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 skills/usefulsoftwareco/executor/prod-telemetrynpx skills add UsefulSoftwareCo/executor --skill prod-telemetrygit clone --depth 1 https://github.com/UsefulSoftwareCo/executorWhat 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 | $0.00075 | $0.01607 |
| Opus 5 | $0.00037 | $0.00804 |
| Sonnet 5 | $0.00015 | $0.00321 |
| Haiku 4.5 | $0.00007 | $0.00161 |
Grade A, and why
prod-telemetry 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 — 132 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Production telemetry access
All three stores are queryable through the Executor MCP's connected
integrations — no dashboards or credentials needed. Verify the connection
exists with connections.list if a call fails.
Axiom traces (axiom_mcp)
Tool: axiom_mcp.user.axiomMcpOAuth.querydataset — the argument is apl
(NOT query). Dataset: ['executor-cloud'] (worker spans; browser spans
join the same traces via traceparent).
Field layout (the part you'd otherwise rediscover by failed queries):
- Custom span attributes live under the JSON map
['attributes.custom'], NOT as top-levelattributes.*columns. Read with['attributes.custom']['mcp.tool.name']. A nonexistent top-level field is a hard query error ("invalid field"), not an empty result. - Span status:
['status.code']("OK"/"ERROR"),['status.message']. - Exceptions: the
eventscolumn carriesexception.type/exception.stacktraceJSON. - OTel basics are top-level:
name,trace_id,span_id,parent_span_id,duration,_time.
Span names worth querying (and their custom attrs):
mcp.execute/mcp.execute.resume—mcp.execute.mode(pausable/inline),mcp.execute.code_length, andmcp.execute.outcome(ok/fail/paused) with, on failures,mcp.execute.error_kind(type_error|reference_error|syntax_error|range_error|tool_error|timeout|resource_limit|serialization_error|thrown|unknown). Sandbox script failures ride the MCP success channel, sostatus.codestays OK — filter on these attributes, not span status. Spans from before the attributes shipped carry neither; absence is not success. Alsomcp.execute.result_chars(compact-JSON size of the returned value, pre-truncation; -1 = unmeasurable),mcp.execute.log_chars,mcp.execute.emitted— the dump-vs-narrow signal (the model preview truncates at 30k chars, soresult_chars > 30000means the model tried to pull a truncated blob into context).executor.tool.execute—mcp.tool.name(full address), and since PR #992:executor.tool.outcome(ok/fail),executor.tool.error_code,executor.tool.error_status,executor.tenant,executor.subject.mcp.tool.dispatch—mcp.tool.name(sandbox path),mcp.tool.integration, same outcome attrs.plugin.openapi.invoke—plugin.openapi.method/path_template/base_url, and since PR #992http.status_code.mcp.request(outer) —mcp.auth.organization_id,mcp.auth.account_id,mcp.tool.name, CF edge fields (cf.country…), MCP client fingerprint (mcp.client.name…), and on managed-cloudexecute/execute-actioncallsmcp.execute.code(the script itself, capped at 10k chars — cloud-only content capture; local/self-host telemetry never records content).
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 · 132 lines · 75 tokens per session scan A 8964805377f7
prod-telemetry is a skill published in the GitHub repository UsefulSoftwareCo/executor (3,466 stars, last pushed yesterday), licensed MIT. It adds 75 tokens to every session and 1,607 once invoked, about $0.0004 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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