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BD Session Memory Extractor

OpenClaw 版 Claude-Mem：自动扫描旧 Session 文件，AI 提炼决策、偏好、事实，追加写入 memory/；同步清理 .jsonl + .trajectory.jsonl，释放数百 MB 磁盘。提炼比清理先跑，记忆不丢失；提炼失败的文件自动 Quarantine 不删，避免静默丢数据。

ClawHub Agent Skills author: lmqiang-711 v1.0.6 MIT-0 7 files · 2 scripts body ≈ 2 119 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process D 41/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ReferenceInfrastructureAI and agentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
B
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
69
Run on models
none yet
Process rating
D
41/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

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: 7. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
    • note frontmatter-key unknown frontmatter key "slug"
    • note frontmatter-key unknown frontmatter key "homepage"
    • note frontmatter-key unknown frontmatter key "changelog"

    Process rating: all ten parameters 41/100

    • 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
    • 30Running it twice. 3 mutating operations with no state check
    • 40Consistency. Frontmatter name (Session Memory Extractor) differs from the folder (session-memory-extractor)
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 100Steps. 58 steps
    • 100Execution cost. Instruction body is 2119 tokens
    • 100Progress reporting. Reports progress
    • low 14 top-level sections: this looks like several domains in one skill
    • low The response is described with custom markup (3 tags): a typed call is more reliable

    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
    • -5TODO / placeholder text left in the skill
    • -226 emoji in the instructions: noise for the model
    • +1No license
    • +2Single-language instructions
    • +3Description length 155: enough signal without eating the budget
    • +4Structure: 28 headings
    • +3Step-by-step instructions: 58 items
    • +4Has examples (11 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.

    External checks

    ClawHub: suspicious
    This skill has a coherent session-cleanup purpose, but it handles highly sensitive session data and credentials with enough under-disclosed network and deletion behavior that users should review it carefully before installing.
    LLM: suspicious (high) · VirusTotal: · 22 Jun 2026