AC memory-lifecycle
Systematic memory management for long-running AI agents. Implements a five-tier lifecycle — heartbeat micro-attention, nightly consolidation, weekly reflection, monthly archiving, and yearly wisdom distillation. Use when setting up a new agent's memory system, improving an existing agent's memory quality, or when the agent's MEMORY.md is growing too large and context quality is degrading. Triggers on "set up memory", "memory management", "improve memory", "memory lifecycle", "nightly consolidation", "sleep cycle", "memory housekeeping".
As a process C 57/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency
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: 8. 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 57/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 8 mutating operations with no state check
- 40Consistency. Frontmatter name (memory-lifecycle) differs from the folder (agent-memory-lifecycle)
- 55Failures and branches. 1 branches
- 70When it triggers. States when to use, but not when not to
- 100Tools and files. No external tools needed
- 100Steps. 39 steps
- 100Execution cost. Instruction body is 1312 tokens
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
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 7 example trigger phrases
- +3Description length 542: enough signal without eating the budget
- +4Structure: 13 headings
- +3Step-by-step instructions: 39 items
- +4Has examples (4 code blocks)
- +4Reference files are cited in the instructions (4 of 4)
- +3All 2 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 96.