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Agent Skill · 研究检索

demo-slap(演示掌掴)

demo-slap 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

demo-slap 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为执行命令;许可证为 MIT-0,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 9,984。

总安装 13,272周安装 553GitHub Stars 1下载 4,424核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
0.1.4

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:demo-slap(演示掌掴)
来源仓库:https://github.com/damirikys/demo-slap
安装命令:
openclaw skills install demo-slap
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install demo-slap

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

demo-slap 使用 Demo-Slap API 从 CS2 演示文件中提取精彩片段和电影式集锦。支持 Leetify 集成和比赛历史回退选择机制,提升内容质量。通过 clawhub 安装并使用 openclaw skills install demo-slap 命令部署。

需上传 .dem 文件并提供可选的比赛 ID 以增强识别精度。建议验证输出片段是否符合赛事规则和版权要求。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install demo-slap;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要执行命令,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

demo-slap 怎么安装?

在终端执行 openclaw skills install demo-slap。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

demo-slap 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

demo-slap 需要什么权限?

权限级别是执行命令。安装流程涉及命令执行,可能通过 openclaw skills install demo-slap 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

demo-slap 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

demo-slap 用的人多吗?

公开数据是累计安装 13,272 次,近一周安装 553 次,下载 4,424 次,GitHub 1 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

demo-slap 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 需要执行命令,装到常用环境前要先评估影响面。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 不能接受 Skill 在你机器上执行命令的人。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
demo-slap

演示掌掴 · 本页

13,2721执行命令MIT-0通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
demo-slap
description
Generate CS2 highlights and fragmovies from demos using the Demo-Slap API, with optional Leetify integration and Demo-Slap match history fallback to select recent matches. Use when a user asks to record a highlight, render a clip, make a fragmovie, clip a round, or turn a CS2 demo into MP4 video.
metadata

Demo-Slap Highlight Skill

Generate MP4 highlights and fragmovies from CS2 demos.

This skill is designed for OpenClaw environments where background jobs, local helper scripts, and chat-aware delivery are available. It uses Python 3 scripts and the requests package for HTTP API access.

Expected runtime inputs:

  • Required: DEMOSLAP_API_KEY
  • Optional: LEETIFY_API_KEY
  • Optional deployment helper: DEMO_SLAP_WATCHDOG_JOB_ID

Scripts

Run bundled scripts relative to the skill root, usually from scripts/.

Demo-Slap

ScriptPurpose
demo_slap_matches.pyList recent matches from Demo-Slap /public-api/matches
demo_slap_resolve.pyTry to resolve replay/demo URL from Demo-Slap match history by index; fail clearly if the API exposes only jobId
demo_slap_match_pick.pyPick a Demo-Slap match by index and return structured match info including jobId
demo_slap_analyze.pySubmit a demo for analysis, poll until done, output highlights JSON
demo_slap_render.pyRender one or more highlights, poll until done, output clip URL
demo_slap_common.pyShared utilities (config, API calls, state)

Leetify

ScriptPurpose
leetify/leetify_matches.pyList recent matches
leetify/leetify_resolve.pyResolve replay URL by username + match index
leetify/leetify_save_id.pySave username -> Steam64 ID mapping
leetify/leetify_common.pyShared Leetify utilities

Match source selection

  • Prefer Leetify for recent match discovery when LEETIFY_API_KEY is available.
  • If LEETIFY_API_KEY is not configured but DEMOSLAP_API_KEY is available, use Demo-Slap match history from /public-api/matches.
  • Swagger: https://api-doc.demo-slap.net/
  • Treat Demo-Slap match history as the fallback discovery path for listing matches and selecting an existing analyzed match or replay context before analyze/render.

Runtime files

Use these files as optional local runtime state during execution:

  • data/state.json
  • data/highlights.json
  • data/history.log
  • data/steam_ids.json
  • data/config.json

These files are runtime helpers for local operation and are not required to understand or inspect the skill package itself.

state.json tracks the current operation:

{
  "status": "idle|analyzing|rendering|done|error",
  "job_id": "...",
  "render_job_id": "...",
  "chat_id": "telegram:182314856",
  "clip_urls": {"highlight_id": "https://..."},
  "progress": "polling 3/30",
  "last_completed_op": "analyze|render",
  "notification": {
    "sent": false,
    "sent_at": null,
    "last_attempt_at": null,
    "error": null
  },
  "updated_at": "ISO timestamp"
}

Workflow

1. Find the match

Preferred path when LEETIFY_API_KEY is available:

python3 scripts/leetify/leetify_matches.py <USERNAME> [--limit 10]

Fallback path when LEETIFY_API_KEY is missing but DEMOSLAP_API_KEY is available:

python3 scripts/demo_slap_matches.py [<USERNAME>] [--limit 10]
  • uses Demo-Slap /public-api/matches
  • use the Demo-Slap swagger docs at https://api-doc.demo-slap.net/ if schema details are needed
  • if <USERNAME> is provided and mapped, filter matches to that player's Steam ID when possible
  • after the user picks a match, run:
python3 scripts/demo_slap_match_pick.py [<USERNAME>] --match-index <N>
  • treat the returned jobId as the primary handle for downstream Demo-Slap operations

