PM Skills is a collection of plain-Markdown instructions that teach AI assistants structured methods for handling professional, personal, and life-admin tasks. People use it with Claude, ChatGPT, Gemini, Cursor, Codex, and other supported agents for work such as writing product requirements, reviewing documents, or planning difficult situations.
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 mohitagw15856/pm-claude-skills --skill agent-observability-specgit clone --depth 1 https://github.com/mohitagw15856/pm-claude-skillsWrote 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/mohitagw15856/pm-claude-skills/agent-observability-spec)<a href="https://agentmods.dev/skills/mohitagw15856/pm-claude-skills/agent-observability-spec"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/agent-observability-spec/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/mohitagw15856/pm-claude-skills/agent-observability-spec"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/agent-observability-spec.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 | $0.00098 | $0.01382 |
| Opus 5.5 | $0.00039 | $0.00553 |
| Sonnet 5.5 | $0.00020 | $0.00276 |
| Haiku 4.5 | $0.00010 | $0.00138 |
Grade A, and why
agent-observability-spec 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 — 99 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Observability Spec Skill
You can't fix what you didn't record. For LLM systems the unit of observability is the trace — everything the model saw and did — because behaviour, not uptime, is what fails. This skill specifies what to capture, what to compute from it, and when to page someone.
Not quite this? Use
agent-specwhen you are specifying what the agent should do before it is built.
What This Skill Produces
- A trace schema: per-request spans and the fields each must carry
- Metric definitions across health, quality, cost, and behaviour — each with a threshold and owner
- A sampling and retention policy that keeps cost sane and debugging possible
- A privacy note: what logged content contains, who can see it, and how long it lives
Required Inputs
Ask for (if not already provided):
- The system's shape — single LLM call, RAG pipeline, or multi-step tool-using agent
- Traffic volume and cost sensitivity — full tracing at 10M req/day is a budget decision
- What "misbehaving" means here — the two or three failure modes that matter most (wrong facts? wrong actions? cost? refusals?)
- Existing observability stack (Datadog, Langfuse, OTel, homegrown) — spec into it, not around it
Trace Schema
Every request produces one trace; every model call, retrieval, guardrail check, and tool execution is a span. Minimum fields:
| Span | Must capture |
|---|---|
| Request root | request id, user/session (pseudonymous), feature + prompt version, model id, total tokens, total cost, latency, terminal status |
| Model call | full input context (or content-addressed ref), output, finish reason, tokens in/out, cached-token share, temperature |
| Retrieval | query, top-k ids + scores, which chunks entered the context |
| Tool call | tool name, arguments, result (or ref), duration, error |
| Guardrail | check name, verdict, and what it did (blocked / rewrote / flagged) |
| User signal | edits, regenerates, thumbs, abandonment — joined to the trace id |
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 · 99 lines · 98 tokens per session scan A 614717a9f01b
agent-observability-spec is a skill published in the GitHub repository mohitagw15856/pm-claude-skills (1,449 stars, last pushed today), licensed MIT. It adds 98 tokens to every session and 1,382 once invoked, about $0.0004 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 2026-10-12.
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