Slo Architect

What it does

When your agent uses it

• Operating SLOs/SLIs/error budgets
• What should our SLO be
• Service level objective
• Google SRE workbook

Example prompts

• “define an SLO”
• “what should our SLO be”
• “error budget”
• “/slo-architect”

Requirements

• Python 3

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

1. 1Target too high (99.99%+ on services that can't support it) — every minor blip violates SLO; alerts become noise.
2. 2Wrong SLI (CPU usage as proxy for user experience) — system can be "green" while users suffer.
3. 3No error budget policy — burning budget means nothing if there's no agreed action.
4. 4Single-window burn-rate alert — either too noisy (page on a 5-min spike) or too slow (notice budget exhausted after the fact).

What it can do on your machine

Read from SKILL.md and the folder at commit 19392f7. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  Ships 3 files in scripts/ (Python), which the agent can run.

  Shell commands in SKILL.md call:

  • python

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  No URLs in SKILL.md.

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Slo Architect loads about 2.6k tokens when it runs, and up to ~7.8k if it reads all its reference files. Until then it costs about 179 tokens; SKILL.md has 842 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~179

When it runs · the whole SKILL.md, loaded when a task matches

~2.6k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~7.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from alirezarezvani/claude-skills at commit 19392f7, republished under its MIT licence (© alirezarezvani). 842 words, ~2,608 tokens.

name

slo-architect

description

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.

context

fork

version

2.9.0

author

claude-code-skills

license

MIT

tags

slo, sli, sla, error-budget, burn-rate, sre, reliability, google-sre-workbook, observability

compatible_tools

claude-code, codex-cli, cursor, antigravity, opencode, gemini-cli

SLI  ⟶  measurable signal of user-perceived health (e.g., HTTP 2xx rate)
SLO  ⟶  target for the SLI over a window (e.g., 99.9% over 30 days)
SLA  ⟶  customer-facing commitment with consequences (separate concern)
EB   ⟶  error budget: 100% − SLO target = how much "bad" you can spend
BR   ⟶  burn rate: how fast you're consuming the error budget

SKILL=engineering/slo-architect/skills/slo-architect

# 1. Design an SLO
python "$SKILL/scripts/slo_designer.py" \
  --service checkout-svc \
  --sli-type request-success-rate \
  --target 99.9 \
  --window-days 30

# 2. Compute error budget + multi-window burn-rate alerts
python "$SKILL/scripts/error_budget_calculator.py" \
  --target 99.9 --window-days 30

# 3. Review existing SLO definitions for common bugs
python "$SKILL/scripts/slo_review.py" --slo-doc docs/slos/

python scripts/slo_designer.py \
  --service checkout-svc \
  --sli-type request-success-rate \
  --target 99.9 \
  --window-days 30 \
  --owner team-checkout

python scripts/error_budget_calculator.py --target 99.9 --window-days 30
python scripts/error_budget_calculator.py --target 99.95 --window-days 7 --format json

python scripts/slo_review.py --slo-doc docs/slos/

1. Pick the user journey to protect (e.g., "checkout completion").
2. Choose SLI type (request-success-rate, latency, availability, freshness, correctness).
3. Define the SLI precisely: numerator/denominator with concrete labels.
4. Pick a target by measuring 30 days of historical SLI value:
     target = floor(p50 of last 30 days × 100) / 100
   This avoids targets the system has never sustained.
5. Pick a window (28 days = 4 calendar weeks, recommended).
6. Run slo_designer.py to render the SLO definition.
7. Run error_budget_calculator.py to get burn-rate alerts.
8. Write the error budget policy (what happens when budget burns).
9. Run slo_review.py — must pass before the SLO is "live".

1. For every active SLO, run slo_review.py — fix any FAIL findings.
2. Look at last quarter's data:
   - Was the SLO too easy (never burned budget)? Tighten target.
   - Was it too hard (frequently burned)? Loosen target OR fix the system.
   - Did burn-rate alerts fire usefully (not too noisy, not too late)? Adjust thresholds.
3. Audit error budget policies — were they actually followed when budget burned?
4. Commit revised SLOs; archive old versions with date stamps.

1. New deploy starts burning error budget faster than baseline.
2. Burn-rate alert fires (from error_budget_calculator.py thresholds).
3. Auto-rollback via feature flag (kill switch from feature-flags-architect).
4. Postmortem feeds into next SLO revision.

© alirezarezvani, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 9 other files (scripts, references, assets) in engineering/skills/slo-architect of alirezarezvani/claude-skills.

• SKILL.md
• assets/error_budget_policy.md
• assets/slo_template.yaml
• references/composition.md
• references/error_budget.md
• references/sli_design.md
• references/slo_principles.md
• scripts/error_budget_calculator.py
• scripts/slo_designer.py
• scripts/slo_review.py

Open the folder on GitHubat commit 19392f7