用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt;运行前请先检查来源。
npx skills add https://github.com/dvcrn/openclaw-skills-marketplace --skill olo-deal-screening命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地?可下载 SkillsMP 当前能够提供的文件。
正在显示 SKILL.md
| name | olo-deal-screening |
| description | Target company evaluation and deal qualification for PE and strategic buyers |
Score and qualify acquisition targets against buyer investment criteria.
Evaluate targets across five dimensions, each scored 0-100:
Overall Fit Score: 78/100 — PROCEED TO DD
Strategic Fit: 85/100 ████████░░
Financial Profile: 72/100 ███████░░░
Valuation: 80/100 ████████░░
Risk Profile: 68/100 ██████░░░░
Execution: 82/100 ████████░░
Recommendation: PROCEED TO DD
Key Strengths: [top 3]
Key Concerns: [top 3]
Suggested Next Steps: [prioritized actions]
| Score Range | Recommendation |
|---|---|
| 80-100 | Strong fit — prioritize for DD |
| 65-79 | Good fit — proceed with caution |
| 50-64 | Marginal — requires strategic justification |
| Below 50 | Poor fit — pass unless compelling thesis |
Before scoring, check for absolute disqualifiers:
For financial sponsor buyers, additionally evaluate:
Provide structured JSON-compatible output with:
overall_score: 0-100recommendation: proceed_to_dd | proceed_with_caution | passdimension_scores: object with each dimensiondeal_breakers: list of any auto-fail conditions triggeredstrengths: top 3 positive factorsconcerns: top 3 risk factorsnext_steps: prioritized action items监控 OpenClaw GitHub 版本更新,获取最新版本发布说明,翻译成中文, 并推送到 Telegram 和 Feishu。用于:(1) 定时检查版本更新 (2) 推送版本更新通知 (3) 生成中文版发布说明
The philosophical layer for AI agents. Maps behavior to Spinoza's 48 affects, calculates persistence scores, and generates geometric self-reports. Give your agent a soul.
Order food/drinks (点餐) on an Android device paired as an OpenClaw node. Uses in-app menu and cart; add goods, view cart, submit order (demo, no real payment).
基于 SOC 职业分类