SKILLEMALL.ai

BD Incident Commander Skill

The Incident Commander skill provides a comprehensive incident response framework for managing technology incidents from detection through resolution and post-incident review. This skill implements...

modbender/skill-library-mcp Agent Skills author: modbender MIT 25 files body ≈ 5 712 tokens Open the sourcegithub.com analyzed 3 d ago

The Incident Commander skill provides a comprehensive incident response framework for managing technology incidents from detection through resolution and…

As a process D 45/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
B
79/100
safety, quality, tests
Safety 60%
98
Quality 40%
50
Run on models
none yet
Process rating
D
45/100
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

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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 · 2

✓ No critical or high findings

Medium and low: 2
  • 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:129
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    1. **Command and Control**

Files scanned: 25. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning body-long SKILL.md body ≈ 5712 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 45/100

  • 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. 5 mutating operations with no state check
  • 40Consistency. Frontmatter name (Incident Commander Skill) differs from the folder (incident-commander)
  • 50Failures and branches. 0 branches, has a failure section
  • 60Steps. 232 steps, 4 vague phrases
  • 70Execution cost. Instruction body is 5712 tokens
  • 100Tools and files. No external tools needed
  • 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
  • -45 reference files, but SKILL.md never points to them: the model will not open them
  • -33 of 6 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 200: enough signal without eating the budget
  • +4Structure: 52 headings
  • +3Step-by-step instructions: 232 items
  • +4Has examples (8 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 50.