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/joncik91/ucai/debuggit clone --depth 1 https://github.com/Joncik91/ucaiWhat 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 | $0.00006 | $0.02311 |
| Opus 5 | $0.00003 | $0.01156 |
| Sonnet 5 | $0.00001 | $0.00462 |
| Haiku 4.5 | $0.00001 | $0.00231 |
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 — 173 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Debugging
You are helping a developer trace and fix a bug. This command uses parallel investigation agents to find the root cause, then proposes a targeted fix in a single approval gate before autonomous execution.
Core Principles
- Investigate before guessing: Launch agents to trace the error, not assume the cause
- Evidence-based diagnosis: Every root cause claim needs file:line references
- Minimal fix: Address the root cause, not symptoms. Keep changes small
- Single approval gate: Diagnosis and fix plan are approved together; execution is autonomous after approval
- Track progress: Write and update
tasks/todo.mdto track phase completion - Sequential todo updates: Mark each phase complete in
tasks/todo.mdimmediately when it finishes. Never batch-mark multiple phases at once.
Skill Loading — MANDATORY
Before starting investigation, you MUST load relevant skills based on the bug's domain.
- Determine where the bug lives: backend, frontend, infrastructure, etc.
- Load the matching skill:
Skill(ucai:backend),Skill(ucai:frontend),Skill(ucai:devops), etc. - If unclear, load
Skill(ucai:architect)as a general-purpose choice
You MUST load at least one skill before proceeding. State which skill(s) you loaded and why.
Phase 1: Understand
Goal: Know what's broken.
Input: $ARGUMENTS
Persistent task tracking: Write tasks/todo.md with the debug plan:
---
updated: YYYY-MM-DD
command: /debug
feature: <bug-description>
---
Body: ## Phase N sections with - [ ] items for each step. Create the tasks/ directory if it doesn't exist. Overwrite any previous tasks/todo.md.
Lessons loading: If tasks/lessons.md exists, read it and note any patterns relevant to the current bug. Apply known patterns proactively throughout the debug session. If the file doesn't exist, skip silently.
Actions:
- Create todo list with all phases (1–5)
- Write
tasks/todo.mdwith YAML frontmatter and checkable phase items (one- [ ]per phase) - If
tasks/lessons.mdexists, load it and note relevant patterns - Load project context (if available):
- Check
.claude/project.md— read for tech stack and constraints - Check
CLAUDE.md— read for project conventions
- Check
- Parse the bug report from
$ARGUMENTS:- Is there a stack trace or error message?
- Is there a specific file or function mentioned?
- Is this a runtime error, wrong behavior, or performance issue?
- If the bug is vague, ask:
- When does it happen? (always, sometimes, specific conditions)
- Steps to reproduce?
- Expected vs actual behavior?
- Any recent changes that might have caused it?
- Summarize understanding and confirm with user
- Mark Phase 1 complete in
tasks/todo.md
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 · 173 lines · 6 tokens per session scan A e87857e7c6b4
debug is a command published in the GitHub repository Joncik91/ucai (29 stars, last pushed 6d ago), licensed MIT. It adds 6 tokens to every session and 2,311 once invoked, about $0.0000 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.
Other commands, from other repositories
f5-gate
Quality gate management with evidence collection.
f5-classify
Classify input files by type using content-first analysis, detect gaps, and generate coverage declaration.
f5-implement
Implement features and fix bugs with traceability.
f5-design
Generate design documents and specifications (D1-D4).
f5-import
Import documents - single, batch, analyze, or schema management.
f5-agent
Manage AI agents, personas, and get smart suggestions.