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-add-itemsgit 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-add-items)<a href="https://agentmods.dev/skills/weizhena/deep-research-skills/research-add-items"><img src="https://agentmods.dev/badge/skills/weizhena/deep-research-skills/research-add-items/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-add-items"><img src="https://agentmods.dev/badge/skills/weizhena/deep-research-skills/research-add-items.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 | $0.00015 | $0.00167 |
| Opus 5 | $0.00008 | $0.00084 |
| Sonnet 5 | $0.00003 | $0.00033 |
| Haiku 4.5 | $0.00002 | $0.00017 |
Grade A, and why
research-add-items 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.
What it actually says
Research Add Items - Supplement Research Objects
Trigger
/research-add-items
Workflow
Step 1: Auto-locate Outline
Find */outline.yaml file in current working directory, auto-read.
Step 2: Get Supplement Sources in Parallel
Simultaneously:
- A. Ask user: What items to supplement? Any specific names?
- B. Ask if Web Search needed: Launch agent to search for more items?
Step 3: Merge and Update
- Append new items to outline.yaml
- Display to user for confirmation
- Avoid duplicates
- Save updated outline
Output
Updated {topic}/outline.yaml file (in-place modification)
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 · 29 lines · 15 tokens per session scan A 52195e187e55
research-add-items is a skill published in the GitHub repository Weizhena/Deep-Research-skills (2,135 stars, last pushed 19d ago), licensed MIT. It adds 15 tokens to every session and 167 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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