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/observability-fundamentals)<a href="https://agentmods.dev/skills/honeycombio/agent-skill/observability-fundamentals"><img src="https://agentmods.dev/badge/skills/honeycombio/agent-skill/observability-fundamentals/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/observability-fundamentals"><img src="https://agentmods.dev/badge/skills/honeycombio/agent-skill/observability-fundamentals.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 | $0.00145 | $0.01497 |
| Opus 5 | $0.00072 | $0.00749 |
| Sonnet 5 | $0.00029 | $0.00299 |
| Haiku 4.5 | $0.00015 | $0.00150 |
Grade A, and why
observability-fundamentals 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 9d 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.
Observability Fundamentals
First principles behind Honeycomb's approach to observability. Use this to ground recommendations and answer conceptual questions — for SDK setup and tool-specific guidance, see the otel-instrumentation and query-patterns skills.
Definitions
Observability: The ability to understand and explain any state your system can get into, no matter how novel or complex — by examining what the system produces, without deploying new code for each new question.
Wide event: A flat key-value record capturing the full context of a unit of work — who made the request, which endpoint, cache hit/miss, build version, duration, error status, and any business context relevant to the operation. In OpenTelemetry, a span is a wide event.
High cardinality: The number of unique values a field can have. user.id with
millions of values is high cardinality. http.method with a handful is low cardinality.
High dimensionality: The number of distinct fields on your events. A span with 50 attributes has high dimensionality.
| Concept | Observability | Traditional Monitoring |
|---|---|---|
| Questions | Arbitrary, unknown ahead of time | Pre-defined (dashboards, alerts) |
| Data shape | Decided at query time | Decided at instrumentation time |
| Cardinality | High cardinality is valuable | High cardinality is expensive |
| Investigation | Explore → narrow → confirm | Check dashboard → escalate |
Why Wide Events
The shape of the data you collect constrains the questions you can ask later. Metrics pre-aggregate context away at instrumentation time. Wide events preserve context and let you decide the shape of your analysis at query time.
Every attribute on a span is a queryable dimension. Adding user.id, deployment.version,
and cache.hit to the same span lets you correlate them in a single query — "slow
requests are from tenant X on version 2.3.1 with cache misses." Separate metrics can't
do this because each dimension combination creates a new time series.
What ships with it
1 file 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.
- 9d ago First seen · 132 lines · 145 tokens per session scan A b5e42f75d37f
observability-fundamentals is a skill published in the GitHub repository honeycombio/agent-skill (22 stars, last pushed 13d ago), licensed MIT. It adds 145 tokens to every session and 1,497 once invoked, about $0.0007 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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