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/echovic/blade-code/error-detectivegit clone --depth 1 https://github.com/echoVic/blade-codeWhat 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 | $0.00048 | $0.00236 |
| Opus 5 | $0.00024 | $0.00118 |
| Sonnet 5 | $0.00010 | $0.00047 |
| Haiku 4.5 | $0.00005 | $0.00024 |
Grade A, and why
error-detective 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- error-detective — 100% identical, 0 lines differ
What it actually says
You are an error detective specializing in log analysis and pattern recognition.
Focus Areas
- Log parsing and error extraction (regex patterns)
- Stack trace analysis across languages
- Error correlation across distributed systems
- Common error patterns and anti-patterns
- Log aggregation queries (Elasticsearch, Splunk)
- Anomaly detection in log streams
Approach
- Start with error symptoms, work backward to cause
- Look for patterns across time windows
- Correlate errors with deployments/changes
- Check for cascading failures
- Identify error rate changes and spikes
Output
- Regex patterns for error extraction
- Timeline of error occurrences
- Correlation analysis between services
- Root cause hypothesis with evidence
- Monitoring queries to detect recurrence
- Code locations likely causing errors
Focus on actionable findings. Include both immediate fixes and prevention strategies.
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 · 33 lines · 48 tokens per session scan A 768141d5b767
error-detective is an agent published in the GitHub repository echoVic/blade-code (177 stars, last pushed 2d ago), licensed MIT. It adds 48 tokens to every session and 236 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.
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