AC agent-memory-architect
Complete memory architecture for AI agents — tiered storage (HOT/WARM/COLD), auto-learning from corrections, self-reflection, multi-agent memory sharing, and intelligent decay. One-click setup gives any agent persistent memory that compounds over time. Use when: setting up agent memory, the agent needs to remember preferences/patterns/corrections, building multi-agent teams with shared knowledge, when asked about memory architecture/self-learning/self-improving agents, or when user says "记住这个", "remember this", "memory setup", "memory stats", "what do you know about me", "forget X".
As a process C 63/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
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 · 1
✓ No critical or high findings
Medium and low: 1
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low Risky intent
intent-offensive-securityreferences/security.md:13Offensive-security / dual-use content (legitimate for authorised testing; review intended use)| Access | System permissions, admin credentials | Privilege escalation |
Files scanned: 6. 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 63/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 5 mutating operations with no state check
- 100Tools and files. No external tools needed
- 100Steps. 22 steps
- 100Failures and branches. 1 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1494 tokens
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 10 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
- +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 5 example trigger phrases
- +3Description length 589: enough signal without eating the budget
- +4Structure: 19 headings
- +3Step-by-step instructions: 22 items
- +4Has examples (9 code blocks)
- +4Reference files are cited in the instructions (3 of 3)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 96.