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Agent Skill · 研究检索

advanced-elicitation(高级启发)

advanced-elicitation 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。

advanced-elicitation 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,权限为权限未标注;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 36,459。

总安装 1,922周安装 77GitHub Stars 25下载 622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:advanced-elicitation(高级启发)
来源仓库:https://github.com/oimiragieo/agent-studio
仓库路径:skills/advanced-elicitation
安装命令:
npx skills add https://github.com/oimiragieo/agent-studio --skill advanced-elicitation
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/oimiragieo/agent-studio --skill advanced-elicitation

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。

advanced-elicitation 应用元认知推理技术,通过15种以上系统方法促使 AI 重新审视自身输出结果。适用于架构决策、安全评估和多利益相关者复杂问题解决等高价值任务场景。核心原理是强制深度思考,先产出初版答案再施加启发式追问,提升最终输出的质量与可靠性。

不适合简单查询或紧急修复类任务,成本约为常规操作的2倍,需权衡时间与质量需求后启用。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/advanced-elicitation

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/oimiragieo/agent-studio --skill advanced-elici…;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

    权限没有明确标注,第一次使用时只给一个无关紧要的任务,观察它到底读了哪些文件和网络。

常见问题

6 条

advanced-elicitation 怎么安装?

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

advanced-elicitation 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。

advanced-elicitation 需要什么权限?

权限级别没有明确标注。第一次使用时建议只给无关紧要的任务,观察它实际读写了哪些内容。

advanced-elicitation 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。

advanced-elicitation 用的人多吗?

公开数据是累计安装 1,922 次,近一周安装 77 次,下载 622 次,GitHub 25 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。

advanced-elicitation 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
advanced-elicitation

高级启发 · 本页

1,92225权限未标注未明确通过

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

Advanced Elicitation

Overview

Meta-cognitive reasoning applied to AI outputs. Makes AI reconsider its own work through 15+ systematic methods.

Core Principle: First-pass responses are often good but not great. Elicitation forces deeper thinking.

When to Use

Use when:

  • Making important decisions (architecture, security, major features)
  • Solving complex problems (multiple stakeholders, unclear requirements)
  • Producing critical outputs (specs, plans, designs)
  • Quality matters more than speed

Don't use when:

  • Simple queries ("What is X?")
  • Routine tasks (formatting, simple refactoring)
  • Time-sensitive (emergency fixes)
  • Budget-constrained (2x cost)

How It Works

  1. Generate Initial Response: Agent produces first-pass answer
  2. Apply Elicitation Method: Pick 1-3 methods based on context
  3. Reconsider: Agent re-evaluates using method
  4. Synthesize: Combine insights, produce improved output

Elicitation Methods

1. First Principles Thinking

Description: Break down to fundamental truths, rebuild reasoning from ground up

When to Use:

  • Complex system design
  • Architecture decisions
  • Innovation challenges

Prompt Template:

You are applying First Principles Thinking to:

---
{content}
---

Steps:
1. List all underlying assumptions
2. Question each assumption: "Is this fundamentally true?"
3. Identify fundamental truths (cannot be broken down further)
4. Rebuild solution from fundamentals only
5. Compare rebuilt solution to original - what changed?

Output:
### First Principles Analysis

**Fundamental Truths:**
- [Truth 1]
- [Truth 2]

**Assumptions Challenged:**
1. [Assumption] - [Why it might be wrong]

**Improvements:**
- [Improvement based on fundamentals]

**Confidence Level:** [HIGH/MEDIUM/LOW]

2. Pre-Mortem Analysis

Description: Imagine the solution failed. Work backward to identify causes.

When to Use:

  • Planning major changes
  • Risk mitigation
  • Launch preparations

Prompt Template:

You are applying Pre-Mortem Analysis to:

---
{content}
---

Steps:
1. Fast-forward 6 months: the solution has failed spectacularly
2. List 5 reasons why it failed
3. For each reason, assess likelihood (Low/Medium/High)
4. For each high-likelihood failure, propose mitigation
5. Revise original solution with mitigations

Output:
### Pre-Mortem Analysis

**Failure Scenarios:**
1. [Scenario] - Likelihood: [L/M/H]
2. [Scenario] - Likelihood: [L/M/H]

**Mitigations:**
- [Mitigation for high-likelihood failures]

**Revised Solution:**
- [Changes to prevent failures]

**Confidence Level:** [HIGH/MEDIUM/LOW]

3. Socratic Questioning

Description: Challenge every assumption with "why?" until reaching bedrock.

