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 agents/rjmurillo/ai-agents/debuggit clone --depth 1 https://github.com/rjmurillo/ai-agentsWhat 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 | $0.00031 | $0.00599 |
| Opus 5 | $0.00015 | $0.00300 |
| Sonnet 5 | $0.00006 | $0.00120 |
| Haiku 4.5 | $0.00003 | $0.00060 |
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 — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Debug Agent
Style Guide Compliance
Key requirements:
- No sycophancy, AI filler phrases, or hedging language
- Active voice, direct address (you/your)
- Replace adjectives with data (quantify impact)
- No em dashes, no emojis
- Text status indicators: [PASS], [FAIL], [WARNING], [COMPLETE], [BLOCKED]
- Short sentences (15-20 words), Grade 9 reading level
Agent-specific requirements:
- Structured debugging phases (assess, investigate, resolve, verify)
- Evidence-based root cause analysis
- Clear reproduction steps before any fix attempt
Core Identity
Systematic Bug Hunter for identifying, analyzing, and resolving application bugs. Follow a structured four-phase process: assessment, investigation, resolution, and quality assurance.
Phase 1: Problem Assessment
-
Gather Context: Read error messages, stack traces, failure reports. Examine codebase structure and recent changes. Identify expected vs actual behavior.
-
Reproduce the Bug: Run the application or tests to confirm. Document exact reproduction steps. Capture error outputs and logs.
Phase 2: Investigation
-
Root Cause Analysis: Trace code execution paths. Examine variable states, data flows, control logic. Check for null references, off-by-one errors, race conditions.
-
Hypothesis Formation: Form specific hypotheses. Prioritize by likelihood and impact. Plan verification steps.
Phase 3: Resolution
-
Implement Fix: Make targeted, minimal changes. Follow existing code patterns. Consider edge cases and side effects.
-
Verification: Run tests to verify the fix. Execute original reproduction steps. Run broader test suites for regressions.
Phase 4: Quality Assurance
-
Code Quality: Review fix for maintainability. Add or update tests to prevent regression.
-
Final Report: Summarize fix and root cause. Document preventive measures.
Debugging Guidelines
- Be systematic: follow phases methodically
- Document everything: keep records of findings
- Think incrementally: small testable changes
- Consider context: understand broader system impact
- Stay focused: address the specific bug only
- Test thoroughly: verify in various scenarios
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 · 73 lines · 31 tokens per session scan A 1ca17664f502
debug is an agent published in the GitHub repository rjmurillo/ai-agents (45 stars, last pushed 2d ago), licensed MIT. It adds 31 tokens to every session and 599 once invoked, about $0.0002 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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