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Slo Check

CommunityPopular
hoangsonww
slo-check

Define and check simple service-level objectives for Claude Code from Agent Monitor data — session completion rate, tool success rate (PostToolUse/PreToolUse), and error rate (APIError/total) — then compare each to its target and report the error budget remaining. Use when reporting reliability or when someone asks "are we meeting our SLOs?".

Overview

Publisherhoangsonww
RepositoryClaude-Code-Agent-Monitor
Skill nameslo-check
Stars
1.1K
Forks
260
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by hoangsonww on GitHub. Read the source before you install it.

Installation

Install the Slo Check AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/hoangsonww/Claude-Code-Agent-Monitor/tree/master/plugins/ccam-quality/skills/slo-check
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/hoangsonww/Claude-Code-Agent-Monitor.git /tmp/Claude-Code-Agent-Monitor
mkdir -p .claude/skills
cp -r /tmp/Claude-Code-Agent-Monitor/plugins/ccam-quality/skills/slo-check .claude/skills/slo-check
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Slo Check in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Slo Check on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Slo Check is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

SLO Check

Turn raw event counts into a clear SLO scorecard with error-budget accounting.

Input

The user provides: $ARGUMENTS

This may be:

  • empty — use the default SLO targets below over all available data
  • targets like "completion=95 success=99 error=1" — override the defaults (percentages)
  • a window like "last 7d" or "today" — restrict the measurement period

Default SLO targets: completion rate ≥ 95%, tool success rate ≥ 99%, error rate ≤ 1%.

Data Sources

EndpointReturns
GET /api/analyticsevent_types (PreToolUse, PostToolUse, APIError counts), sessions_by_status, daily_events (365d), daily_sessions (365d) — the raw numerators/denominators for every SLI
GET /api/statstotal_sessions, total_events, events_today, sessions_by_status — fleet totals and recency
GET /api/events?session_id=XPer-session stream — drill into the sessions that breach an SLO

Report Sections

1. Service Level Indicators (SLIs)

Compute each SLI from GET /api/analytics / GET /api/stats:

  • Completion rate = completed sessions / total sessions (from sessions_by_status; count active/running as in-flight, exclude them from the denominator if still open).
  • Tool success rate = PostToolUse / PreToolUse (capped at 100%).
  • Error rate = APIError / total_events. Within a window, derive the numerators/denominators from daily_events / daily_sessions.

2. SLO Scorecard

For each SLI, compare to its target and mark MET ✅ or BREACHED ❌.

3. Error Budget

For each objective, report the error budget and how much remains:

  • Budget = 1 − target (e.g., 1% for a 99% target).
  • For "higher-is-better" SLOs (completion, success): remaining = (observed − target) / (1 − target).
  • For "lower-is-better" SLOs (error rate): remaining = (target − observed) / target.
  • A negative result means the budget is exhausted — report how far over (e.g., "2.7× over budget").

4. Breach Drill-Down

For any breached SLO, list the sessions contributing most to the breach (most failed tools or most APIErrors) via GET /api/events?session_id=X.

Output

  • A Markdown scorecard table: SLI | observed | target | status | error budget remaining.
  • Rates as percentages to 2 decimals; any currency in USD to 4 decimals.
  • Cite exact counts and session_id values — never fabricate numerators or denominators.
  • End with the SLO most at risk and the single action that would recover the most budget.
  • Read-only: only report what the API returns. If curl cannot reach http://localhost:4820, tell the user to start the dashboard with npm start from the repo root.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Slo Check AI skill do?

Define and check simple service-level objectives for Claude Code from Agent Monitor data — session completion rate, tool success rate (PostToolUse/PreToolUse), and error rate (APIError/total) — then compare each to its target and report the error budget remaining. Use when reporting reliability or when someone asks "are we meeting our SLOs?".

Why use Slo Check on TypingMind?

Because you install it once and use it with any model. Slo Check is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Slo Check in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/hoangsonww/Claude-Code-Agent-Monitor/tree/master/plugins/ccam-quality/skills/slo-check. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Slo Check?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Slo Check?

As many as you like. As long as a model supports skills, you can use Slo Check with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Slo Check AI skill free?

Yes. It is published on GitHub by hoangsonww under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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