Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add honeycombio/agent-skill/plugin install honeycombWrote 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/skills/honeycombio/agent-skill/production-investigation)<a href="https://agentmods.dev/skills/honeycombio/agent-skill/production-investigation"><img src="https://agentmods.dev/badge/skills/honeycombio/agent-skill/production-investigation/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/skills/honeycombio/agent-skill/production-investigation"><img src="https://agentmods.dev/badge/skills/honeycombio/agent-skill/production-investigation.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 | $0.00112 | $0.01719 |
| Opus 5 | $0.00056 | $0.00860 |
| Sonnet 5 | $0.00022 | $0.00344 |
| Haiku 4.5 | $0.00011 | $0.00172 |
Grade A, and why
production-investigation 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 11d 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 — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Honeycomb Production Investigation
Structured workflows for debugging production issues. The MCP tools document their own parameters — this skill focuses on the sequence of tool calls and how to interpret results to reach a root cause.
The Core Analysis Loop
This workflow implements the core analysis loop (Define → Visualize → Investigate → Evaluate) from the observability-fundamentals skill. If BubbleUp returns nothing useful, the issue is often an instrumentation gap — add the missing attributes (see the otel-instrumentation skill) and try again.
Investigation Workflow
Step 1: Orient
get_workspace_context→ environments and datasetsget_slos→ any SLOs in violation? (frames severity)get_triggers→ any alerts firing? (narrows scope)find_queries→ has anyone investigated this before?
Step 2: Characterize the Problem
Run a broad query to see the shape of the issue:
- Latency spike: P99(duration_ms), HEATMAP(duration_ms) grouped by service or route
- Error surge: count failed operation spans (
error=true) by service/route/category, then separately count exception event rows usingevent.name=exceptionandexception.type exists; use sampledtrace.trace_idvalues to drill into representative traces - Unknown: COUNT grouped by service.name to find which service has anomalous volume
Also call get_service_map — it shows P95 durations between services and can immediately reveal which dependency is slow.
Exception data has two query surfaces: operation failures belong on spans (error=true, span
status, low-cardinality exception.slug/error category); full exception diagnostics may belong on
trace-correlated Logs API event rows. Do not assume exception.* exists on the containing span.
When investigating exceptions, discover the dataset schema first, query event.name=exception
with exception.type exists and trace.trace_id exists, take a sample, then pass its
trace.trace_id to get_trace(show_events=true). For legacy span-event exceptions, also check
name=exception and meta.signal_type=trace; Logs API events use event.name/body and
meta.signal_type=log.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 138 lines · 112 tokens per session scan A 60b3b1c4c5b1
production-investigation is a skill published in the GitHub repository honeycombio/agent-skill (22 stars, last pushed 14d ago), licensed MIT. It adds 112 tokens to every session and 1,719 once invoked, about $0.0006 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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