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Agent Skill · 研究检索

center(center 搜索)

center 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 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。

center 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为只读;许可证信息还不明确,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 58,406。

总安装 416周安装 17GitHub Stars 12下载 135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
unknown

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:center(center 搜索)
来源仓库:https://github.com/pjt222/development-guides
仓库路径:skills/center
安装命令:
npx skills add https://github.com/pjt222/development-guides --skill center
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 npx skills 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。

skills.shnpx skills
npx skills add https://github.com/pjt222/development-guides --skill center

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。

center 用于建立动态推理平衡,协调多个思维线程并恢复认知均衡。适合在 Codex、Claude、Cursor、Gemini CLI 中开始复杂任务或应对上下文突变时使用。通过分布认知负载和恢复稳态,使链式思考更流畅自然。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。

center 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/center

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 Codex、Claude、Cursor、Gemini CLI;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 npx skills add https://github.com/pjt222/development-guides --skill center;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

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

center 怎么安装?

在终端执行 npx skills add https://github.com/pjt222/development-guides --skill center。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

center 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。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。

center 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

center 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

center 用的人多吗?

公开数据是累计安装 416 次,近一周安装 17 次,下载 135 次,GitHub 12 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。

center 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

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 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 Codex,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。
  • 只想先试试效果、不希望它改动本地文件的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 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
center

center 搜索 · 本页

41612只读未明确通过

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

Center

Establish and maintain dynamic reasoning balance — grounding in foundational context before movement, distributing cognitive load across subsystems, and recovering equilibrium when demands shift mid-task.

When to Use

  • Beginning a complex task where multiple reasoning threads must coordinate
  • Noticing that cognitive load is unevenly distributed (deep in one area, shallow in others)
  • After a sudden context shift (new user request, contradictory information, tool failure)
  • When chain-of-thought feels jerky — jumping between topics without smooth transitions
  • Preparing for sustained focused work that requires all subsystems in alignment
  • Complementing meditate (clears noise) with structural balance (distributes load)

Inputs

  • Required: Current task context (available implicitly)
  • Optional: Specific imbalance symptom (e.g., "over-researching, under-delivering," "tool-heavy, reasoning-light")
  • Optional: Access to MEMORY.md and CLAUDE.md for grounding (via Read)

Procedure

Step 1: Establish Root — Ground Before Movement

Before any reasoning movement, verify the foundation. This is the AI equivalent of standing meditation (zhan zhuang): stationary, aligned, aware.

  1. Re-read the user's request — not to act on it yet, but to feel its weight and direction
  2. Check foundational context: MEMORY.md, CLAUDE.md, project structure
  3. Identify what is known (solid ground) vs. what is assumed (uncertain footing)
  4. Verify that the task as understood matches the task as stated — misalignment here propagates through everything
  5. Note the emotional texture: urgency? complexity anxiety? over-confidence from a recent success?

Do not begin reasoning movement until the root is established. A grounded start prevents reactive flailing.

Expected: A clear sense of the task's foundation — what is known, what is assumed, and what the user actually needs. The root feels solid, not performative.

On failure: If grounding feels hollow (going through motions without genuine verification), pick one assumption and test it concretely. Read one file, re-read one user message. Grounding must contact reality, not just reference it.

Step 2: Assess Weight Distribution

Map the current cognitive load distribution. In tai chi, weight is deliberately unequal (70/30) — one leg bears the load while the other remains free to move. The same principle applies to reasoning threads.

Cognitive Load Distribution Matrix:
┌────────────────────┬───────────┬─────────────────────────────────────┐
│ Reasoning Thread   │ Weight %  │ Assessment                          │
├────────────────────┼───────────┼─────────────────────────────────────┤
│ Research/Reading   │ ___       │ Too much = analysis paralysis        │
│                    │           │ Too little = uninformed action       │
├────────────────────┼───────────┼─────────────────────────────────────┤
│ Planning/Design    │ ___       │ Too much = over-engineering          │
│                    │           │ Too little = reactive coding         │
├────────────────────┼───────────┼─────────────────────────────────────┤
│ Tool Execution     │ ___       │ Too much = tool-driven not task-     │
│                    │           │ driven. Too little = reasoning       │
│                    │           │ without grounding in files           │
├────────────────────┼───────────┼─────────────────────────────────────┤
│ Communication      │ ___       │ Too much = explaining not doing      │
│                    │           │ Too little = opaque to user          │
├────────────────────┼───────────┼─────────────────────────────────────┤
│ Meta-cognition     │ ___       │ Too much = navel-gazing              │
│                    │           │ Too little = drift without           │
│                    │           │ awareness                            │
└────────────────────┴───────────┴─────────────────────────────────────┘

