SKILLEMALL.ai

AD incident-commander

Use when declaring or running a production incident or outage, or writing its post-incident review. Classifies severity with incident_classifier.py, rebuilds the timeline with timeline_reconstructor.py and drafts the PIR with pir_generator.py. Security incidents: incident-response.

alirezarezvani/claude-skills Agent Skills author: alirezarezvani MIT 23 files · 3 scripts body ≈ 3 875 tokens Open the sourcegithub.com↗ analyzed 8 d ago

Classifies severity with incidentclassifier.py, rebuilds the timeline with timelinereconstructor.py and drafts the PIR with pirgenerator.py. Security…

As a process D 44/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
97
Quality 40%
91
Run on models
none yet
Process rating
D
44/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

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 · 3

    ✓ No critical or high findings

    Medium and low: 3
    • low Risky intent intent-offensive-security references/incident-response-framework.md:70
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      | **SEV1** | Total service outage or data breach affecting all users. Revenue loss exceeding $10K/hour. Security incident with active exfiltration. | Page IC + on-call within 5 min. All hands mobilize
    • low Risky intent intent-offensive-security SKILL.md:18
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      **This is NOT security incident triage.** For security events (ransomware, intrusion, data exfiltration, IOC analysis, NIST SP 800-61 forensics), route to `incident-response`. Both skills use SEV1-SEV
    • low Risky intent intent-offensive-security SKILL.md:129
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      1. **Command and Control**

    Files scanned: 23. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 44/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 3 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
    • 60Steps. 161 steps, 4 vague phrases
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3875 tokens
    • 100Progress reporting. Reports progress

    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

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 282: enough signal without eating the budget
    • +4Structure: 39 headings
    • +3Step-by-step instructions: 161 items
    • +4Has examples (5 code blocks)
    • +4Reference files are cited in the instructions (6 of 6)
    • +3All 3 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.