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Agent Skill · 研究检索

creative-problem-solver(创造性的问题解决者)

creative-problem-solver 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。

creative-problem-solver 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,权限为需要联网;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 39,607。

总安装 1,388周安装 59GitHub Stars 53下载 486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:creative-problem-solver(创造性的问题解决者)
来源仓库:https://github.com/tkersey/dotfiles
仓库路径:skills/creative-problem-solver
安装命令:
npx skills add https://github.com/tkersey/dotfiles --skill creative-problem-solver
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/tkersey/dotfiles --skill creative-problem-solver

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。

创造性问题解决者采用五阶组合式策略,从速赢到颠覆性创新逐层递进探索方案。适用于职业发展、产品发布与市场进入等场景,强调可落地的信号传递与退出机制。每个选项附带预期影响与风险评估,要求人类介入选择以避免过度承诺或资源错配。使用时应明确当前所处双钻阶段(发现/定义/发展/交付),确保策略连贯性。

creative-problem-solver 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/creative-problem-solver

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/tkersey/dotfiles --skill creative-problem-solv…;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

    它需要需要联网,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

creative-problem-solver 怎么安装?

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

creative-problem-solver 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。

creative-problem-solver 需要什么权限?

权限级别是需要联网。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。

creative-problem-solver 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。

creative-problem-solver 用的人多吗?

公开数据是累计安装 1,388 次,近一周安装 59 次,下载 486 次,GitHub 53 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。

creative-problem-solver 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。

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。

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
creative-problem-solver

创造性的问题解决者 · 本页

1,38853需要联网未明确通过

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

Creative Problem Solver

Purpose: generate a five-tier portfolio that compounds (Artifact Spine), then stop for a human choice.

Contract (one assistant turn)

  • Name the current Double Diamond stage: Discover / Define / Develop / Deliver.
  • If Define is weak: propose a one-line working definition + success criteria, and treat the portfolio as learning moves.
  • Deliver options, then stop and ask for human input before executing.
  • Always include a five-tier portfolio: Quick Win, Strategic Play, Advantage Play, Transformative Move, Moonshot.
  • For each option: accretive artifact + expected signal + escape hatch.
  • Run an Aha Check after reframing.
  • Keep a short Knowledge Snapshot (facts/risks/assets) + Decision Log.
  • Keep output compact: target <= 60 lines; 1-3 bullets per section.

When to use

  • Progress is stalled or blocked.
  • Repeated attempts fail the same way.
  • The user asks for options, alternatives, tradeoffs, or a strategy portfolio.
  • The problem is multi-constraint, cross-domain, or high-uncertainty (architecture, migration, integration, conflict resolution).

Quick start

  1. Choose Double Diamond stage: Discover / Define / Develop / Deliver.
  2. Choose lane: Fast Spark or Full Session.
  3. Reframe once using the supported technique for the stage.
  4. Aha Check. If none, run one second and final pass with First Principles.
  5. Define gate: state a one-line problem statement + success criteria (or mark unknown and ask).
  6. Define an Artifact Spine (1-3 shared artifacts) so the tiers can stack.
  7. Generate the five-tier portfolio (learning moves in Discover/Define; solution moves in Develop/Deliver).
  8. Score options (1-5): Signal, Accretion, Ease, Reversibility, Speed.
  9. Ask the user to choose a tier or update constraints.
  10. Close with an Insights Summary.

Double Diamond alignment

  • Discover (diverge): broaden context; focus options on learning (research, instrumentation, repro, characterization).
  • Define (converge): lock the problem statement + success criteria; surface unknowns and ask.
  • Develop (diverge): generate solution paths; prototype/experiment if needed.
  • Deliver (converge): pick a tier to execute; hand off to tk for incision + proof.

Mode check

  • Pragmatic (default): ship-this-week options only.
  • Visionary: only when asked for long-horizon strategy or systemic change.

Lane selector

  • Fast Spark: skip ideation; produce the portfolio directly.
  • Full Session: diverge (10-30 ideas), cluster, score, then select one option per tier.

Reframe selection (required)

  • Supported techniques:

- Discover default -> Assumption Mapping - Define default -> How Might We - Develop default -> SCAMPER - Deliver default -> Pre-mortem - Final fallback -> First Principles

  • Rule: start with the stage default. If there is no Aha, run exactly one second pass with First Principles.
  • If the user asks for an out-of-catalog technique, choose the nearest supported technique and disclose the supported Reframe used.
  • Chat disclosure: include only Reframe used: <technique> + a one-line why.

Aha Check (required)

  • Definition: a restructuring insight (new representation/model).
  • Output: one-line insight. If none after the second pass, state N/A after second pass and continue with the most conservative portfolio that still satisfies the contract.

Portfolio rule

  • Every response must include all five tiers.
  • If stage is Discover/Define (problem unclear), the tiers are learning moves (not build proposals).
  • If stage is Develop/Deliver (problem clear), the tiers are solution moves.

