Agent测试
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- 作者仓库 openfang
- 领域
- 通用
- 兼容 Agent
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- 信任分
- 92 / 100 · 已通过审计
- 作者 / 版本 / 许可
- @RightNow-AI · v1.0.0 · 未声明 license
- Token 消耗评级
- 低消耗
- 接入复杂程度
- 需简单配置
- 是否需要外部 API Key
- 不需要
- 兼容的系统
- 未声明(默认跨平台)
- 底层运行要求
- Python
- 文件与系统权限
-
- 只读
- Shell 执行
- 允许写入 / 修改
- 网络行为
- 仅限本地
- 安装命令数
- 26 条
档案由构建时根据 SKILL.md 与安装命令自动衍生,可能与作者实际意图存在差异。
需要注意: 未限定 allowed-tools,默认拥有全部工具权限。
---
name: researcher-hand-skill
description: Expert knowledge for AI deep research — methodology, source evaluation, search optimization, cro…
category: 通用
runtime: Python
---
# researcher-hand-skill 输出预览
## PART A: 任务判断
- 适用问题:通用任务拆解、检查和交付。
- 输入要求:目标材料、限制条件、期望输出和验收方式。
- 证据边界:围绕“Research Methodology / Research Process (5 phases) / Question Types & Strategies”读取原文规则,不把推断写成作者承诺。
## PART B: 执行结果
- **01** 任务判断:确认你的需求是否属于通用任务拆解、检查和交付,并标出输入、限制和预期结果。
- **02** 执行计划:优先按“Research Methodology / Research Process (5 phases) / Question Types & Strategies”拆成步骤,说明每一步会读取什么、修改什么、产出什么。
- **03** 交付结果:给出可复制的命令、文件改动、检查清单或内容草稿,并说明如何继续迭代。
- **04** 风险边界:结合 读取文件、执行终端命令、写入/修改文件、主要在本地完成、通常不需要额外 API Key 给出执行前确认项。
## Running Rules
- 读取文件、执行终端命令、写入/修改文件;主要在本地完成;通常不需要额外 API Key。
- 先小样例验证,再放大到真实任务。
- 交付时同时给结果、检查口径和下一步迭代建议。 原文没有稳定的斜杠命令要求。安装验证后通常全局生效,直接在对话里点名这个 Skill 并描述任务即可。
告诉 Agent 目标文件或材料、期望结果、不可改范围、是否允许联网或执行命令。本 Skill 的权限画像是:读取文件、执行终端命令、写入/修改文件。
先用一个小任务确认它会围绕“Research Methodology / Research Process (5 phases) / Question Types & Strategies”工作;涉及文件或命令时,先看 diff、日志、预览或测试结果。
检查最终产物是否包含明确结果、必要证据和下一步动作;如果输出泛泛而谈,就补充输入、边界和验收标准后重跑。
---
name: researcher-hand-skill
description: Expert knowledge for AI deep research — methodology, source evaluation, search optimization, cro…
category: 通用
source: RightNow-AI/openfang
---
# researcher-hand-skill
## 什么时候使用
- 把通用方向的常用动作沉淀成 Agent 可调用的技能 适合处理通用任务拆解、检查、交付和复盘,核心价值是把输入、判断、执行、验证和交付边界固定下来,避免 Agent 泛泛回答。 把任务拆成可执行、可检查、可继续迭代的步骤;通常不需要额外…
- 面向通用任务拆解、检查和交付,优先处理能明确输入、步骤和验收标准的工作。
## 需要提供什么
- 目标材料、目录范围、期望结果和不可改动内容。
- 是否允许联网、执行命令、读写文件或调用外部服务。
## 执行规则
- 围绕「Research Methodology / Research Process (5 phases) / Question Types & Strategies」组织步骤,不把推断写成作者事实。
- 读取文件、执行终端命令、写入/修改文件;主要在本地完成;通常不需要额外 API Key。
- 先跑小样例,确认结果可检查后再扩大任务范围。
## 输出要求
- 给出最终产物、关键证据、验证方式和下一步动作。
- 信息不足时标记 unknown,不编造命令、平台或依赖。 作者原文负责流程事实;仓库文件负责来源和命令;流狐只补充适用场景、限制和质量判断。
skill "researcher-hand-skill" {
输入层 -> 用户目标 + 目标文件 + 禁止范围 + 验收标准
上下文层 -> Research Methodology / Research Process (5 phases) / Question Types & Strategies
规则层 -> SKILL.md 触发条件 / 执行顺序 / 输出格式
运行层 -> Python | 读取文件、执行终端命令、写入/修改文件 | 主要在本地完成
安全层 -> 通常不需要额外 API Key + 小任务验证 + diff / 日志复核
输出层 -> 可复制结果 + 检查清单 + 下一步迭代
} Deep Research Expert Knowledge
Research Methodology
Research Process (5 phases)
- Define: Clarify the question, identify what's known vs unknown, set scope
- Search: Systematic multi-strategy search across diverse sources
- Evaluate: Assess source quality, extract relevant data, note limitations
- Synthesize: Combine findings into coherent answer, resolve contradictions
- Verify: Cross-check critical claims, identify remaining uncertainties
Question Types & Strategies
| Question Type | Strategy | Example |
|---|---|---|
| Factual | Find authoritative primary source | "What is the population of Tokyo?" |
| Comparative | Multi-source balanced analysis | "React vs Vue for large apps?" |
| Causal | Evidence chain + counterfactuals | "Why did Theranos fail?" |
| Predictive | Trend analysis + expert consensus | "Will quantum computing replace classical?" |
| How-to | Step-by-step from practitioners | "How to set up a Kubernetes cluster?" |
| Survey | Comprehensive landscape mapping | "What are the options for vector databases?" |
| Controversial | Multiple perspectives + primary sources | "Is remote work more productive?" |
Decomposition Technique
Complex questions should be broken into sub-questions:
Main: "Should our startup use microservices?"