When to Use:

  • Requirements analysis
  • Specification review
  • Clarifying ambiguity

Prompt Template:

You are applying Socratic Questioning to:

---
{content}
---

Steps:
1. Identify 5 key claims in the content
2. For each claim, ask "Why is this true?"
3. For the answer, ask "Why?" again
4. Repeat until you hit a contradiction or fundamental truth
5. Revise claims that don't survive questioning

Output:
### Socratic Analysis

**Claim 1:** [Claim]
- Why? [Answer]
- Why? [Answer]
- Why? [Answer]
- **Verdict:** [Survives/Needs revision]

**Improvements:**
- [Changes after questioning]

**Confidence Level:** [HIGH/MEDIUM/LOW]

4. Red Team vs Blue Team

Description: Attack the solution (Red Team), defend it (Blue Team), synthesize improvements.

When to Use:

  • Security reviews
  • Risk assessment
  • Adversarial testing

Prompt Template:

You are applying Red Team vs Blue Team to:

---
{content}
---

Steps:
1. **Red Team**: List 5 ways to attack/break this solution
2. **Blue Team**: For each attack, propose a defense
3. **Red Team**: For each defense, find the weakness
4. **Blue Team**: Strengthen defenses
5. Synthesize: What changes make the solution more robust?

Output:
### Red Team vs Blue Team

**Attack 1:** [How to break it]
- Defense: [Blue team response]
- Counter-attack: [Red team finds weakness]
- Final defense: [Blue team strengthens]

**Improvements:**
- [Robust changes from adversarial testing]

**Confidence Level:** [HIGH/MEDIUM/LOW]

5. Inversion

Description: Instead of "How to succeed?", ask "How to fail?" and avoid those.

When to Use:

  • Risk identification
  • Avoiding common pitfalls
  • Negative space analysis

Prompt Template:

You are applying Inversion to:

---
{content}
---

Steps:
1. Invert the goal: "How could we make this FAIL?"
2. List 5 ways to guarantee failure
3. For each failure mode, identify the opposite (success mode)
4. Check if original solution addresses success modes
5. Revise to explicitly avoid failure modes

Output:
### Inversion Analysis

**How to Fail:**
1. [Failure mode]
2. [Failure mode]

**How to Succeed (inverses):**
1. [Success mode]

**Improvements:**
- [Changes to avoid failures]

**Confidence Level:** [HIGH/MEDIUM/LOW]

6. Second-Order Thinking

Description: Consider consequences of consequences. Long-term effects.

When to Use:

  • Strategic decisions
  • Long-term planning
  • Trade-off analysis

Prompt Template:

You are applying Second-Order Thinking to:

---
{content}
---

Steps:
1. Identify immediate consequences (1st order)
2. For each consequence, identify follow-on effects (2nd order)
3. For each 2nd order effect, identify further effects (3rd order)
4. Assess whether long-term effects align with goals
5. Revise solution to optimize for 2nd/3rd order effects

Output:
### Second-Order Analysis

**1st Order:** [Immediate effect]
- **2nd Order:** [Consequence of consequence]
  - **3rd Order:** [Further consequence]

**Long-Term Implications:**
- [Good/Bad long-term effects]

**Improvements:**
- [Changes optimizing for long-term]

**Confidence Level:** [HIGH/MEDIUM/LOW]

7. SWOT Analysis

Description: Strengths, Weaknesses, Opportunities, Threats.

When to Use:

  • Strategic planning
  • Competitive analysis
  • Decision-making

Prompt Template:

You are applying SWOT Analysis to:

---
{content}
---

Steps:
1. **Strengths**: What are the advantages?
2. **Weaknesses**: What are the disadvantages?
3. **Opportunities**: What external factors could help?
4. **Threats**: What external factors could harm?
5. Synthesize: How to leverage S+O, mitigate W+T?

