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AC slo-architect

Use when defining, reviewing, or operating SLOs/SLIs/error budgets. Triggers on "define an SLO", "what should our SLO be", "error budget", "burn rate", "SLI", "service level objective", "Google SRE workbook", "multi-window burn-rate alert", or any reliability-target question. Ships SLO designer, error-budget calculator with multi-window burn-rate thresholds, and SLO reviewer that catches the common bugs (target too aggressive, window too short, conflicting SLOs, no SLI definition). 4 references on SLO principles + SLI design + error budget math + composition with feature-flags-architect/chaos-engineering/kubernetes-operator. NOT a generic observability skill — specifically the SLO discipline.

alirezarezvani/claude-skills Agent Skills author: alirezarezvani MIT 10 files body ≈ 2 358 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Triggers on "define an SLO", "what should our SLO be", "error budget", "burn rate", "SLI", "service level objective", "Google SRE workbook", "multi-window…

As a process C 57/100 · Has gaps — weak spots: result and completion, inputs and preconditions, progress reporting

AnalyzerKubernetesInfrastructuretype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
A
98/100
safety, quality, tests
Safety 60%
100
Quality 40%
96
Run on models
none yet
Process rating
C
57/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: slo-architect (alirezarezvani/claude-skills)

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 0

    ✓ No critical or high findings

    Files scanned: 10. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "compatible_tools"
    • note edit-residue the text marks something as outdated (lines 177): check that old rules are not kept next to new ones — the full check reads the text for contradictions

    Process rating: all ten parameters 57/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 52 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2358 tokens
    • 100Running it twice. No mutating operations
    • low 14 top-level sections: this looks like several domains in one skill

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +2Single-language instructions
    • +5Description quotes 7 example trigger phrases
    • +3Description length 701: enough signal without eating the budget
    • +4Structure: 21 headings
    • +3Step-by-step instructions: 52 items
    • +4Has examples (8 code blocks)
    • +4Reference files are cited in the instructions (4 of 4)
    • +3All 3 scripts are documented
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 96.