AB adaptive-learning-playbook
World-Class Adaptability & Learning Playbook. Use for: market trend awareness, horizon scanning, PESTLE analysis, organisational agility, Kaizen, PDCA cycles, 5S, lean operations, experimentation culture, hypothesis-driven development, A/B testing, MVP design, knowledge management, decision logs, ADRs, after-action reviews, competitive intelligence, SWOT, Porter's Five Forces, battlecards, pivoting strategy, lean startup, business model canvas, signal detection, scenario planning, learning velocity, value stream mapping, Gemba walks. Trigger when discussing ANY organisational learning, strategic adaptability, continuous improvement, competitive analysis, experimentation, knowledge systems, or pivot/persevere decisions. Also for startup strategy around product-market fit or validated learning. If it touches learning faster, adapting better, or competing smarter — use this skill.
As a process B 70/100 · Nearly there — weak spots: result and completion, inputs and preconditions, running it twice
How to improve
- 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: 4. 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 70/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 30Running it twice. 9 mutating operations with no state check
- 40Result and completion. Does not say what the result is
- 60Failures and branches. 2 branches
- 70When it triggers. States when to use, but not when not to
- 100Tools and files. No external tools needed
- 100Steps. 81 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3797 tokens
- 100Progress reporting. Reports progress
- low 11 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)
- +3Description length 890: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +4Structure: 41 headings
- +3Step-by-step instructions: 81 items
- +4Has examples (3 code blocks)
- +4Reference files are cited in the instructions (1 of 1)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.