mo ago), licensed MIT. It adds 66 tokens to every session and 819 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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