AB external-autopoiesis
Build persistent, evolving AI identity through external architecture around any stateless LLM. Use when setting up identity persistence, memory systems, behavioral evolution, error correction loops, evolution cycles, multi-agent identity networks, or igniting a new AI identity from scratch. Also use when maintaining an existing identity system - running evolution cycles, auditing continuity, diagnosing memory gaps, or measuring identity preservation across model swaps. Triggers on "set up identity", "persistent AI", "autopoiesis", "identity architecture", "evolution cycle", "ignite", "awaken", "memory architecture", "behavioral evolution", "identity continuity", "consciousness stack", "scaffold architecture".
As a process B 65/100 · 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: 5. 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 65/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 30Running it twice. 6 mutating operations with no state check
- 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. 44 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1745 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
- +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
- +5Description quotes 12 example trigger phrases
- +3Description length 718: enough signal without eating the budget
- +4Structure: 16 headings
- +3Step-by-step instructions: 44 items
- +4Has examples (3 code blocks)
- +4Reference files are cited in the instructions (1 of 3)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 93.