Deep-Research-skills is a structured research workflow for Claude Code, OpenCode, and Codex that guides agents through outlining and then investigating a question. Researchers use it for tasks such as literature reviews, technology comparisons, market analysis, and due diligence, with human approval during the process.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Weizhena/Deep-Research-skills --skill research-deepgit clone --depth 1 https://github.com/Weizhena/Deep-Research-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/weizhena/deep-research-skills/research-deep)<a href="https://agentmods.dev/skills/weizhena/deep-research-skills/research-deep"><img src="https://agentmods.dev/badge/skills/weizhena/deep-research-skills/research-deep/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/weizhena/deep-research-skills/research-deep"><img src="https://agentmods.dev/badge/skills/weizhena/deep-research-skills/research-deep.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00022 | $0.00746 |
| Opus 5 | $0.00011 | $0.00373 |
| Sonnet 5 | $0.00004 | $0.00149 |
| Haiku 4.5 | $0.00002 | $0.00075 |
Grade A, and why
research-deep scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 99 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 ~/.codex/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 ~/.codex/skills/research/validate_json.py -f {project_dir}/fields.yaml -j {project_dir}/results/GitHub_Copilot.json
Task is complete only after validation passes.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 99 lines · 22 tokens per session scan A 8fcc496fb1af
research-deep is a skill published in the GitHub repository Weizhena/Deep-Research-skills (2,135 stars, last pushed 19d ago), licensed MIT. It adds 22 tokens to every session and 746 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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