AC human-like-memory
HumanLike Memory / Human-Like Memory is a persistent AI agent memory system for long-term memory search, recall, and save. It helps agents store facts, preferences, and decisions and keep context across sessions for conversation continuity.
As a process C 54/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency
How to improve
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 · 0
✓ No critical or high findings
Files scanned: 9. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- note
frontmatter-keyunknown frontmatter key "homepage"
Process rating: all ten parameters 54/100
- 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. 2 mutating operations with no state check
- 40Consistency. Frontmatter name (human-like-memory) differs from the folder (humanlike-memory)
- 60Tools and files. Uses tools (bash) that frontmatter does not declare
- 60Failures and branches. 2 branches
- 100Steps. 30 steps
- 100When it triggers. States when to use and when not to
- 100Execution cost. Instruction body is 1191 tokens
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
- -32 of 3 scripts are never mentioned in SKILL.md
- +2Single-language instructions
- +3Description length 240: enough signal without eating the budget
- +4Structure: 15 headings
- +3Step-by-step instructions: 30 items
- +4Has examples (7 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.
External checks
ClawHub: clean
This is a disclosed remote long-term memory skill, with privacy risks users should manage but no artifact-backed evidence of hidden or malicious behavior.
LLM: benign (high) · VirusTotal: · 29 May 2026