Observal Ops

Name: Observal Ops
Author: Observal

By Observal· Observal/Observal· 0

Inspect traces, logs, and telemetry to diagnose agent health and generate operational reports.

Installation

1
Make sure Claude is on your device and in your terminal.
Skills load from ~/.claude/skills/ when Claude Code starts up — so you need it on your machine first. If you don't have it yet, install it once with the command below, then run claude in any terminal to verify.
One-time setup
```
npm i -g @anthropic-ai/claude-code
```
Already have it? Skip ahead.

Paste into Claude Code or into your terminal.

Install

git clone ht•••••••••••••••••••••••••••••••••••••• ••••••••••••••••••••••• •• ••••• •• •••••••••••••••••••••••••••••••••••••• •• •• •• •••••••••••••••••••••••••••••••••••••••••••••••••••••••••• •••••••••••••••••••••••••••••••••••••••

This copies the whole skill folder into ~/.claude/skills/observal-ops-observal/ — the SKILL.md plus any scripts, reference docs, or templates the skill ships with. Safe default: works for every skill.

Faster alternative (instruction-only skills)

Skips the clone and grabs only the SKILL.md file. Don't use this if the skill ships Python scripts, reference markdowns, or asset templates — they won't be downloaded and the skill will fail when it tries to load them.

Quick install (SKILL.md only)

mkdir -p ~/.••••••••••••••••••••••••••••••••••• •• •••• ••••• •••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• •• •••••••••••••••••••••••••••••••••••••••••••••••

Restart Claude Code.
Quit and reopen Claude Code (or any other agent that loads from ~/.claude/skills/). New skills are picked up on startup.
Just ask Claude.
Skills auto-activate when your request matches the skill's description — no slash command needed. Trigger phrases live in the skill's own frontmatter; you can read them in the “What this skill does” section above.

Prefer to read the source first? Open on GitHub.

When Claude uses it

Inspects Observal traces, sessions, rankings, feedback, telemetry health, logs, and Agent insight reports. Use when the user wants operational evidence, current activity, telemetry diagnosis, ratings, report generation, regression analysis, or recommendations grounded in Agent usage.

What this skill does

Observing Observal

Execution contract

Execute commands with a 60 second timeout. Use a longer timeout only for report generation or an intentional stream.
Use machine output by default: add --output json whenever supported. Parse list envelopes, finite objects, and JSON Lines separately.
Run --help before acting when a path or flag is uncertain.
Start with the narrowest read that answers the question. Do not generate a new report when a completed report already suffices.
Ground every conclusion in returned fields, report period, and sample size. Distinguish missing data from healthy data.
Verify rating mutations and report generation results.
Never repeat secrets or sensitive trace and log content unless the user explicitly requests the specific data and is authorized.
After an uncertain rating or report-generation failure, read current state before retrying.

Choose the workflow

User intent	Read
Sessions, traces, rankings, ratings, telemetry, or logs	Operational workflows
Agent health, friction, costs, versions, regressions, or suggestions	Insight reports

Read the selected reference completely before executing.

Analysis rules

A zero exit status from telemetry diagnosis can still contain issues or warnings. Read the JSON health fields.
No events is not automatically a hook problem. Check authentication, server reachability, local outbox, and hook state in that order.
Quote report evidence accurately and say when session count is thin.
Only component_ref proves an insight suggestion maps to a Registry component. Never reconstruct a component identity from prose.
Lead with reuse suggestions before create-new suggestions.
For logs and traces, summarize the minimum sensitive content needed to answer the question.

Completion

Report the time range, filters, counts, health state, strongest evidence, uncertainty, and concrete next action. Do not present popularity fallback or sparse reports as personalized certainty.

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