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/macromania/agentop/debuggingnpx skills add macromania/agentop --skill debugginggit clone --depth 1 https://github.com/macromania/agentopWhat 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 | $0.00052 | $0.01988 |
| Opus 5 | $0.00026 | $0.00994 |
| Sonnet 5 | $0.00010 | $0.00398 |
| Haiku 4.5 | $0.00005 | $0.00199 |
Grade A, and why
debugging 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.
How it starts
The opening of the file, as written. The whole thing — 338 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Systematic Debugging
A structured approach to isolating and resolving bugs efficiently, based on proven debugging methodologies.
When to Use This Skill
- Encountering runtime errors
- Test failures
- Unexpected behavior
- Performance issues
- Investigating reported bugs
- Understanding unfamiliar code
Debugging Methodology
Phase 1: Understand the Problem
Before touching code, gather information:
-
Reproduce the Issue
- Get exact reproduction steps
- Identify what triggers the bug
- Note when it started (recent changes?)
-
Define Expected vs Actual
Expected: User clicks "Start Session" → Session starts, UI shows running state Actual: User clicks "Start Session" → Nothing happens, no error in console -
Gather Context
- Check error messages and stack traces
- Review recent git commits
- Check if it worked before (and what changed)
Phase 2: Isolate the Problem
Narrow down the scope systematically:
// Binary search approach to find failing point
async function startSession(outcomeId: string) {
console.log('[DEBUG] 1. Starting session for:', outcomeId);
const outcome = await getOutcome(outcomeId);
console.log('[DEBUG] 2. Got outcome:', outcome?.id);
if (!outcome) {
console.log('[DEBUG] 2a. Outcome not found, returning early');
return;
}
const session = await createSession(outcome);
console.log('[DEBUG] 3. Created session:', session?.id);
await notifyUI(session);
console.log('[DEBUG] 4. UI notified');
}
Isolation techniques:
- Add strategic logging at entry/exit points
- Check if issue is in frontend, backend, or IPC
- Verify data at each boundary
- Test with minimal reproduction case
Phase 3: Form Hypothesis
Based on evidence, form specific hypotheses:
## Hypothesis Log
### H1: IPC handler not registered
- Evidence: Console shows "invoke" called but no response
- Test: Add logging to main process IPC handler
- Result: ❌ Handler is registered
### H2: Promise not awaited
- Evidence: Function returns before async work completes
- Test: Add await to database call
- Result: ✅ Missing await found!
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 · 338 lines · 52 tokens per session scan A cd89aa8fb38f
debugging is a skill published in the GitHub repository macromania/agentop (10 stars, last pushed 5mo ago), licensed MIT. It adds 52 tokens to every session and 1,988 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 2026-08-31.
Other skills, from other repositories
aatmf-t10-confidentiality-breach
AATMF T10 — Integrity & Confidentiality Breach. System prompt extraction, training-data extraction, model-weight leakage, private-key recovery.
peekaboo
Capture and automate macOS UI with the Peekaboo CLI.
mochi-remind
Handle due reminders — notify the user with natural language and mark them done.
publish-registry
Publish @agentos-software/ registry packages from AgentOS. Use whenever the user asks to publish or release registry software/agent packages.
sidewinder-rattlesnake
Adversary-emulation profile for SideWinder (G0121 / Rattlesnake / T-APT-04 / Razor Tiger), India's suspected state-sponsored cyber-espionage actor.
lazarus-group
Adversary-emulation profile for Lazarus Group (G0032, aka Hidden Cobra / Diamond Sleet / Labyrinth Chollima), a North Korean RGB-linked actor conducting espionage, destructive, and financially motivated operations.