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 skills add madeinoz67/madeinoz-knowledge-system --skill codebase-explorergit clone --depth 1 https://github.com/madeinoz67/madeinoz-knowledge-systemWrote 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/skills/madeinoz67/madeinoz-knowledge-system/codebase-explorer)<a href="https://agentmods.dev/skills/madeinoz67/madeinoz-knowledge-system/codebase-explorer"><img src="https://agentmods.dev/badge/skills/madeinoz67/madeinoz-knowledge-system/codebase-explorer/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/skills/madeinoz67/madeinoz-knowledge-system/codebase-explorer"><img src="https://agentmods.dev/badge/skills/madeinoz67/madeinoz-knowledge-system/codebase-explorer.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 | $0.00027 | $0.01030 |
| Opus 5.5 | $0.00011 | $0.00412 |
| Sonnet 5.5 | $0.00005 | $0.00206 |
| Haiku 4.5 | $0.00003 | $0.00103 |
Grade A, and why
codebase-explorer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Search Query Analysis
Query Optimization Skill
Codanna's semantic search works best with technical terms and specific concepts. Analyze the situation and optimize your codebase explore queries for code search:
Examples:
- If vague (e.g., "that parsing thing") → Make it specific (e.g., "language parser implementation")
- If a question (e.g., "how does parsing work?") → Extract keywords (e.g., "parsing implementation process")
- If conversational (e.g., "the stuff that handles languages") → Use technical terms (e.g., "language handler processor")
- If too broad (e.g., "errors") → Add context (e.g., "error handling exception management")
OptimizedQuery: {Claude: I will write my optimized query here, then use it below}
Execute this command with your optimized query:
Your Workflow
Gather Context <Step_1 GatherContext>
Use the Bash tool to perform semantic code search:
Execute: codanna mcp semantic_search_with_context query:"$OptimizedQuery" limit:5
What Codanna returns:
- Relevance scores (how well each result matches)
- Symbol signatures and documentation
- Relationships (calls, called_by, implements, defines)
- File locations with line ranges
Your Workflow <Step_2 YourWorkflow>
-
Analyze the results with their relevance scores (focus on results with score > 0.6 (if possible))
-
To see actual implementation of interesting results:
- Use the line range from the Location field to read just the relevant code
- Example: If you see "at
src/io/exit_code.rs:108-120" - Use the Read tool with:
file_path:src/io/exit_code.rs(use the working directory from your environment context to construct the absolute path)offset: 108 (start line)limit: 13 (calculated as: 120 - 108 + 1)
- Formula:
limit = end_line - start_line + 1 - Example:
Read(file_path="/full/path/to/src/io/exit_code.rs", offset=108, limit=13)
-
When relationships are shown (called_by, calls, defines, implements):
- If a relationship looks relevant to answering the query, investigate it
- Execute:
codanna retrieve describe <relationship_symbol_name|symbol_id:ID> - Example: If you see "Called by:
initialize_registry [symbol_id:123]", run:codanna retrieve describe initialize_registryordescribe symbol_id:123 - Note: Following 1-2 key relationships per result is typically sufficient
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.
- 1mo ago First seen · 95 lines · 27 tokens per session scan A 744f23d2a37f
codebase-explorer is a skill published in the GitHub repository madeinoz67/madeinoz-knowledge-system (5 stars, last pushed 6mo ago), licensed MIT. It adds 27 tokens to every session and 1,030 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 2026-08-31.
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orca-replay
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potpie-cli
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potpie-debug-memory
Use while debugging or troubleshooting failures, flaky tests, incidents, production alerts, CI failures, local dev setup issues, repeated bugs, prior fixes, failed attempts, and verification history.
rubber-duck
Adversarial "rubber duck" review that turns explaining-out-loud into a hallucination check. The main session is the PRESENTER (it did the work — a design doc, investigation, or analysis — and holds the real reasoning) and reconstructs the topic to a LISTENER — a spawned subagent pinned to a DIFFERENT-vendor model that…