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 skills add nota-america/forgecat-agent-profiles --skill observability-and-instrumentationgit clone --depth 1 https://github.com/nota-america/forgecat-agent-profilesWrote 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/nota-america/forgecat-agent-profiles/observability-and-instrumentation)<a href="https://agentmods.dev/skills/nota-america/forgecat-agent-profiles/observability-and-instrumentation"><img src="https://agentmods.dev/badge/skills/nota-america/forgecat-agent-profiles/observability-and-instrumentation/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/nota-america/forgecat-agent-profiles/observability-and-instrumentation"><img src="https://agentmods.dev/badge/skills/nota-america/forgecat-agent-profiles/observability-and-instrumentation.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 | $0.00066 | $0.03181 |
| Opus 5.5 | $0.00026 | $0.01272 |
| Sonnet 5.5 | $0.00013 | $0.00636 |
| Haiku 4.5 | $0.00007 | $0.00318 |
Grade A, and why
observability-and-instrumentation 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.
This is a copy
84% identical to observability-and-instrumentation — 8 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 243 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Observability and Instrumentation
Overview
Code you can't observe is code you can't operate. Observability is the ability to answer "what is the system doing and why?" from the outside, using the telemetry the code emits. Instrumentation is not a post-launch add-on — it's written alongside the feature, the same way tests are. If a feature ships without telemetry, the first user-reported bug becomes archaeology instead of a query.
When to Use
- Building any feature that will run in production
- Adding a new service, endpoint, background job, or external integration
- A production incident took too long to diagnose ("we couldn't tell what happened")
- Setting up or reviewing alerting rules
- Reviewing a PR that adds I/O, retries, queues, or cross-service calls
NOT for:
- Diagnosing a failure happening right now — use the
debugging-and-error-recoveryskill (observability is what makes that skill fast next time) - Profiling and optimizing measured slowness — use the
performance-optimizationskill - Launch-day monitoring checklists and rollback triggers — see the
shipping-and-launchskill; this skill covers the instrumentation that feeds them
Process
1. Define "working" before instrumenting
Telemetry without a question is noise. Before adding any instrumentation, write down 2–4 questions an on-call engineer will ask about this feature:
FEATURE: checkout payment retry
QUESTIONS ON-CALL WILL ASK:
1. What fraction of payments succeed on first attempt vs after retry?
2. When a payment fails permanently, why? (provider error? timeout? validation?)
3. Is the payment provider slower than usual?
→ Every signal below must help answer one of these.
If you can't name the questions, you're not ready to instrument — you'll log everything and learn nothing.
2. Pick the right signal for each question
| Signal | Answers | Cost profile | Example |
|---|---|---|---|
| Structured log | "What happened in this specific case?" | Per-event; grows with traffic | payment_failed with provider error code |
| Metric | "How often / how fast, in aggregate?" | Fixed per series; cheap to query | p99 latency of provider calls |
| Trace | "Where did time go across services?" | Per-request; usually sampled | One slow checkout, broken down by hop |
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.
- 1mo ago First seen · 243 lines · 66 tokens per session scan A 92f031091938
observability-and-instrumentation is a skill published in the GitHub repository nota-america/forgecat-agent-profiles (93 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 66 tokens to every session and 3,181 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 84% identical to observability-and-instrumentation, differing in 8 lines, and is treated as a copy.
Other skills, from other repositories
code_fail_skill
This is a mock skill of Coding Agent to process failure log.
systematic-debugging
Use when debugging a failing test, build error, or runtime issue that isn't immediately obvious. Guides a 4-phase root cause analysis instead of random fix attempts.
deck-retro
Run a fully local agent-deck retrospective over the user's own transcripts, Recall index and logs. Use when a user wants to find recurring failures or take their own finding from investigation through synthetic reproduction, a user-filed issue, a test-first fix and contributor PR handoff. Also use for weekly usage…
loop-engineering
Shared loop-engineering reference for COG skills - the agent loop, deterministic verifiers, termination conditions, in-loop context management, and named patterns. Invoke when designing or debugging a skill that iterates (search-verify-retry, scan-until-dry, fetch-retry-gate).
opik-diagnose
Surface the Opik traces worth a developer's attention, ranked by signal — Diagnostics issues first, then errors, failed tool calls, latency, regressions, and low online-eval scores. With the Opik MCP connected it lists the project's agentinsightsissue entities, offers to turn Diagnostics on when the project has it…
qa-knowledge
To run QA engineering — requirements/gap analysis, scenario & spec design, test implementation, failure triage — over the QA knowledge base.