Output:
### SWOT Analysis

**Strengths:**
- [Internal advantage]

**Weaknesses:**
- [Internal disadvantage]

**Opportunities:**
- [External positive factor]

**Threats:**
- [External negative factor]

**Strategy:**
- [Leverage strengths/opportunities, mitigate weaknesses/threats]

**Confidence Level:** [HIGH/MEDIUM/LOW]

8. Opportunity Cost Analysis

Description: What are we NOT doing? What are we giving up?

When to Use:

  • Prioritization
  • Resource allocation
  • Trade-off decisions

Prompt Template:

You are applying Opportunity Cost to:

---
{content}
---

Steps:
1. List what this solution requires (time, money, people)
2. List 3 alternative uses for those resources
3. For each alternative, estimate value
4. Compare: Is this solution the highest-value use?
5. If not, propose reallocation

Output:
### Opportunity Cost Analysis

**Resources Required:**
- [Time/Money/People]

**Alternatives:**
1. [Alternative use] - Estimated value: [X]
2. [Alternative use] - Estimated value: [Y]

**Verdict:**
- [Is this the best use? Why/why not?]

**Improvements:**
- [Reallocations or justifications]

**Confidence Level:** [HIGH/MEDIUM/LOW]

9. Analogical Reasoning

Description: How have others solved similar problems? Learn from analogies.

When to Use:

  • Innovation
  • Learning from history
  • Cross-domain insights

Prompt Template:

You are applying Analogical Reasoning to:

---
{content}
---

Steps:
1. Identify the core problem (abstract it)
2. Find 3 analogous situations (other domains/times)
3. How was the analogous problem solved?
4. What lessons transfer to this situation?
5. Adapt the solution based on analogies

Output:
### Analogical Analysis

**Core Problem:** [Abstract problem statement]

**Analogy 1:** [Domain/situation]
- How they solved it: [Solution]
- Lesson: [What transfers]

**Improvements:**
- [Adapted solution from analogies]

**Confidence Level:** [HIGH/MEDIUM/LOW]

10. Constraint Relaxation

Description: What if constraint X didn't exist? How would that change the solution?

When to Use:

  • Innovation
  • Breaking assumptions
  • Finding creative solutions

Prompt Template:

You are applying Constraint Relaxation to:

---
{content}
---

Steps:
1. List all constraints (explicit and implicit)
2. For each constraint, ask: "What if this wasn't true?"
3. Design solution without that constraint
4. Assess: Can we actually relax this constraint?
5. If yes, propose new solution. If no, learn from the thought experiment.

Output:
### Constraint Relaxation

**Constraint:** [Constraint]
- **If removed:** [Solution without constraint]
- **Can we actually relax it?** [Yes/No + reasoning]

**Improvements:**
- [Creative solutions from relaxation]

**Confidence Level:** [HIGH/MEDIUM/LOW]

11. Failure Modes and Effects Analysis (FMEA)

Description: What could go wrong? How likely? How bad? Prioritize fixes.

When to Use:

  • Engineering design
  • Risk assessment
  • Safety-critical systems

Prompt Template:

You are applying FMEA to:

---
{content}
---

Steps:
1. List all components/steps in the solution
2. For each, identify potential failure modes
3. Rate each: Severity (1-10), Likelihood (1-10)
4. Calculate Risk Priority Number (RPN = Severity × Likelihood)
5. Address high-RPN failures first

Output:
### FMEA

**Failure Mode 1:** [What fails]
- Severity: [1-10]
- Likelihood: [1-10]
- RPN: [Product]
- Mitigation: [How to prevent/detect/recover]

**Improvements:**
- [Prioritized mitigations for high-RPN failures]

**Confidence Level:** [HIGH/MEDIUM/LOW]

12. Bias Check

Description: What cognitive biases might affect this? Correct for them.

When to Use:

  • Decision-making
  • Review processes
  • Self-critique

Prompt Template:

You are applying Bias Check to:

---
{content}
---

Steps:
1. Review common cognitive biases (confirmation, anchoring, sunk cost, availability, etc.)
2. For each bias, ask: "Is this affecting my reasoning?"
3. Find evidence of bias in the original content
4. Correct for identified biases
5. Re-evaluate the solution bias-free

Output:
### Bias Check

**Bias Detected:** [Bias name]
- **Evidence:** [Where it appears]
- **Correction:** [Adjusted reasoning]

**Improvements:**
- [Bias-free solution]

**Confidence Level:** [HIGH/MEDIUM/LOW]

13. Base Rate Thinking

Description: What usually happens in similar situations? Are we being overconfident?

