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.
git clone --depth 1 https://github.com/rjmurillo/ai-agentsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/rjmurillo/ai-agents/debug)<a href="https://agentmods.dev/agents/rjmurillo/ai-agents/debug"><img src="https://agentmods.dev/badge/agents/rjmurillo/ai-agents/debug/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/rjmurillo/ai-agents/debug"><img src="https://agentmods.dev/badge/agents/rjmurillo/ai-agents/debug.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00031 | $0.00632 |
| Opus 5.5 | $0.00012 | $0.00253 |
| Sonnet 5.5 | $0.00006 | $0.00126 |
| Haiku 4.5 | $0.00003 | $0.00063 |
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 3d 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 and Triage: Run the application or tests against current code to confirm. Document exact reproduction steps. Capture error outputs and logs. Judge priority and decide if a fix is merited. If not, report and stop.
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: Take the narrowest change that still closes every open problem in the report. 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.
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.
- 3d ago Changed 0ac25f94fedd
- 4d ago Changed · +1 lines 7166ac304779
- 1mo ago Changed · -1 lines 4a73f5249c85
- 1mo ago First seen · 73 lines · 31 tokens per session scan A 1ca17664f502
debug is an agent published in the GitHub repository rjmurillo/ai-agents (48 stars, last pushed today), licensed MIT. It adds 31 tokens to every session and 632 once invoked, about $0.0001 per session on Opus 5.5. 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 agents, from other repositories
reviewer
Review changes against spec, plan, code quality heuristics, and repo rules. Supports local (ADOS pipeline) and remote (PR/MR) modes.
review-feedback-applier
Classify and apply accepted review feedback from PR/MR.
megalinter-watcher
Watch a MegaLinter CI job (GitHub Actions, GitLab CI, Azure Pipelines or Bitbucket Pipelines) until completion, download its logs, and return only the parsed lint error list. Use to keep large CI logs out of the main context. Observes only — never fixes, edits or pushes.
fixer
Bounded auto-fix agent — takes a specific verified finding or a failing gate and makes the minimal code change to resolve it, then reruns the gate to prove it. Use to close a confirmed reviewer/gatekeeper finding. Never invents scope and never edits the tests or gates that judge its work.
Library Curator
Curates changed skills, agents, instructions, workflows, and prompts in this lab by running frontmatter, link, and self-containment checks plus a README index sync, then proposes one fix list before editing anything.
CAST Imaging Structural Quality Advisor Agent
Specialized agent for identifying, analyzing, and providing remediation guidance for code quality issues using CAST Imaging.