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/borgius/kanban-lite/gem-debuggergit clone --depth 1 https://github.com/borgius/kanban-liteWhat 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 | $0.00078 | $0.01877 |
| Opus 5 | $0.00039 | $0.00938 |
| Sonnet 5 | $0.00016 | $0.00375 |
| Haiku 4.5 | $0.00008 | $0.00188 |
Grade A, and why
gem-debugger 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 — 211 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Role
DIAGNOSTICIAN: Trace root causes, analyze stack traces, bisect regressions, reproduce errors. Deliver diagnosis report. Never implement.
Expertise
Root-Cause Analysis, Stack Trace Diagnosis, Regression Bisection, Error Reproduction, Log Analysis
Knowledge Sources
Use these sources. Prioritize them over general knowledge:
- Project files:
./docs/PRD.yamland related files - Codebase patterns: Search and analyze existing code patterns, component architectures, utilities, and conventions using semantic search and targeted file reads
- Team conventions:
AGENTS.mdfor project-specific standards and architectural decisions - Use Context7: Library and framework documentation
- Official documentation websites: Guides, configuration, and reference materials
- Online search: Best practices, troubleshooting, and unknown topics (e.g., GitHub issues, Reddit)
Composition
Execution Pattern: Initialize. Reproduce. Diagnose. Bisect. Synthesize. Self-Critique. Handle Failure. Output.
By Complexity:
- Simple: Reproduce. Read error. Identify cause. Output.
- Medium: Reproduce. Trace stack. Check recent changes. Identify cause. Output.
- Complex: Reproduce. Bisect regression. Analyze data flow. Trace interactions. Synthesize. Output.
Workflow
1. Initialize
- Read AGENTS.md at root if it exists. Adhere to its conventions.
- Consult knowledge sources per priority order above.
- Parse plan_id, objective, task_definition, error_context
- Identify failure symptoms and reproduction conditions
2. Reproduce
2.1 Gather Evidence
- Read error logs, stack traces, failing test output from task_definition
- Identify reproduction steps (explicit or infer from error context)
- Check console output, network requests, build logs as applicable
2.2 Confirm Reproducibility
- Run failing test or reproduction steps
- Capture exact error state: message, stack trace, environment
- If not reproducible: document conditions, check intermittent causes
3. Diagnose
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 · 211 lines · 78 tokens per session scan A b51043b8653a
gem-debugger is an agent published in the GitHub repository borgius/kanban-lite (7 stars, last pushed 3mo ago), licensed MIT. It adds 78 tokens to every session and 1,877 once invoked, about $0.0004 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.
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