deep-scout

deep-scout

Multi-stage deep intelligence pipeline (Search → Filter → Fetch → Synthesize).

🛠️ Installation

1. Ask OpenClaw (Recommended)

Tell OpenClaw: "Install the deep-scout skill." The agent will handle the installation and configuration automatically.

2. Manual Installation (CLI)

If you prefer the terminal, run:

openclaw skills install @jonathanjing/deep-scout

🚀 Usage

/deep-scout "Your research question" [--depth 5] [--freshness pw] [--country US] [--style report]

Options

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🛠️ Pipeline — Agent Loop Instructions

When this skill is invoked, execute the following four-stage pipeline:

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Stage 1: SEARCH

Call web_search with:

query: 
count: 
country: 
search_lang: 
freshness: 

Collect: title, url, snippet for each result.
If fewer than 3 results returned, retry with freshness: "py" (relaxed).

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Stage 2: FILTER

Load {baseDir}/prompts/filter.txt. Replace template vars:

• {{query}} → the user's query
• {{freshness}} → freshness param
• {{min_score}} → min_score param
• {{results_json}} → JSON array of search results

Call the LLM with this prompt. Parse the returned JSON array.
Keep only results where keep: true. Sort by score descending.
Take top depth URLs as the fetch list.

Deduplication: Max 2 results per root domain (already handled in filter prompt).

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Stage 3: FETCH (Tiered Escalation)

For each URL in the filtered list:

Tier 1 — web_fetch (fast):

Call web_fetch(url)
If content length >= 200 chars → accept, trim to max_chars_per_source

Tier 2 — Firecrawl (deep/JS):

If Tier 1 fails or returns < 200 chars:
  Run: bash "{baseDir}/scripts/firecrawl-wrap.sh"  
  If output != "FIRECRAWL_UNAVAILABLE" and != "FIRECRAWL_EMPTY" → accept

Tier 3 — Browser (last resort):

If Tier 2 fails:
  Call browser(action="open", url=url)
  Call browser(action="snapshot")
  Load `{baseDir}/prompts/browser-extract.txt`, substitute {{query}} and {{max_chars_per_source}}
  Call LLM with snapshot content + extraction prompt
  If output != "FETCH_FAILED:..." → accept

If all tiers fail: Use the original snippet from Stage 1 search results. Mark as [snippet only].

Store: { url: extracted_content } dict.

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Stage 4: SYNTHESIZE

Choose prompt template based on --style:

• report / bullets / timeline → {baseDir}/prompts/synthesize-report.txt
• comparison → {baseDir}/prompts/synthesize-comparison.txt

Replace template vars:

• {{query}} → user query
• {{today}} → current date (YYYY-MM-DD)
• {{language}} → language param
• {{source_count}} → number of successfully fetched sources
• {{dimensions_or_auto}} → dimensions param (or "auto")
• {{fetched_content_blocks}} → build as:

  [Source 1] (url1)
  
  ---
  [Source 2] (url2)
  
  

Call LLM with the filled prompt. The output is the final report.

If --output FILE is set, write the report to that file. Otherwise, print to the channel.

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⚙️ Configuration

Defaults are in config.yaml. Override via CLI flags above.

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📂 Project Structure

skills/deep-scout/
├── SKILL.md                     ← This file (agent instructions)
├── config.yaml                  ← Default parameter values
├── prompts/
│   ├── filter.txt               ← Stage 2: relevance scoring prompt
│   ├── synthesize-report.txt    ← Stage 4: report/bullets/timeline synthesis
│   ├── synthesize-comparison.txt← Stage 4: comparison table synthesis
│   └── browser-extract.txt      ← Stage 3: browser snapshot extraction
├── scripts/
│   ├── run.sh                   ← CLI entrypoint (emits pipeline actions)
│   └── firecrawl-wrap.sh        ← Firecrawl CLI wrapper with fallback handling
└── examples/
    └── openclaw-acquisition.md  ← Example output: OpenClaw M&A intelligence

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🔧 Error Handling

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📋 Example Outputs

See examples/openclaw-acquisition.md for a full sample report.

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Deep Scout v0.1.5 · OpenClaw Skill