SKILLEMALL.ai

BB pagerduty-escalation-architect

Design PagerDuty escalation policies, schedules, services, response plays, and incident workflows that handle real on-call traffic without burning out the rotation. Covers follow-the-sun rotations, primary/secondary patterns, weekly handoffs, escalation timeouts matched to severity, business-hour vs always-on services, override patterns for vacations, training shadow rotations, response play composition (status pages, Slack, conference bridges, Zoom auto-create), incident workflows, and postmortem auto-generation from PagerDuty timelines. Acts as a senior SRE who has run a 200-engineer on-call program across three time zones, audited the comp model for fairness, and survived multiple SOC2 audits of escalation evidence. Use when on-call is unfair, when escalations time out before humans see them, when a new service needs its routing set up, when M&A merges two PagerDuty tenants, or when comp/fairness math is overdue. Triggers on "pagerduty", "escalation policy", "on-call rotation", "schedule", "follow the sun", "primary secondary", "response play", "incident workflow", "on-call comp", "on-call fairness", "shadow rotation", "service routing", "pd routing".

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 3 files body ≈ 4 673 tokens Open the sourcegithub.com analyzed 3 d ago

Design PagerDuty escalation policies, schedules, services, response plays, and incident workflows that handle real on-call traffic without burning out the…

As a process B 68/100 · Nearly there — weak spots: running it twice

ProcedureSlackCustomer supportInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
B
86/100
safety, quality, tests
Safety 60%
100
Quality 40%
64
Run on models
none yet
Process rating
B
68/100
Nearly there
Running it twice w 4
30
Failures and branches w 10
50
Tools and files w 18
60
the three weakest of ten parameters · all ten

How to improve

  1. Shorten the description to 1024 characters.
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: 3. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • error description-long description is 1172 chars, limit 1024

Process rating: all ten parameters 68/100

  • 30Running it twice. 10 mutating operations with no state check
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (web) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 70When it triggers. States when to use, but not when not to
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 70Execution cost. Instruction body is 4673 tokens
  • 85Steps. 77 steps, 1 vague phrases
  • 100Consistency. Name and required fields are in place
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 10 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)
  • +3Description length 1172: 120–800 characters recommended
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 13 example trigger phrases
  • +4Structure: 28 headings
  • +3Step-by-step instructions: 77 items
  • +3Output format is stated explicitly
  • +4Has examples (20 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 64.