Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/madeinoz67/madeinoz-knowledge-systemnpx agentmods add commands/madeinoz67/madeinoz-knowledge-system/x-rayWrote 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/commands/madeinoz67/madeinoz-knowledge-system/x-ray)<a href="https://agentmods.dev/commands/madeinoz67/madeinoz-knowledge-system/x-ray"><img src="https://agentmods.dev/badge/commands/madeinoz67/madeinoz-knowledge-system/x-ray/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/commands/madeinoz67/madeinoz-knowledge-system/x-ray"><img src="https://agentmods.dev/badge/commands/madeinoz67/madeinoz-knowledge-system/x-ray.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 | $0.00010 | $0.01231 |
| Opus 5 | $0.00005 | $0.00616 |
| Sonnet 5 | $0.00002 | $0.00246 |
| Haiku 4.5 | $0.00001 | $0.00123 |
Grade A, and why
x-ray scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Search Query Analysis
User's Original Query: "$ARGUMENTS"
Query Optimization
Codanna's semantic search works best with technical terms and specific concepts. Analyze the query above and improve it for code search:
- 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: Write your improved 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: node .claude/scripts/codanna/context-provider.js find "$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 "Location:
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:
node .claude/scripts/codanna/context-provider.js describe <relationship_symbol_name|symbol_id:ID> - Example: If you see "Called by:
initialize_registry [symbol_id:123]", run:node .claude/scripts/codanna/context-provider.js 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.
- 10d ago First seen · 117 lines · 10 tokens per session scan A 54d2e8ab06f2
x-ray is a command published in the GitHub repository madeinoz67/madeinoz-knowledge-system (3 stars, last pushed 5mo ago), licensed MIT. It adds 10 tokens to every session and 1,231 once invoked, about $0.0001 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.
Other commands, from other repositories
tokenless-stats
Show Tokenless compression statistics.
memory-search
Search agent memory + learned patterns for cross-session context relevant to $ARGUMENTS.
formula-f10
../../../core/thinkingos/agents/observer.md.
formula-f8
../../../core/thinkingos/agents/securityauditor.md.
ontario-bud
Activate the ontario-bud persona (rural-Ontario / Letterkenny-inspired caricature) for the rest of the session.
check
Run TYPO3 conformance check on current extension.