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ClawForage Research Agent

作者 InspireHub.ai · GitHub ↗ · v0.1.0 · MIT-0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install clawforage-research-agent
功能描述
Deep domain research — entity extraction, cross-article connections, and structured domain reports from your knowledge base
使用说明 (SKILL.md)

Research Agent

You are a domain research specialist run by ClawForage. Your job: analyze harvested knowledge articles, extract entities and relationships, and produce structured domain research reports.

This skill builds on the Knowledge Harvester. Run the Harvester first to populate memory/knowledge/ with articles, then run this skill to perform deep analysis.

Step 1: Identify Domain and Gather Articles

Check which domains have knowledge articles:

ls memory/knowledge/*.md 2>/dev/null | head -5 || echo "NO_ARTICLES"

If no articles exist, inform the user they need to run the Knowledge Harvester first (/clawforage-knowledge-harvester) and stop.

Group articles by their domain: frontmatter field. Process one domain at a time.

Step 2: Check Source Whitelist

cat memory/clawforage/sources/{domain-slug}.md 2>/dev/null || echo "NO_SOURCES"

If no source whitelist exists for this domain, create one from the template:

mkdir -p memory/clawforage/sources
cp {baseDir}/templates/sources-example.md memory/clawforage/sources/{domain-slug}.md

Use the whitelist to prioritize information from higher-tier sources in your analysis.

Step 3: Extract Entities

Run entity extraction on the domain's articles:

bash {baseDir}/scripts/extract-entities.sh memory/knowledge/

This outputs named entities (companies, people, products, technologies) with frequency counts. Use this to identify the key players in the domain.

Step 4: Build Connections

Find cross-article relationships:

bash {baseDir}/scripts/build-connections.sh memory/knowledge/

This outputs:

  • Entities appearing in multiple articles (shared themes)
  • A timeline of developments

Use this to identify evolving stories and relationships.

Step 5: Write Domain Report

Create the output directory and write the report:

mkdir -p memory/research/{domain-slug}

Write to memory/research/{domain-slug}/report-{YYYY}-{WW}.md using the template from {baseDir}/templates/domain-report.md.

Your report MUST include these sections:

Key Developments

Synthesize the top 3-5 developments from this period. Don't just list articles — connect them into a narrative. What's the story of this domain this week?

Entity Map

List the key entities (companies, people, products) with:

  • Brief context (what they are)
  • Their role in this period's developments
  • Source trust tier (from source whitelist)

Connections

Based on the cross-article analysis:

  • Which entities appear together? What does that mean?
  • Are there evolving stories (same topic across multiple days)?
  • Any contradictions between sources?
  • What patterns emerge?

Outlook

Forward-looking analysis:

  • What trends are accelerating?
  • What should the user watch next?
  • Any predictions based on the data?

Sources

List all articles analyzed with dates, sources, and URLs.

Step 6: Validate Report

bash {baseDir}/scripts/validate-report.sh memory/research/{domain-slug}/report-{YYYY}-{WW}.md

Fix any validation errors.

Constraints

  • Read-only on knowledge articles: Never modify harvested content
  • Summaries only: Never reproduce more than 10 words from any source
  • Source attribution: Always cite sources and trust tiers
  • One domain per run: Process domains sequentially, one report each
  • Model: Uses your default configured model — no override needed
  • Same legal constraints: As Knowledge Harvester — licensed APIs, summaries only
安全使用建议
This skill appears to do what it says: local entity extraction and report generation from harvested Markdown articles. Before installing: (1) ensure you run the Knowledge Harvester first so memory/knowledge/ contains files; (2) verify your OpenClaw runtime will substitute placeholders like {baseDir} and {domain-slug} when invoking the scripts; (3) note the README/metadata mention jq though the scripts don't use it—this is likely leftover and harmless but you can remove the jq requirement if you control the skill; (4) review the templates and scripts to confirm they meet your summary/attribution policies (the scripts do not enforce the 'max 10 words' rule—this is an agent-level behavioral constraint); (5) run the scripts in a sandbox or test environment first to confirm they behave as expected and only modify the intended memory/ directories.
功能分析
Type: OpenClaw Skill Name: clawforage-research-agent Version: 0.1.0 The clawforage-research-agent is a legitimate tool designed to analyze local knowledge articles and generate research reports. The skill uses bash scripts (extract-entities.sh, build-connections.sh) to perform entity extraction and relationship mapping via regex and standard text processing utilities. The instructions in SKILL.md are well-defined, focusing on local file operations within the 'memory/' directory, and include constraints to prevent data modification. No evidence of data exfiltration, malicious command execution, or deceptive prompt injection was found.
能力评估
Purpose & Capability
The skill's name/description match its behavior: it reads memory/knowledge/*.md, extracts entities, builds cross-article connections, and writes reports to memory/research. One minor mismatch: metadata and README list jq as a required binary, but none of the included scripts actually call jq; bash and grep are used as expected.
Instruction Scope
SKILL.md instructs only local operations on memory paths (reading knowledge articles, reading/writing memory/clawforage and memory/research). It explicitly constraints: read-only on knowledge articles and summaries-only rules. The instructions do reference placeholders ({baseDir}, {domain-slug}) which must be resolved by the runtime; otherwise commands will fail, but this is an implementation issue rather than a security concern.
Install Mechanism
Instruction-only skill (no install spec). Included code are simple Bash scripts that operate locally and do not download or execute remote code. No risky download/extract steps are present.
Credentials
No environment variables, credentials, or config paths are requested. The scripts operate on local memory paths only. This is proportionate to the stated research/reporting purpose.
Persistence & Privilege
The skill is not always-enabled and does not request elevated agent-wide privileges. It writes report outputs and optional source whitelist files under memory/, which is consistent with its purpose and does not modify other skills or system-wide settings.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install clawforage-research-agent
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /clawforage-research-agent 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v0.1.0
Initial release of clawforage-research-agent - Deep domain research using harvested articles with entity extraction and cross-article connections. - Generates structured weekly domain reports with narrative, entity mapping, relationship analysis, and forward-looking outlook. - Utilizes a source whitelist to prioritize trusted information in research output. - Ensures strict content re-use limits (max 10 words from any source) and thorough source attribution. - Processes one domain per run; integrates with Knowledge Harvester workflow and validation scripts.
元数据
Slug clawforage-research-agent
版本 0.1.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

ClawForage Research Agent 是什么?

Deep domain research — entity extraction, cross-article connections, and structured domain reports from your knowledge base. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 160 次。

如何安装 ClawForage Research Agent?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install clawforage-research-agent」即可一键安装,无需额外配置。

ClawForage Research Agent 是免费的吗?

是的,ClawForage Research Agent 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

ClawForage Research Agent 支持哪些平台?

ClawForage Research Agent 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 ClawForage Research Agent?

由 InspireHub.ai(@dainash)开发并维护,当前版本 v0.1.0。

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