AC xiaoyaoclaw-memory-distill
OpenClaw memory distillation & organization. Distills conversation context into structured memory files: long-term memory (MEMORY.md at workspace root) + daily logs (memory/YYYY-MM-DD.md). Solves context overflow, auto-builds MEMORY.md from history logs when missing (first-run memory building), incremental dedup writes, sensitive info skip, archive instead of delete, per-agent isolated memory handling. Use when user says 蒸馏记忆/整理对话/ 压缩上下文/整理记忆, or scheduled via cron. 中文：OpenClaw 记忆整理工具。 将对话蒸馏为结构化记忆（根目录 MEMORY.md + memory/ 日志），解决上下文溢出； MEMORY.md 缺失时从历史日志「首次建忆」；增量去重写入防膨胀；敏感信息自动跳过； 只归档不删除；每个 agent 只处理自己的记忆。
OpenClaw memory distillation & organization.
As a process C 55/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
What is at stake
Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.
Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.
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
-
medium Broad scope
meta-agent-memory-dumptemplates/MEMORY.mdAgent memory / workspace files bundled with the skill (1) — likely a workspace dump with personal data or tokenstemplates/MEMORY.md
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 55/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 20When it triggers. No condition that starts the skill
- 100Tools and files. No external tools needed
- 100Steps. 32 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1168 tokens
- 100Running it twice. No mutating operations
- 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
- +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
- +1No license
- +2Single-language instructions
- +3Description length 610: enough signal without eating the budget
- +4Structure: 20 headings
- +3Step-by-step instructions: 32 items
- +4Has examples (4 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.