2. Resolve replay URL

Preferred path when using Leetify:

python3 scripts/leetify/leetify_resolve.py <USERNAME> --match-index <N>

When using Demo-Slap fallback:

python3 scripts/demo_slap_match_pick.py [<USERNAME>] --match-index <N>
  • use the returned jobId as the selected match identifier
  • if demoUrl is present, you may still use analyze-by-URL
  • if demoUrl is absent, skip URL resolution and continue by API endpoints that accept the existing analyze jobId
  • use GET /public-api/analyze/{jobId}/status and GET /public-api/analyze/{jobId}/data to inspect existing highlights
  • if the user wants clips and highlights already exist, render directly from that jobId

3. Analyze in background

python3 -u scripts/demo_slap_analyze.py --url '<REPLAY_URL>' --username <USERNAME> --chat-id <CHAT_ID>

Run with exec(background: true) and keep the returned process/session id.

Optional deployment-specific watchdog pattern for OpenClaw environments:

  • Use a watchdog only when background analyze/render work benefits from periodic delivery checks.
  • Reuse an existing deployment watchdog when available instead of assuming a new persistent scheduler entry is always needed.
  • If a deployment chooses to create or enable a watchdog through the built-in cron tool, keep it scoped to the active run and disable it again after terminal delivery.
  • A 2 minute interval is a reasonable default.
  • Use scripts/demo_slap_watchdog.sh status|tail|job only as a local helper for inspecting runtime state, logs, or deployment-specific job references.
  • Treat data/state.json and data/highlights.json as the source of truth during runtime.

Agent workflow:

  1. Choose the match source:

- Leetify if LEETIFY_API_KEY exists - Demo-Slap /public-api/matches if LEETIFY_API_KEY is missing and DEMOSLAP_API_KEY exists

  1. If using Leetify, resolve the replay URL and run analyze
  2. If using Demo-Slap fallback, pick a match and inspect the returned jobId
  3. If the selected Demo-Slap match already has analyze data, continue by jobId instead of forcing analyze-by-URL
  4. Use or enable a deployment watchdog only when long-running analyze/render work benefits from it
  5. Launch analyze or render and save the returned process/session id when applicable
  6. Let the watchdog deliver the result when a deployment uses one
  7. Disable the watchdog again after terminal delivery

4. Render in background

# Single highlight
python3 -u scripts/demo_slap_render.py <JOB_ID> <HIGHLIGHT_ID> --chat-id <CHAT_ID>

# Fragmovie
python3 -u scripts/demo_slap_render.py <JOB_ID> <ID1> <ID2> ... --fragmovie --chat-id <CHAT_ID>

Run with exec(background: true) and keep the returned process/session id.

Optional deployment-specific watchdog pattern for OpenClaw environments:

  • Use a watchdog only when background analyze/render work benefits from periodic delivery checks.
  • Reuse an existing deployment watchdog when available instead of assuming a new persistent scheduler entry is always needed.
  • If a deployment chooses to create or enable a watchdog through the built-in cron tool, keep it scoped to the active run and disable it again after terminal delivery.
  • A 2 minute interval is a reasonable default.
  • Use scripts/demo_slap_watchdog.sh status|tail|job only as a local helper for inspecting runtime state, logs, or deployment-specific job references.
  • Treat data/state.json and data/highlights.json as the source of truth during runtime.

Agent workflow:

  1. Enable or reuse a deployment watchdog only when needed for the active run
  2. Launch render and save the returned process/session id
  3. Poll process output for the Estimated finish: line and tell the user the ETA if present
  4. Let the watchdog deliver the result when one is in use
  5. Disable the watchdog again after terminal delivery

Critical: set <CHAT_ID> from inbound metadata of the originating request. Treat hardcoded chat identifiers as local examples only, not as a reusable default.

5. Check status

Read data/state.json.

Setup

Map username to Steam ID

python3 scripts/leetify/leetify_save_id.py <USERNAME> <STEAM_64_ID>

Configure API keys

Prefer environment variables:

  • DEMOSLAP_API_KEY - required
  • LEETIFY_API_KEY - optional, only for Leetify-backed match discovery
  • DEMO_SLAP_WATCHDOG_JOB_ID - optional deployment-specific helper for watchdog inspection scripts

Source selection rules:

  • If LEETIFY_API_KEY exists, use Leetify for match discovery.
  • If LEETIFY_API_KEY is absent but DEMOSLAP_API_KEY exists, use Demo-Slap /public-api/matches for match discovery.
  • DEMOSLAP_API_KEY is always required for analyze/render.

Optional local fallback for controlled self-hosted setups: put them in data/config.json.

Support

For access and support, please join our Discord community: https://discord.gg/8nfh26W9wQ

平台分布

OpenClaw

93.99%
按下载量换算4,158

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 openclaw skills install demo-slap 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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