executor: Skill for Claude Code

.claude/skills/prod-telemetry/SKILL.md

prod-telemetry is a skill for Claude Code from UsefulSoftwareCo/executor. It costs 75 tokens per session (1,906 once invoked), scanned A, original, MIT.

A way to query production telemetry, meaning records about how a live application runs, from traces, database data, and product analytics.

In plain words
What is it for?
Use it to investigate production errors and slow requests, understand feature usage and churn signals, and check whether a deployment changed real-world behavior.
Why use it?
It avoids searching separate dashboards or rediscovering the structure of each data source when investigating live-system behavior.

Skill for Claude Code

Written for Claude Code: installed under .claude/.

This is UsefulSoftwareCo/executor's own configuration. It tells Claude Code how to work on executor itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything executor configures →

About the project

Executor is a shared integration layer that lets AI agents use tools from MCP servers, OpenAPI specifications, GraphQL APIs, and custom JavaScript functions through one catalog. It centralizes credentials and per-tool access policies so MCP-compatible agents can reuse the same integrations.

UsefulSoftwareCo/executor · 4,297 stars · on GitHub · executor.sh

Reuse

Borrowing it

Nothing to install: this file belongs to UsefulSoftwareCo/executor. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/UsefulSoftwareCo/executor/main/.claude/skills/prod-telemetry/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/UsefulSoftwareCo/executor

Made for: Claude Code.

Wrote 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.

agentmods badge for prod-telemetry

README.md
[![agentmods](https://agentmods.dev/badge/skills/usefulsoftwareco/executor/prod-telemetry/github.svg)](https://agentmods.dev/skills/usefulsoftwareco/executor/prod-telemetry)
Your own site
<a href="https://agentmods.dev/skills/usefulsoftwareco/executor/prod-telemetry"><img src="https://agentmods.dev/badge/skills/usefulsoftwareco/executor/prod-telemetry/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.

agentmods 80×15 button for prod-telemetry

Your own site · 80×15
<a href="https://agentmods.dev/skills/usefulsoftwareco/executor/prod-telemetry"><img src="https://agentmods.dev/badge/skills/usefulsoftwareco/executor/prod-telemetry.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 75 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,906 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00075 $0.01906
Opus 5.5 $0.00030 $0.00762
Sonnet 5.5 $0.00015 $0.00381
Haiku 4.5 $0.00007 $0.00191

Measured 1mo ago against content hash fc1c3e4dc16c, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 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.

.claude/skills/prod-telemetry/SKILL.md · 160 lines

How it starts

The opening of the file, as written. The whole thing — 160 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-level attributes.* 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 events column carries exception.type / exception.stacktrace JSON.
  • 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, and mcp.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, so status.code stays OK — filter on these attributes, not span status. Spans from before the attributes shipped carry neither; absence is not success. Also mcp.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, so result_chars > 30000 means 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 #992 http.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-cloud execute/execute-action calls mcp.execute.code (the script itself, capped at 10k chars — cloud-only content capture; local/self-host telemetry never records content).
  • auth.authorize_organization — every membership authorization. Reads the local membership mirror unconditionally; there is no per-request readiness check and no WorkOS fallback, so this span carries no readiness attribute. The mirror's write spans are workos_mirror.<op>; the reconciler run is workos_events.sync. workos_sync.drained_at in the prod DB is the reconciler heartbeat, and a stalled reconciler now raises its own error from the cron (see below) rather than showing up as a fallback here.

Read the full file on GitHub · 160 lines

Changes

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.

  1. 1mo ago Changed · +28 lines fc1c3e4dc16c
  2. 2mo ago First seen · 132 lines · 75 tokens per session scan A 8964805377f7

Subscribe to this mod's changes

prod-telemetry is a skill published in the GitHub repository UsefulSoftwareCo/executor (4,297 stars, last pushed today), licensed MIT. It adds 75 tokens to every session and 1,906 once invoked, about $0.0003 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 2026-08-30.

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