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 commands/felipeslo/opencode-kit/debuggit clone --depth 1 https://github.com/felipeslo/opencode-kitWhat 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 | $0.00013 | $0.00433 |
| Opus 5 | $0.00006 | $0.00217 |
| Sonnet 5 | $0.00003 | $0.00087 |
| Haiku 4.5 | $0.00001 | $0.00043 |
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 yesterday.
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.
What it actually says
/debug - Systematic Problem Investigation
$ARGUMENTS
Purpose
This command activates DEBUG mode for systematic investigation of issues, errors, or unexpected behavior.
Behavior
When /debug is triggered:
-
Gather information
- Error message
- Reproduction steps
- Expected vs actual behavior
- Recent changes
-
Form hypotheses
- List possible causes
- Order by likelihood
-
Investigate systematically
- Test each hypothesis
- Check logs, data flow
- Use elimination method
-
Fix and prevent
- Apply fix
- Explain root cause
- Add prevention measures
Output Format
## 🔍 Debug: [Issue]
### 1. Symptom
[What's happening]
### 2. Information Gathered
- Error: `[error message]`
- File: `[filepath]`
- Line: [line number]
### 3. Hypotheses
1. ❓ [Most likely cause]
2. ❓ [Second possibility]
3. ❓ [Less likely cause]
### 4. Investigation
**Testing hypothesis 1:**
[What I checked] → [Result]
**Testing hypothesis 2:**
[What I checked] → [Result]
### 5. Root Cause
🎯 **[Explanation of why this happened]**
### 6. Fix
```[language]
// Before
[broken code]
// After
[fixed code]
7. Prevention
🛡️ [How to prevent this in the future]
---
## Examples
/debug login not working /debug API returns 500 /debug form doesn't submit /debug data not saving
---
## Key Principles
- **Ask before assuming** - get full error context
- **Test hypotheses** - don't guess randomly
- **Explain why** - not just what to fix
- **Prevent recurrence** - add tests, validation
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.
- yesterday First seen · 104 lines · 13 tokens per session scan A 96af93058370
debug is a command published in the GitHub repository felipeslo/opencode-kit (2 stars, last pushed 5mo ago), licensed MIT. It adds 13 tokens to every session and 433 once invoked, about $0.0001 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 2026-08-31.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.