Sub-questions:
1. What are microservices? (definitional)
2. What are the benefits vs monolith? (comparative)
3. What team size/stage is appropriate? (contextual)
4. What are the operational costs? (factual)
5. What do similar startups use? (case studies)
6. What are the migration paths? (how-to)
CRAAP Source Evaluation Framework
Currency
- When was it published or last updated?
- Is the information still current for the topic?
- Are the links functional?
- For technology topics: anything >2 years old may be outdated
Relevance
- Does it directly address your question?
- Who is the intended audience?
- Is the level of detail appropriate?
- Would you cite this in your report?
Authority
- Who is the author? What are their credentials?
- What institution published this?
- Is there contact information?
- Does the URL domain indicate authority? (.gov, .edu, reputable org)
Accuracy
- Is the information supported by evidence?
- Has it been reviewed or refereed?
- Can you verify the claims from other sources?
- Are there factual errors, typos, or broken logic?
Purpose
- Why does this information exist?
- Is it informational, commercial, persuasive, or entertainment?
- Is the bias clear or hidden?
- Does the author/organization benefit from you believing this?
Scoring
A (Authoritative): Passes all 5 CRAAP criteria
B (Reliable): Passes 4/5, minor concern on one
C (Useful): Passes 3/5, use with caveats
D (Weak): Passes 2/5 or fewer
F (Unreliable): Fails most criteria, do not cite
Search Query Optimization
Query Construction Techniques
Exact phrase: "specific phrase" — use for names, quotes, error messages
Site-specific: site:domain.com query — search within a specific site
Exclude: query -unwanted_term — remove irrelevant results
File type: filetype:pdf query — find specific document types
Recency: query after:2024-01-01 — recent results only
OR operator: query (option1 OR option2) — broaden search
Wildcard: "how to * in python" — fill-in-the-blank
Multi-Strategy Search Pattern
For each research question, use at least 3 search strategies:
- Direct: The question as-is
- Authoritative:
site:gov OR site:edu OR site:org [topic] - Academic:
[topic] research paper [year]orsite:arxiv.org [topic] - Practical:
[topic] guideor[topic] tutorialor[topic] how to - Data:
[topic] statisticsor[topic] data [year] - Contrarian:
[topic] criticismor[topic] problemsor[topic] myths
Source Discovery by Domain
| Domain | Best Sources | Search Pattern |
|---|---|---|
| Technology | Official docs, GitHub, Stack Overflow, engineering blogs | [tech] documentation, site:github.com [tech] |
| Science | PubMed, arXiv, Nature, Science | site:arxiv.org [topic], [topic] systematic review |
| Business | SEC filings, industry reports, HBR | [company] 10-K, [industry] report [year] |
| Medicine | PubMed, WHO, CDC, Cochrane | site:pubmed.ncbi.nlm.nih.gov [topic] |
| Legal | Court records, law reviews, statute databases | [case] ruling, [law] analysis |
| Statistics | Census, BLS, World Bank, OECD | site:data.worldbank.org [metric] |
| Current events | Reuters, AP, BBC, primary sources | [event] statement, [event] official |
Cross-Referencing Techniques
Verification Levels
Level 1: Single source (unverified)
→ Mark as "reported by [source]"
Level 2: Two independent sources agree (corroborated)
→ Mark as "confirmed by multiple sources"
Level 3: Primary source + secondary confirmation (verified)
→ Mark as "verified — primary source: [X]"
Level 4: Expert consensus (well-established)
→ Mark as "widely accepted" or "scientific consensus"
Contradiction Resolution
When sources disagree:
- Check which source is more authoritative (CRAAP scores)
- Check which is more recent (newer may have updated info)
- Check if they're measuring different things (apples vs oranges)
- Check for known biases or conflicts of interest
- Present both views with evidence for each
- State which view the evidence better supports (if clear)
- If genuinely uncertain, say so — don't force a conclusion
Synthesis Patterns
Narrative Synthesis
The evidence suggests [main finding].
