Research deep
Skill Weizhena/Deep-Research-skills/skills/research-en/research-deep
Read research outline, launch independent agent for each item for deep research. Disable task output.From its SKILL.md
npx -y skills add Weizhena/Deep-Research-skills --skill research-deepAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
- runs commandsInstructs the agent to run 1 command, including `python ~/.claude/skills/research/validate_json.py -f {fields_path} -j {output_path}`.
SKILL.md
3.3 KB, 719 tokens by cl100k_base, as published. Nobody here has run it
Research Deep - Deep Research
Trigger
/research-deep
Workflow
Step 1: Auto-locate Outline
Find */outline.yaml file in current working directory, read items list, execution config (including items_per_agent).
Step 2: Resume Check
- Check completed JSON files in output_dir
- Skip completed items
Step 3: Batch Execution
- Batch by batch_size (need user approval before next batch)
- Each agent handles items_per_agent items
- Launch web-search-agent (background parallel, disable task output)
Parameter Retrieval:
{topic}: topic field from outline.yaml{item_name}: item's name field{item_related_info}: item's complete yaml content (name + category + description etc.){output_dir}: execution.output_dir from outline.yaml (default: ./results){fields_path}: absolute path to {topic}/fields.yaml{output_path}: absolute path to {output_dir}/{item_name_slug}.json (slugify item_name: replace spaces with _, remove special chars)
Hard Constraint: The following prompt must be strictly reproduced, only replacing variables in {xxx}, do not modify structure or wording.
Prompt Template:
prompt = f"""## Task
Research {item_related_info}, output structured JSON to {output_path}
## Field Definitions
Read {fields_path} to get all field definitions
## Output Requirements
1. Output JSON according to fields defined in fields.yaml
2. Mark uncertain field values with [uncertain]
3. Add uncertain array at the end of JSON, listing all uncertain field names
4. All field values must be in English
## Output Path
{output_path}
## Validation
After completing JSON output, run validation script to ensure complete field coverage:
python ~/.claude/skills/research/validate_json.py -f {fields_path} -j {output_path}
Task is complete only after validation passes.
"""
One-shot Example (assuming researching GitHub Copilot):
## Task
Research name: GitHub Copilot
category: International Product
description: Developed by Microsoft/GitHub, first mainstream AI coding assistant, ~40% market share, output structured JSON to {project_dir}/results/GitHub_Copilot.json
## Field Definitions
Read {project_dir}/fields.yaml to get all field definitions
## Output Requirements
1. Output JSON according to fields defined in fields.yaml
2. Mark uncertain field values with [uncertain]
3. Add uncertain array at the end of JSON, listing all uncertain field names
4. All field values must be in English
## Output Path
{project_dir}/results/GitHub_Copilot.json
## Validation
After completing JSON output, run validation script to ensure complete field coverage:
python ~/.claude/skills/research/validate_json.py -f {project_dir}/fields.yaml -j {project_dir}/results/GitHub_Copilot.json
Task is complete only after validation passes.
Step 4: Wait and Monitor
- Wait for current batch to complete
- Launch next batch
- Display progress
Step 5: Summary Report
After all complete, output:
- Completion count
- Failed/uncertain marked items
- Output directory
Agent Config
- Background execution: Yes
- Task Output: Disabled (agent has explicit output file when complete)
- Resume support: Yes
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.
Gives 0 of the 12 instructions most research analysis skills give in 719 tokens
Counted across 1,213 of the 2,113 authors here whose files we hold, read 2026-09-06
- Cite sources for every important claimin 47 of 1213, across 38 files
- Separate facts from inferences and recommendationsin 21 of 1213, across 12 files
- Write findings to a markdown filein 19 of 1213
- Label every insight with a confidence levelin 18 of 1213, across 8 files
- Read product marketing context before asking questionsin 18 of 1213, across 8 files
- Rank themes by frequency and intensityin 16 of 1213, across 6 files
- Establish research mode before proceedingin 16 of 1213, across 6 files
- Segment survey responses by customer tier or tenurein 16 of 1213, across 6 files
- Categorize support tickets before analyzingin 16 of 1213, across 6 files
- Weight research sources from the last twelve monthsin 16 of 1213, across 6 files
- Use at least five data points per segmentin 15 of 1213, across 5 files
- Extract verbatim quotes for all research findingsin 15 of 1213, across 5 files
Said here and by no other author read
- Skip items already present in output directory
- Batch research items based on configuration
- Request user approval before starting next batch
- Launch web search agent for each item
- Disable task output for background agents
- Mark uncertain field values with uncertain
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.