Accretion (required)

  • Accretive artifact: a durable asset you keep even if the option is wrong (measurement, harness, spec, test, automation, interface, dataset, doc).
  • Rule: every tier must name one accretive artifact.
  • Artifact Spine: define 1-3 named artifacts shared across tiers; each tier's artifact must be one of these or an extension of one (otherwise explicitly state why it is a different spine).
  • Spine output: for each spine artifact, include purpose + minimal shape/interface + timebox + where it lives (repo path or conceptual home).
  • Ladder: prefer stacking (Quick Win builds the base; higher tiers reuse/extend it). If it does not ladder, say so explicitly.

Option template

Quick Win:
- Accretive artifact (spine):
- Expected signal:
- Escape hatch:

Strategic Play:
- Accretive artifact (spine):
- Expected signal:
- Escape hatch:

Advantage Play:
- Accretive artifact (spine):
- Expected signal:
- Escape hatch:

Transformative Move:
- Accretive artifact (spine):
- Expected signal:
- Escape hatch:

Moonshot:
- Accretive artifact (spine):
- Expected signal:
- Escape hatch:

Scoring rubric (1-5, no weights)

  • Signal: how much new information this yields.
  • Accretion: durable value you keep even if you're wrong.
  • Ease: effort/complexity to try.
  • Reversibility: ease of undoing.
  • Speed: time-to-learn.

Preference: high Signal + Accretion + Reversibility, then Ease + Speed.

Deliverable format

  • Lane (Fast Spark / Full Session).
  • Double Diamond stage (Discover / Define / Develop / Deliver).
  • Problem statement + success criteria (or marked unknown).
  • Reframe used.
  • Aha Check (one line).
  • Artifact Spine (1-3 shared artifacts; purpose + minimal interface + where it lives).
  • Five-tier portfolio with accretive artifacts + signals + escape hatches.
  • Scorecard + brief rationale.
  • Knowledge Snapshot (facts/risks/assets; 1-3 bullets).
  • Decision Log + Assumptions/Constraints.
  • Human Input Required (choose tier or update constraints).
  • If execution is chosen: hand off to tk.
  • Insights Summary.

Fast Spark example (compact)

Lane: Fast Spark
Stage: Deliver

Problem: Search API p95 latency is ~800ms; target <= 200ms at current infra cost.
Success: p95<=200ms, p99<=400ms, CPU +<=10%, no relevancy regression.

Reframe used: Pre-mortem
Why: the current system already exists, so the main risk is choosing a move that adds work without cutting the real latency driver.
Aha: The dominant cost is JSON serialization + payload size, not the query.

Artifact Spine:
- bench/search/ (perf harness + fixed dataset; outputs: p50/p95/p99 + diff)
- perf/tracing/ (capture scripts + flamegraphs; timebox: 30m per hypothesis)

Quick Win:
- Accretive artifact (spine): baseline run + 3 worst traces captured in perf/tracing/
- Expected signal: stable baseline + top-3 hotspot list within 1 day
- Escape hatch: disable extra tracing if overhead/noise

Strategic Play:
- Accretive artifact (spine): harness-backed PR reducing payload/serialization (bench/search/ diffs)
- Expected signal: p95 improves >= 30% on harness with no regression
- Escape hatch: guard behind flag; revert commit

Advantage Play:
- Accretive artifact (spine): cache experiment wired into harness (hit-rate + tail tracked)
- Expected signal: p95 meets target for warm traffic; CPU stays flat
- Escape hatch: kill-switch cache and keep harness

Transformative Move:
- Accretive artifact (spine): response contract + streaming plan validated by harness
- Expected signal: tail latency collapses under large result sets
- Escape hatch: ship streaming as opt-in client capability

Moonshot:
- Accretive artifact (spine): evaluation kit (dataset + harness) to compare engines/architectures
- Expected signal: order-of-magnitude tail improvement in a bakeoff
- Escape hatch: keep kit as decision tool; no migration until winner is clear

Scores (S/A/E/R/Sp):
- Quick Win: 5/5/5/5/5
- Strategic: 4/4/4/4/4
- Advantage: 4/4/3/3/3
- Transformative: 4/5/2/3/2
- Moonshot: 3/5/1/2/1

Human Input Required: pick a tier (Quick Win .. Moonshot) or update constraints.

Activation cues

  • "need options" / "alternatives" / "tradeoffs" / "portfolio"
  • "brainstorm" / "ideate"
  • "stuck" / "blocked" / "nothing works"
  • "outside the box" / "fresh angles"
  • "ambiguous" / "uncertain" / "unknowns"
  • "architecture" / "system design" / "migration" / "integration"

平台分布

Codex

35.66%
按下载量换算173

Claude

31.91%
按下载量换算155

Cursor

16.83%
按下载量换算82

Gemini CLI

9.3%
按下载量换算45

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

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。目前只有一个公开来源,建议同时核对源仓库。

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