research-deep

research-deep is a skill for Claude Code from rvk7895/llm-knowledge-bases. It costs 19 tokens per session (887 once invoked), scanned A, original, MIT.

A workflow that reads a research outline and assigns its items to independent research agents. It saves each completed item as a JSON file and can resume from earlier results.

In plain words
What is it for?
Use it to run item-by-item research from an outline, distribute work among agents, resume interrupted research, and store structured results.
Why use it?
It splits a large research job into smaller pieces and avoids repeating items that are already finished. Work can be processed in batches requiring approval before the next batch.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: reads .claude/ paths.

Not installable: its command points at a path on the author’s own machine, so it runs nowhere else. The line is /home/weizhena/AIcoding/aicoding-history/results/GitHub_Copilot.json.

Part of the kb plugin — 8 skills shipped together

Good fit Use it to run item-by-item research from an outline, distribute work among agents, resume interrupted research, and store structured results.

Compare 6 skills from other repositories ↓
Install

Getting it into your agent

This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.

Claude Code
/plugin marketplace add rvk7895/llm-knowledge-bases
Claude Code
/plugin install kb

Made for: Claude Code.

Or install kb, the plugin that ships this one along with the rest of its 8 skills.

Wrote 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.

agentmods badge for research-deep

README.md
[![agentmods](https://agentmods.dev/badge/skills/rvk7895/llm-knowledge-bases/research-deep/github.svg)](https://agentmods.dev/skills/rvk7895/llm-knowledge-bases/research-deep)
Your own site
<a href="https://agentmods.dev/skills/rvk7895/llm-knowledge-bases/research-deep"><img src="https://agentmods.dev/badge/skills/rvk7895/llm-knowledge-bases/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.

agentmods 80×15 button for research-deep

Your own site · 80×15
<a href="https://agentmods.dev/skills/rvk7895/llm-knowledge-bases/research-deep"><img src="https://agentmods.dev/badge/skills/rvk7895/llm-knowledge-bases/research-deep.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 19 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 887 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00019 $0.00887
Opus 5 $0.00010 $0.00443
Sonnet 5 $0.00004 $0.00177
Haiku 4.5 $0.00002 $0.00089

Measured 9d ago against content hash 3770a5df327c, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

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 9d 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.

plugins/kb/skills/research-deep/SKILL.md · 100 lines

How it starts

The opening of the file, as written. The whole thing — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Attribution: Originally authored by Weizhena. Included with attribution for use in the Deep query workflow.

Research Deep - Deep Research

Trigger Method

/research-deep

Execution Flow

Step 1: Auto-locate Outline

Find */outline.yaml file in current working directory, read items list and execution configuration (including items_per_agent).

Step 2: Resume From Checkpoint

  • Check completed JSON files in output_dir
  • Skip already completed items

Step 3: Batch Execution

  • Execute in batches by batch_size (need user approval to proceed to next batch after completing one)
  • Each agent handles items_per_agent projects
  • Launch web-search-agent (background parallel, task output disabled)

Parameter Retrieval:

  • {topic}: topic field from outline.yaml
  • {item_name}: name field of item
  • {item_related_info}: complete yaml content of item (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 characters)

Hard Constraint: The following prompt must be strictly recited, only replace variables in {xxx}, do not rewrite 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 as [不确定]
3. Add uncertain array at end of JSON, listing all uncertain field names
4. All field values must be output in Chinese (research process can use English, but final JSON values in Chinese)

## 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 only complete after validation passes.
"""

Read the full file on GitHub · 100 lines

Changes

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.

  1. 9d ago First seen · 100 lines · 19 tokens per session scan A 3770a5df327c

Subscribe to this mod's changes

research-deep is a skill published in the GitHub repository rvk7895/llm-knowledge-bases (37 stars, last pushed 2mo ago), licensed MIT. It adds 19 tokens to every session and 887 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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