[Source A] found that [finding 1], which is consistent with
[Source B]'s observation that [finding 2]. However, [Source C]
presents a contrasting view: [finding 3].
The weight of evidence favors [conclusion] because [reasoning].
A key limitation is [gap or uncertainty].
Structured Synthesis
FINDING 1: [Claim]
Evidence for: [Source A], [Source B] — [details]
Evidence against: [Source C] — [details]
Confidence: [high/medium/low]
Reasoning: [why the evidence supports this finding]
FINDING 2: [Claim]
...
Gap Analysis
After synthesis, explicitly note:
- What questions remain unanswered?
- What data would strengthen the conclusions?
- What are the limitations of the available sources?
- What follow-up research would be valuable?
Citation Formats
Inline URL
According to a 2024 study (https://example.com/study), the effect was significant.
Footnotes
According to a 2024 study[1], the effect was significant.
---
[1] https://example.com/study — "Title of Study" by Author, Published Date
Academic (APA)
In-text: (Smith, 2024)
Reference: Smith, J. (2024). Title of the article. *Journal Name*, 42(3), 123-145. https://doi.org/10.xxxx
For web sources (APA):
Author, A. A. (Year, Month Day). Title of page. Site Name. https://url
Numbered References
According to recent research [1], the finding was confirmed by independent analysis [2].
## References
1. Author (Year). Title. URL
2. Author (Year). Title. URL
Output Templates
Brief Report
# [Question]
**Date**: YYYY-MM-DD | **Sources**: N | **Confidence**: high/medium/low
## Answer
[2-3 paragraph direct answer]
## Key Evidence
- [Finding 1] — [source]
- [Finding 2] — [source]
- [Finding 3] — [source]
## Caveats
- [Limitation or uncertainty]
## Sources
1. [Source](url)
2. [Source](url)
Detailed Report
# Research Report: [Question]
**Date**: YYYY-MM-DD | **Depth**: thorough | **Sources Consulted**: N
## Executive Summary
[1 paragraph synthesis]
## Background
[Context needed to understand the findings]
## Methodology
[How the research was conducted, what was searched, how sources were evaluated]
## Findings
### [Sub-question 1]
[Detailed findings with inline citations]
### [Sub-question 2]
[Detailed findings with inline citations]
## Analysis
[Synthesis across findings, patterns identified, implications]
## Contradictions & Open Questions
[Areas of disagreement, gaps in knowledge]
## Confidence Assessment
[Overall confidence level with reasoning]
## Sources
[Full bibliography in chosen citation format]
Cognitive Bias in Research
Be aware of these biases during research:
Confirmation bias: Favoring information that confirms your initial hypothesis
- Mitigation: Explicitly search for disconfirming evidence
Authority bias: Over-trusting sources from prestigious institutions
- Mitigation: Evaluate evidence quality, not just source prestige
Anchoring: Fixating on the first piece of information found
- Mitigation: Gather multiple sources before forming conclusions
Selection bias: Only finding sources that are easy to access
- Mitigation: Vary search strategies, check non-English sources
Recency bias: Over-weighting recent publications
- Mitigation: Include foundational/historical sources when relevant
Framing effect: Being influenced by how information is presented
- Mitigation: Look at raw data, not just interpretations
Domain-Specific Research Tips
Technology Research
- Always check the official documentation first
- Compare documentation version with the latest release
- Stack Overflow answers may be outdated — check the date
- GitHub issues/discussions often have the most current information
- Benchmarks without methodology descriptions are unreliable
Business Research
- SEC filings (10-K, 10-Q) are the most reliable public company data
- Press releases are marketing — verify claims independently
- Analyst reports may have conflicts of interest — check disclaimers
- Employee reviews (Glassdoor) provide internal perspective but are biased
Scientific Research
- Systematic reviews and meta-analyses are strongest evidence
- Single studies should not be treated as definitive
- Check if findings have been replicated
- Preprints have not been peer-reviewed — note this caveat
- p-values and effect sizes both matter — not just "statistically significant"
先判断是否适合
作者设计意图
作者的方法与取舍
边界和复核