AC tech-data-playbook
World-Class Technology & Data Playbook. Use for: software development best practices, IT infrastructure design, cybersecurity strategy, data analytics, business intelligence, automation & DevOps, cloud computing architecture, AI/ML adoption, technical architecture decisions, digital transformation strategy, platform engineering, CI/CD pipelines, zero-trust security, data governance, FinOps, edge computing, observability, MLOps, and technology leadership. Trigger when discussing ANY technology strategy, engineering practice, data platform, security posture, cloud architecture, AI implementation, or digital transformation topic. If in doubt, use this skill.
World-Class Technology & Data Playbook.
As a process C 58/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches
How to improve
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- 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
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low Risky intent
intent-offensive-securitySKILL.md:132Offensive-security / dual-use content (legitimate for authorised testing; review intended use)8. **Network Segmentation** — Micro-segmentation prevents lateral movement after initial compromise
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low Risky intent
intent-offensive-securitySKILL.md:152Offensive-security / dual-use content (legitimate for authorised testing; review intended use)- Documented runbooks for top 5 scenarios (ransomware, data breach, DDoS, insider threat, supply chain)
Files scanned: 3. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 6588 tokens (recommended < 5000); move details to references/ - note
edit-residuethe text marks something as outdated (lines 345, 373): check that old rules are not kept next to new ones — the full check reads the text for contradictions
Process rating: all ten parameters 58/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 30Running it twice. 17 mutating operations with no state check
- 70Execution cost. Instruction body is 6588 tokens
- 85Steps. 68 steps, 3 vague phrases
- 100Tools and files. No external tools needed
- 100When it triggers. States when to use and when not to
- 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 12 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
- +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 663: enough signal without eating the budget
- +4Structure: 52 headings
- +3Step-by-step instructions: 68 items
- +4Has examples (9 code blocks)
- +4Reference files are cited in the instructions (1 of 1)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.