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-reportgit 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-report)<a href="https://agentmods.dev/skills/weizhena/deep-research-skills/research-report"><img src="https://agentmods.dev/badge/skills/weizhena/deep-research-skills/research-report/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-report"><img src="https://agentmods.dev/badge/skills/weizhena/deep-research-skills/research-report.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- 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 | $0.00021 | $0.00873 |
| Opus 5 | $0.00010 | $0.00436 |
| Sonnet 5 | $0.00004 | $0.00175 |
| Haiku 4.5 | $0.00002 | $0.00087 |
Grade A, and why
research-report 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 — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Report - Summary Report
Trigger
/research-report
Workflow
Step 1: Locate Results Directory
Find */outline.yaml in current working directory, read topic and output_dir config.
Step 2: Scan Optional Summary Fields
Read all JSON results, extract fields suitable for TOC display (numeric, short metrics), e.g.:
- github_stars
- google_scholar_cites
- swe_bench_score
- user_scale
- valuation
- release_date
Use request_user_input to ask user:
- Which fields to display in TOC besides item name?
- Provide dynamic options list (based on actual fields in JSON)
Step 3: Generate Python Conversion Script
Generate generate_report.py in {topic}/ directory, script requirements:
- Read all JSON from output_dir
- Read fields.yaml to get field structure
- Cover all field values from each JSON
- Skip fields with values containing [uncertain]
- Skip fields listed in uncertain array
- Generate markdown report format: Table of contents (with anchor links + user-selected summary fields) + Detailed content (by field category)
- Save to
{topic}/report.md
TOC Format Requirements:
- Must include every item
- Each item displays: number, name (anchor link), user-selected summary fields
- Example:
1. [GitHub Copilot](#github-copilot) - Stars: 10k | Score: 85%
Script Technical Requirements (Must Follow)
1. JSON Structure Compatibility Support two JSON structures:
- Flat structure: Fields directly at top level
{"name": "xxx", "release_date": "xxx"} - Nested structure: Fields in category sub-dict
{"basic_info": {"name": "xxx"}, "technical_features": {...}}
Field lookup order: Top level -> category mapping key -> Traverse all nested dicts
2. Category Multi-language Mapping fields.yaml category names and JSON keys can be any combination (CN-CN, CN-EN, EN-CN, EN-EN). Must establish bidirectional mapping:
CATEGORY_MAPPING = {
"Basic Info": ["basic_info", "Basic Info"],
"Technical Features": ["technical_features", "technical_characteristics", "Technical Features"],
"Performance Metrics": ["performance_metrics", "performance", "Performance Metrics"],
"Milestone Significance": ["milestone_significance", "milestones", "Milestone Significance"],
"Business Info": ["business_info", "commercial_info", "Business Info"],
"Competition & Ecosystem": ["competition_ecosystem", "competition", "Competition & Ecosystem"],
"History": ["history", "History"],
"Market Positioning": ["market_positioning", "market", "Market Positioning"],
}
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 · 92 lines · 21 tokens per session scan A 3f338b814cda
research-report is a skill published in the GitHub repository Weizhena/Deep-Research-skills (2,135 stars, last pushed 19d ago), licensed MIT. It adds 21 tokens to every session and 873 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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