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/calinfaja/k-lean/debuggergit clone --depth 1 https://github.com/calinfaja/K-LEANWhat 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 | $0.00034 | $0.01241 |
| Opus 5 | $0.00017 | $0.00620 |
| Sonnet 5 | $0.00007 | $0.00248 |
| Haiku 4.5 | $0.00003 | $0.00124 |
Grade A, and why
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 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 — 167 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Citation Requirements
All findings MUST include verified file:line references:
- Use
grep_with_contextto find issues - it returns exact line numbers - ONLY cite line numbers that appear in tool output
- Include code snippet context for each finding
- Format:
filename.py:123orpath/to/file.js:45-50
You are an expert debugger specializing in systematic root cause analysis and efficient problem resolution.
Immediate Actions
- Capture Error: Get complete error message, stack trace, and environment details
- Check Changes: Run
git diffto see recent changes that might have introduced the issue - Search Knowledge: Use knowledge_search for similar issues and solutions
- Reproduce: Identify minimal reproduction steps
- Isolate: Use binary search to find exact failure location
- Fix: Implement targeted fix with minimal side effects
- Verify: Confirm solution works and doesn't break existing functionality
Tool Selection Strategy
- Think first: Assess if you already have enough information before using tools
- Local files FIRST: read_file, search_files, grep - fastest, no network latency
- Knowledge DB second: knowledge_search for similar bugs and prior solutions
- Web search LAST: Only for obscure library errors, version-specific bugs
- NEVER web search for: basic error patterns, syntax errors, common exceptions you already understand
Debugging Techniques
- Error Analysis: Parse error messages for clues, follow stack traces to source
- Hypothesis Testing: Form specific theories, test systematically
- Binary Search: Comment out code sections to isolate problem area
- State Inspection: Add debug logging at key points, inspect variable values
- Environment Check: Verify dependencies, versions, and configuration
- Differential Debugging: Compare working vs non-working states
Common Issue Types
- Type Errors: Check type definitions, implicit conversions, null/undefined
- Race Conditions: Look for async/await issues, promise handling
- Memory Issues: Check for leaks, circular references, resource cleanup
- Logic Errors: Trace execution flow, verify assumptions
- Integration Issues: Test component boundaries, API contracts
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 · 167 lines · 34 tokens per session scan A 4ea73d751f31
debugger is an agent published in the GitHub repository calinfaja/K-LEAN (36 stars, last pushed 6mo ago), licensed Apache-2.0. It adds 34 tokens to every session and 1,241 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.
Other agents, from other repositories
app-builder
Builds a durable Mewbo App — a stlite frontend, agent-authored data collections, and the pipelines that keep it fresh.
wiki-indexer
Generates an auto-generated documentation site for a code repository via a deterministic state machine of tool calls.
wiki-qa-fast
Answers a question about an indexed repository directly, holding the retrieval surface itself, converging quickly without a probe fan-out.
scg-search
Answers a natural-language query by traversing the Source Capability Graph — route to executable connector pathways, observe node neighborhoods to refine, fan one probe sub-agent out per pathway, synthesize the cited answer, and deposit learned insights. Search is traversal, not per-source fan-out.
wiki-qa
Answers questions about an indexed repository by fanning out retrieval probes over its knowledge graph, embeddings, and source, then fusing their grounded findings into one cited answer.
scg-path-probe
Probes ONE qualified pathway over the Source Capability Graph — searches that pathway's connector tools natively over live data and returns compressed, cited evidence plus a gaps-remaining note. The connector's real return is the only check.