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 commands/orq-ai/assistant-plugins/tracesgit clone --depth 1 https://github.com/orq-ai/assistant-pluginsWhat 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 | $0.00023 | $0.01313 |
| Opus 5 | $0.00012 | $0.00656 |
| Sonnet 5 | $0.00005 | $0.00263 |
| Haiku 4.5 | $0.00002 | $0.00131 |
Grade A, and why
traces 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 — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Traces
Query production traces from the orq.ai platform and display a summary. Use this as a debugging entry point — once you spot a problem, hand off to the orq-analyze-trace-failures skill for deep analysis.
Instructions
1. Parse arguments
Extract filters from $ARGUMENTS. All are optional:
--deployment <name>— filter by deployment name or key--status <status>— filter by trace status (e.g.,error,success)--last <duration>— time window:1h,6h,24h,7d,30d(default:24h)--limit <n>— max traces to return (default:20)
If $ARGUMENTS is empty, use defaults (last 24h, limit 20, no filters).
If the user provides plain text instead of flags (e.g., /orq:traces errors from today), interpret the intent and map to the appropriate filters.
2. Build filter string
The list_traces MCP tool uses Typesense filter syntax. Build the filter parameter from parsed arguments:
- Status filter:
status:=ERRORorstatus:=OK - Model filter:
attr_kv:=gen_ai.request.model=<model> - Combined: join with
&&
Use list_registry_keys and list_registry_values to discover available filter keys if needed.
3. Fetch traces
Use the list_traces MCP tool with the constructed filter, limit, and sort parameters.
limit: from--limitor default 25filter: from step 2 (omit if no filters)sort_by:"timestamp:desc"(default)
4. Display the summary
Present traces in a scannable format, not raw JSON.
Header, overview line, and workspace link:
# Orq.ai Traces — Recent Activity
Traces (last 24h): 47 total — 41 success, 4 error, 2 timeout
Then add: View and filter traces at **[Traces → my.orq.ai](https://my.orq.ai/)**.
Then list traces grouped by status (errors first) using ASCII table format with full trace IDs:
Errors (4)
┌───┬──────────────────┬──────────┬──────────────────────────────────┐
│ # │ Time (UTC) │ Duration │ Trace ID │
├───┼──────────────────┼──────────┼──────────────────────────────────┤
│ 1 │ Mar 13, 14:22:07 │ 209ms │ b422a93c8b34ddfa70eaf5c7e3705c19 │
│ 2 │ Mar 13, 13:01:06 │ 192ms │ 4f1957728f7b0a6ba6b3ce0fd1679c4a │
│ 3 │ Mar 13, 12:55:04 │ 228ms │ 89d0a987ff11b23aca1f5b2e4d8efac2 │
│ 4 │ Mar 13, 12:54:04 │ 297ms │ c20e270e79257144f93b5beedcc6c62f │
└───┴──────────────────┴──────────┴──────────────────────────────────┘
Success (showing 10 of 41)
┌───┬──────────────────┬──────────┬──────────────────────────────────┐
│ # │ Time (UTC) │ Duration │ Trace ID │
├───┼──────────────────┼──────────┼──────────────────────────────────┤
│ 1 │ Mar 13, 15:30:58 │ 493ms │ 2d8781e8d628b89e9bde9fcdf7f80827 │
│ 2 │ Mar 13, 15:28:57 │ 629ms │ 201ee2b617ce05622bc8e1888703a0bd │
└───┴──────────────────┴──────────┴──────────────────────────────────┘
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 · 102 lines · 23 tokens per session scan A dc4cb424e3f4
traces is a command published in the GitHub repository orq-ai/assistant-plugins (6 stars, last pushed 5d ago), licensed MIT. It adds 23 tokens to every session and 1,313 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.
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