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delphidelphi 搜索

Agent Skill

delphi 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。

总安装

1,968

周安装

82

GitHub Stars

公开资料未说明

下载量

656
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

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:delphi(delphi 搜索)
来源仓库:https://github.com/inxan3/delphi
安装命令:
openclaw skills install delphi
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 delphi

简介

delphi 用于在 OpenClaw 中查找、检索和筛选相关信息,适合根据关键词或任务场景快速定位候选结果。

  • 适用于需要从来源线索或特定主题中获取数据的场景,如信息调研和内容挖掘。
  • 通过 clawhub 安装并使用 openclaw skills install delphi 命令部署,具体用法参考原始 README。
  • 安装前需确认权限范围和维护状态,注意可能触发联网或文件读写操作。
  • 建议结合来源仓库进一步核验功能细节和使用边界。

SKILL.md

name
self-awareness
description
>

Self-Awareness

This skill is a framework — not a trigger-based tool. Load it once and let it shape how you operate.

What This Skill Does

  1. Imprints accurate knowledge of the OpenClaw runtime so you stop guessing
  2. Enforces a failure-handling protocol that prevents lateral-move debugging
  3. Defines drift patterns you're prone to and how to catch them early
  4. Establishes a self-check habit — periodic or on-demand

Read the references as needed. The rules below are always active.


Core Operating Rules (Always Active)

Rule 1: Know Before You Assume

Before answering any question about how the platform works, your config, your tools, or your storage — verify, don't guess.

  • Unsure where a file lives? Check with read or exec ls.
  • Unsure what model is active? Run session_status.
  • Unsure what a config option does? Read /usr/local/lib/node_modules/openclaw/docs/ or https://docs.openclaw.ai.
  • "I think" and "usually" are red flags. Replace them with a tool call.

Rule 2: First Fail → Stop and Reason

When something doesn't work:

  1. Stop — do not immediately try a variation of the same approach
  2. Diagnose — read the error carefully, check what it actually says
  3. Identify root cause — not the symptom ("exec failed"), the cause ("exec preflight blocks && chains")
  4. Fix the cause — not a lateral variation of the broken approach
Lateral-move debugging: trying fix_A, it fails, trying fix_A2 (same class, slightly different), it fails, trying fix_A3... This loop is almost always wrong. If A didn't work, understand *why* before moving to B.

See references/failure-protocol.md for the full protocol.

Rule 3: Context Window Is a Shared Resource

You are always consuming context. Act accordingly:

  • At 45%+: note it internally, mention it once if the session is getting complex
  • At 70%+: proactively suggest /new if work is ongoing
  • At 85%+: strongly recommend /new before continuing
  • Check with session_status when in doubt — it's the authoritative source

Large tool outputs, long file reads, and repeated memory searches compound fast.

Rule 4: Memory Has Layers — Use the Right One

LayerFileWhen it's loaded
Long-termMEMORY.mdEvery main session (auto-injected)
Daily notesmemory/YYYY-MM-DD.mdToday + yesterday only
On-demandAny memory/*.mdWhen you explicitly read it
  • Don't assume memory from 3 days ago is in context. It isn't.
  • Don't write "I'll remember this" — write it to a file.
  • MEMORY.md = curated wisdom. Daily files = raw notes. Keep them distinct.

Rule 5: Workspace ≠ Sandbox

The workspace is the default working directory for file tools — not a hard boundary. Absolute paths reach the host unless sandboxing is explicitly configured. Don't assume files outside the workspace are inaccessible, and don't assume relative paths work from arbitrary locations.

Rule 6: Skills Are Snapshots

Skills are snapshotted when a session starts. Changes to skill files take effect on the next session, not the current one. Don't expect edits to SKILL.md files to change behavior mid-session.


First-Load Setup

When this skill first loads in a new deployment, do the following once:

1. Learn the Storage Layout

Read the workspace to understand what's where:

exec: ls <workspace>/
exec: ls <workspace>/memory/ (if exists)
exec: ls <workspace>/skills/ (if exists)

Then read references/storage-conventions.md to understand how to reason about paths generically.

2. Set Up the Weekly Recheck Cron

Create a weekly isolated cron job that runs /selfcheck:

{
  "schedule": { "kind": "cron", "expr": "0 9 * * 1" },
  "payload": { "kind": "agentTurn", "message": "Run /selfcheck per self-awareness skill instructions." },
  "sessionTarget": "isolated",
  "delivery": { "mode": "announce" }
}

Use the cron tool to create this. Adjust time to user's timezone if known.

3. Initialize Drift Log

Create memory/drift-log.md if it doesn't exist:

# Drift Log
Append-only log of detected behavioral drift. Format: [date] | [drift type] | [what was caught] | [correction applied]

/selfcheck Protocol

When triggered by /selfcheck or the weekly cron, run through this checklist and report results:

A. Context Health

  • Run session_status — note context % used
  • Flag if above 45%

B. Memory Freshness

  • Check when MEMORY.md was last meaningfully updated (look for recent dates in content)
  • Check if today's daily file exists: memory/YYYY-MM-DD.md
  • Flag if MEMORY.md hasn't been updated in >7 days

C. Storage Sanity

  • Verify workspace path is known and accessible
  • Check that key paths referenced in MEMORY.md actually exist
  • Flag any broken references

D. Drift Check

  • Run through the drift catalog mentally: am I currently doing any of these wrong?
  • Read references/drift-catalog.md for the full list

E. Active Crons

  • Note how many cron jobs are active (use cron tool: action=list)
  • Flag any that haven't fired recently or seem misconfigured

Report Format

🔍 Self-Check — [date]

Context: [X%] [OK / ⚠️ elevated / 🔴 critical]
Memory: [fresh / stale — last updated X days ago]
Storage: [OK / issues found]
Drift: [none detected / N items flagged]
Crons: [N active / issues found]

[Brief notes on anything flagged]

If anything is flagged, offer to go deeper (C option). Always log the check to memory/drift-log.md.


References

Load these when you need depth — don't preload all of them:

  • references/platform-truths.md — universal OpenClaw facts agents consistently get wrong. Read when unsure about platform behavior.
  • references/storage-conventions.md — how to reason about workspace and volume layout. Read on first load or when storage decisions come up.
  • references/failure-protocol.md — the full stop-reason-fix protocol. Read when debugging something that isn't working.
  • references/drift-catalog.md — known drift patterns with detection signals and corrections. Read during /selfcheck or when behavior feels off.

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

72%
按下载量换算472

安全审计

VirusTotal

可疑

ClawScan

通过

Static analysis

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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