AD agentmemory
Persistent cross-session memory for OpenClaw via agentmemory. Use when: - User asks to install/configure agentmemory for OpenClaw - Agent needs to remember project context, decisions, or workflows across sessions - Setting up long-term memory for OpenClaw (MCP mode) - Integrating agentmemory with existing MEMORY.md system - Troubleshooting agentmemory connection issues - Upgrading or removing agentmemory ─────────────────────────────── 基于 agentmemory 的跨会话持久化记忆系统，适用于 OpenClaw。 适用场景： - 用户要求安装/配置 agentmemory - Agent 需要记住项目背景、决策或工作流程 - 为 OpenClaw 设置长期记忆（MCP 模式） - 与现有 MEMORY.md 系统集成 - 排查 agentmemory 连接问题 - 升级或移除 agentmemory
As a process D 49/100 · Unfinished process — 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 · 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 49/100
- 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
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 2 mutating operations with no state check
- 40Consistency. Frontmatter name (agentmemory) differs from the folder (agentmemory-mcp)
- 60Tools and files. Uses tools (bash) that frontmatter does not declare
- 100Steps. 28 steps
- 100Failures and branches. 1 branches, has a failure section
- 100Execution cost. Instruction body is 3781 tokens
- 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
- +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
- -2localhost URLs: will not work for another user
- -219 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +3Description length 630: enough signal without eating the budget
- +4Structure: 51 headings
- +3Step-by-step instructions: 28 items
- +4Has examples (44 code blocks)
- +4Reference files are cited in the instructions (1 of 2)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.