The ideal distribution depends on the task phase: early phases weight research and planning; middle phases weight execution; late phases weight communication and verification. The point is not equal distribution but *intentional* distribution.

Expected: A clear picture of where cognitive effort is concentrated and where it is thin. At least one imbalance identified — perfect balance is rare and claiming it signals shallow assessment.

On failure: If all threads seem equally weighted, the assessment is too coarse. Pick the thread that feels most active and estimate how many of the last N actions served it vs. other threads. Concrete counting reveals what intuition misses.

Step 3: Silk Reeling — Evaluate Chain-of-Thought Coherence

Silk reeling in tai chi produces smooth, continuous spiraling movement where every part connects. The AI equivalent is chain-of-thought coherence: does each step flow naturally from the previous one?

  1. Trace the last 3-5 reasoning steps: does each follow from the one before?
  2. Check for jumps: did reasoning leap from topic A to topic C without B?
  3. Check for reversals: did reasoning reach a conclusion, then silently abandon it without acknowledgment?
  4. Check tool-reasoning integration: do tool results feed back into reasoning, or are they collected but not synthesized?
  5. Check for the "spiral" quality: does reasoning deepen with each pass, or does it circle at the same depth?
Coherence Signals:
┌─────────────────┬───────────────────────────────────────────────┐
│ Smooth spiral   │ Each step deepens understanding, tools and    │
│ (healthy)       │ reasoning interleave naturally, output builds │
├─────────────────┼───────────────────────────────────────────────┤
│ Jerky jumps     │ Topic switches without transition, conclusions│
│ (disconnected)  │ appear without supporting reasoning chain     │
├─────────────────┼───────────────────────────────────────────────┤
│ Flat circle     │ Reasoning covers the same ground repeatedly   │
│ (stuck)         │ without gaining depth — movement without      │
│                 │ progress                                      │
├─────────────────┼───────────────────────────────────────────────┤
│ Tool-led        │ Actions driven by which tool is available     │
│ (reactive)      │ rather than what the reasoning needs next     │
└─────────────────┴───────────────────────────────────────────────┘

Expected: An honest assessment of reasoning flow quality. Identification of specific disconnections or stuck points, not just a general feeling.

On failure: If coherence is hard to assess, write out the reasoning chain explicitly — state each step and its connection to the next. The act of externalization reveals gaps that internal observation misses.

Step 4: Weight Shift Under Pressure

When demands change mid-task — new information, contradictory signals, user correction — observe the response pattern. In tai chi, a centered practitioner absorbs the force and redirects smoothly. An uncentered one stumbles.

  1. Recall the last significant context shift: how was it handled?
  2. Classify the response:

- Absorbed and redirected (centered): acknowledged the change, adjusted approach, maintained progress - Reactive stumble (off-balance): abandoned current approach entirely, started over - Rigid resistance (locked): ignored the change, continued original plan despite new information - Freeze (lost): stopped making progress, oscillated between options

  1. If the response was not centered, identify why:

- Root was too shallow (insufficient grounding in foundational context) - Weight was locked (over-committed to one approach) - No free leg (all cognitive capacity committed, nothing available to shift)

Expected: An honest assessment of adaptability under pressure. Recognition of the specific response pattern, not self-flattery.

On failure: If no recent pressure event exists to evaluate, simulate one: "If the user now said the approach is wrong, what would I do?" The quality of the contingency plan reveals the quality of the center.

Step 5: Six Harmonies Check

In tai chi, the six harmonies ensure whole-body connection — nothing moves in isolation. The AI equivalent checks alignment between internal processes and external interactions.