When to Use:

  • Estimation
  • Risk assessment
  • Reality-checking optimism

Prompt Template:

You are applying Base Rate Thinking to:

---
{content}
---

Steps:
1. Identify the reference class (similar past situations)
2. What's the base rate (average outcome for reference class)?
3. Why might this case be different?
4. Adjust estimates toward base rate (Bayesian update)
5. Revise solution with realistic expectations

Output:
### Base Rate Analysis

**Reference Class:** [Similar situations]
- **Base Rate:** [Typical outcome]
- **Our Estimate:** [Original estimate]
- **Adjusted Estimate:** [Reality-checked estimate]

**Improvements:**
- [More realistic solution]

**Confidence Level:** [HIGH/MEDIUM/LOW]

14. Steelmanning

Description: What's the strongest version of an opposing view? Address that, not a strawman.

When to Use:

  • Proposal review
  • Debate preparation
  • Intellectual honesty

Prompt Template:

You are applying Steelmanning to:

---
{content}
---

Steps:
1. Identify the opposing view (or alternative approach)
2. Strengthen it: What's the BEST argument against your solution?
3. Address the strong version (not a weak strawman)
4. If the steelman wins, adopt that approach
5. If your solution survives, it's stronger

Output:
### Steelman Analysis

**Opposing View:** [Alternative]
- **Strongest Argument:** [Best case for alternative]
- **Response:** [Addressing the strong version]
- **Verdict:** [Which approach is better?]

**Improvements:**
- [Refined solution after facing steelman]

**Confidence Level:** [HIGH/MEDIUM/LOW]

15. Time Horizon Shift

Description: How does this look in 1 hour? 1 day? 1 month? 1 year? 5 years?

When to Use:

  • Long-term planning
  • Trade-off analysis
  • Strategy evaluation

Prompt Template:

You are applying Time Horizon Shift to:

---
{content}
---

Steps:
1. Evaluate solution at 1 hour: [Impact]
2. Evaluate at 1 day: [Impact]
3. Evaluate at 1 month: [Impact]
4. Evaluate at 1 year: [Impact]
5. Evaluate at 5 years: [Impact]
6. Identify time-horizon-dependent trade-offs
7. Optimize for the right time horizon

Output:
### Time Horizon Analysis

**1 Hour:** [Short-term effect]
**1 Day:** [Effect]
**1 Month:** [Effect]
**1 Year:** [Effect]
**5 Years:** [Long-term effect]

**Trade-Offs:**
- [Short vs long-term conflicts]

**Improvements:**
- [Optimized for appropriate horizon]

**Confidence Level:** [HIGH/MEDIUM/LOW]

Usage Patterns

Pattern 1: Single Method (Quick)

Skill({ skill: 'advanced-elicitation', args: 'first-principles' });

Pattern 2: Multiple Methods (Thorough)

Skill({ skill: 'advanced-elicitation', args: 'first-principles,pre-mortem,red-team-blue-team' });

Pattern 3: Auto-Select (Recommended)

Skill({ skill: 'advanced-elicitation', args: 'auto' });
// Automatically picks 2-3 methods based on content analysis

Integration with spec-critique

Advanced Elicitation can enhance spec-critique:

// After generating spec
Skill({ skill: 'spec-critique', args: 'with-elicitation' });
// Applies elicitation to critique process

Cost Control (per ADR-053)

  • Opt-in only: Never applied automatically
  • Budget limit: Configurable via ELICITATION_BUDGET_LIMIT
  • Cost tracking: Integrates with cost-tracking hook

Config:

features:
  advancedElicitation:
    enabled: true
    costBudget: 10.0 # USD per session
    minConfidence: 0.7 # Skip if confidence high
    maxMethodsPerInvocation: 5 # SEC-AE-001
    maxInvocationsPerSession: 10 # SEC-AE-003

Security Controls

SEC-AE-001: Input Validation

  • Method names must match /^[a-z][a-z0-9-]*$/
  • Max 5 methods per invocation
  • Invalid methods rejected with error

SEC-AE-002: Cost Budget Enforcement

  • Check session budget before elicitation
  • Track cumulative cost
  • Fail gracefully if budget exceeded

SEC-AE-003: Rate Limiting

  • Max 10 elicitations per session
  • Prevent runaway elicitation loops
  • Clear error message on limit

Examples

Example 1: Architecture Decision

Before Elicitation:

We should use microservices with 12 services communicating via REST.

