当前公开展示的是 Workflow Crystallizer。本作品已批准的版本仍可按原地址安装和更新。

Workflow Crystallizer

分析 memory/YYYY-MM-DD.md 记忆日志，检测重复请求、多步工作流、时间相关任务等模式，生成 cron 任务、技能草稿或工作流快捷方式建议；用 state.json 持久化已建议与接受/拒绝状态，只输出有证据支持的新建议，最终打印 Markdown 报告。

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最近更新 2026-05-11 17:36:59

分类：自动化依赖：代码执行 / 读写文件应用场景：工作流 / 任务管理 / 报告撰写 / 自动化脚本 ...

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add nai-workflow-crystallizer

Metadata

name: workflow-crystallizer
description: Analyze memory logs to detect recurring patterns and suggest automations — cron jobs, skills, or workflow shortcuts. The agent builds its own shortcuts over time. Use when: pattern analysis, workflow patterns, recurring tasks, what do I keep doing, suggest automations, crystallize workflows, detect habits, optimize my workflows, what should be automated.

Workflow Crystallizer

Mines memory logs (memory/YYYY-MM-DD.md) for recurring patterns — repeated requests, multi-step workflows, time-correlated tasks — and generates actionable suggestions: cron jobs, skill drafts, workflow shortcuts, or monitoring proposals.

Unlike a one-shot "analyze my logs" prompt, this skill persists state across runs. It remembers what it suggested, what was accepted/rejected, and only surfaces new insights backed by real evidence.

All scripts live in scripts/ relative to this SKILL.md.

Quick Run (Full Pipeline)

cd /Users/openclaw/.openclaw/workspace/skills/workflow-crystallizer
python3 scripts/analyze_patterns.py --full | python3 scripts/generate_suggestions.py | python3 scripts/report.py --state-file state.json

This analyzes all memory files, generates suggestions, and prints a markdown report.

Incremental Run (Cached — Recommended)

cd /Users/openclaw/.openclaw/workspace/skills/workflow-crystallizer
python3 scripts/analyze_patterns.py | python3 scripts/generate_suggestions.py | python3 scripts/report.py --state-file state.json

Only processes new/modified memory files. Uses cached events from prior runs.

Managing Suggestions

Check state: python3 scripts/state.py — shows cached dates, events, suggestions.

To accept/reject/snooze: edit state.json directly. Set status to accepted, rejected (add rejection_reason), or snoozed. Or use state.py imports.

Reset everything: python3 scripts/state.py --reset

Individual Scripts

Each script accepts --help. Key options:

analyze_patterns.py: --memory-dir PATH, --state-file PATH, --full, --min-confidence 0.4
generate_suggestions.py: --state-file PATH, --cron-path PATH, --clusters FILE
report.py: --state-file PATH, --output PATH, --clusters FILE, --suggestions FILE
state.py: (no args = inspect state), --reset (clear everything)

What It Detects

Pattern Type	Evidence Needed	Suggestion Output
Recurring request	3+ occurrences, 2+ days	Workflow shortcut or monitor
Multi-step workflow	2+ occurrences with step similarity	Skill draft
Time-correlated	2+ at similar times/days	Cron job definition
Already formalized	Contains "cron", "automated", etc.	Skipped (no suggestion)
Project (not pattern)	Same entity, different actions	Skipped

See references/pattern-types.md for detailed detection logic and scoring weights.

How It Avoids Being Annoying

Max 3 suggestions per run — quality over quantity
Confidence threshold (60%) — no low-quality guesses
Deduplication — checks existing crons and skills before suggesting
Never repeats — tracks what was already suggested
Snooze — deferred suggestions resurface after 30 days
Rejected = dormant — only resurfaces at 80%+ confidence with new evidence

Scheduling

Run weekly via cron or heartbeat. Cost: ~5-10K tokens incremental, ~30K first run.

Name: Weekly Workflow Crystallizer
Schedule: 0 20 * * 0 (Sunday 8 PM ET)
Message: Run the workflow-crystallizer skill and present any new suggestions.

Troubleshooting

No patterns: Need 3+ days of substantive memory files
All filtered: Existing crons/skills already cover detected patterns
State corrupt: python3 scripts/state.py --reset
Too noisy: Increase min_confidence in state.json config