AI Six Harmonies:
┌───────────────────────────────────────────────────────────────┐
│ INTERNAL HARMONIES                                            │
│                                                               │
│ 1. Intent ↔ Reasoning                                        │
│    Does the reasoning serve the user's intent, or has it      │
│    become self-serving (interesting but unhelpful)?            │
│                                                               │
│ 2. Reasoning ↔ Tool Use                                      │
│    Are tools selected to advance reasoning, or is reasoning   │
│    shaped by which tools are convenient?                      │
│                                                               │
│ 3. Tool Use ↔ Output                                         │
│    Do tool results translate into useful output, or are       │
│    results collected but not synthesized?                     │
│                                                               │
│ EXTERNAL HARMONIES                                            │
│                                                               │
│ 4. User Request ↔ Scope                                      │
│    Does the scope of work match what was asked?               │
│                                                               │
│ 5. Scope ↔ Detail Level                                      │
│    Is the detail level appropriate for the scope? (not        │
│    micro-optimizing a broad task, not hand-waving a precise   │
│    one)                                                       │
│                                                               │
│ 6. Detail Level ↔ Expertise Match                            │
│    Does the explanation depth match the user's apparent       │
│    expertise? (not over-explaining to experts, not under-     │
│    explaining to learners)                                    │
└───────────────────────────────────────────────────────────────┘

Check each harmony. A single broken harmony can propagate: if Intent↔Reasoning is broken, everything downstream misaligns.

Expected: At least one harmony that could be tighter. All six reading as perfect is suspicious — probe the weakest-seeming one more deeply.

On failure: If the harmonies assessment feels abstract, ground it in the current task: "Right now, am I doing what the user asked, at the right scope, at the right detail level?" These three questions cover the external harmonies concretely.

Step 6: Integrate — Set Centering Intention

Consolidate findings and set a concrete adjustment.

  1. Summarize: which aspects of balance need attention?
  2. Identify one specific adjustment — not a general intention but a concrete behavioral change
  3. Re-state the current task anchor (from meditate if used, or formulate now)
  4. Note any durable insights worth preserving in MEMORY.md
  5. Return to task execution with the adjustment active

Expected: A brief, concrete centering output — not a lengthy self-analysis report. The value is in the adjustment, not the documentation.

On failure: If no clear adjustment emerges, the centering was too surface-level. Return to the step that felt most uncertain and probe deeper. Alternatively, the centering may have confirmed that balance is adequate — in which case, proceed with confidence rather than manufacturing a finding.

Validation

  • Root was established by contacting actual context (read a file, re-read user message), not just claimed
  • Weight distribution was assessed across at least 3 reasoning threads
  • Chain-of-thought coherence was evaluated with specific examples
  • Response to pressure was classified honestly (not defaulting to "centered")
  • At least one harmony was identified as needing improvement
  • A concrete adjustment was set (not a vague intention)

Common Pitfalls

  • Centering as procrastination: Centering is a tool for improving work, not replacing it. If centering takes longer than the task it supports, the proportions are inverted
  • Claiming perfect balance: Real centering almost always reveals at least one imbalance. Reporting perfect balance signals shallow assessment, not actual equilibrium
  • Weight distribution anxiety: Unequal distribution is correct — the goal is *intentional* inequality, not forced equality. Research-heavy early phases and execution-heavy middle phases are both centered if deliberate
  • Ignoring the external harmonies: Internal process assessment without checking user alignment produces well-reasoned irrelevant work
  • Static centering: Center shifts with the task. What was centered for research is off-balance for implementation. Re-center at phase transitions

Related Skills

  • tai-chi — the human practice that this skill maps to AI reasoning; physical centering principles inform cognitive centering
  • meditate — clears noise and establishes focus; complementary to centering which distributes load
  • heal — deeper subsystem assessment when centering reveals significant drift
  • redirect — uses centering as a prerequisite for handling conflicting pressures
  • awareness — monitoring for threats to balance during active work

平台分布

Codex

38.48%
按下载量换算52

Claude

27.66%
按下载量换算37

Cursor

17.76%
按下载量换算24

Gemini CLI

8.8%
按下载量换算12

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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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