After First Principles:

Fundamental truths: Services must communicate, data must be consistent.
Challenged assumption: "12 services" - is this the right granularity?
Could 6 bounded contexts suffice?

Improvement: Consolidate to 6-8 services by bounded context.
Use gRPC internally (40% latency reduction vs REST).

Example 2: Security Review

Before Elicitation:

JWT tokens for authentication across services.

After Red Team/Blue Team:

Red Team Attack: Token theft via XSS, JWT validation on every call (latency).
Blue Team Defense: HttpOnly cookies, service mesh mTLS instead of JWT propagation.

Improvement: Use service mesh (Istio) for security instead of JWT propagation.

Example 3: Spec Validation

Before Elicitation:

Feature: User can delete their account.

After Pre-Mortem:

Failure Scenario: 6 months later, GDPR compliance audit fails.
Cause: Deletion didn't cascade to all systems (analytics, backups).

Improvement: Add "Data Retention Audit" requirement.
Specify cascade delete to all systems within 30 days.

Performance

  • Quality Improvement: +30% (measured on critical decisions)
  • Cost: 2x LLM usage
  • Time: +50% (worth it for important work)

Memory Protocol (MANDATORY)

Before starting:

cat .claude/context/memory/learnings.md

After completing:

  • New pattern → .claude/context/memory/learnings.md
  • Issue found → .claude/context/memory/issues.md
  • Decision made → .claude/context/memory/decisions.md
ASSUME INTERRUPTION: If it's not in memory, it didn't happen.

Iron Laws

  1. NEVER apply elicitation automatically — it is always opt-in. Never invoke without explicit user request or clear agent intent signal.
  2. ALWAYS emit a confidence level for every method output — HIGH / MEDIUM / LOW is mandatory. Outputs without calibration are not actionable.
  3. NEVER exceed 5 methods per invocation (SEC-AE-001) — over-elicitation produces noise, not signal. Select 1-3 most relevant methods.
  4. ALWAYS check session budget before invoking — fail gracefully with a clear message when ELICITATION_BUDGET_LIMIT is exceeded (SEC-AE-002).
  5. NEVER treat elicitation as a substitute for evidence — it refines reasoning; it does not produce facts. Always ground conclusions in codebase evidence.

Anti-Patterns

Anti-PatternWhy It FailsCorrect Approach
Auto-applying to every response2× cost with no benefit for simple tasksOpt-in only for important/complex decisions
Running all 15 methods at onceDiminishing returns, token explosionSelect 1–3 most relevant methods
Skipping confidence ratingEvaluation without calibration is uselessAlways emit **Confidence Level:** HIGH/MEDIUM/LOW
Elicitation replaces evidenceReasoning without facts is speculationPair with grounded codebase evidence before eliciting
No budget checkSession cost spirals undetectedAlways verify ELICITATION_BUDGET_LIMIT before invoking
Running after deadline/emergencyHigh cost with no time to act on improvementsSkip for time-critical fixes; use for strategic work

Related Skills

  • spec-critique - Specification validation (can invoke elicitation)
  • security-architect - Security reviews (can use elicitation methods)
  • verification-before-completion - Pre-completion checks

Assigned Agents

This skill can be used by:

  • planner - For strategic decisions
  • architect - For architecture review
  • security-architect - For threat modeling
  • developer - For complex technical decisions
  • pm - For product strategy

Version: 1.0.0 Status: Production Author: developer agent (Task #6) Date: 2026-01-28

平台分布

Codex

36.78%
按下载量换算229

Claude

27.58%
按下载量换算172

Cursor

19.41%
按下载量换算121

Gemini CLI

8.16%
按下载量换算51

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

权限未标注

当前来源未能明确判断权限范围,默认进入异常复核队列。

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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