deep-research

A research workflow that investigates a question across multiple sources and produces a report with citations, which are links or references to supporting evidence.

In plain words
What is it for?
Researching topics, gathering evidence from current or original sources, comparing claims, and creating reusable cited reports.
Why use it?
It organizes a broad question into smaller parts and checks sources instead of relying on a single search result. Unresolved gaps and disagreements can be recorded.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/autohandai/code-cli/deep-research
Any agent
npx skills add autohandai/code-cli --skill deep-research
Clone the repo
git clone --depth 1 https://github.com/autohandai/code-cli

Made for: Claude Code, Codex.

Per session 21 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 449 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00021 $0.00449
Opus 5 $0.00010 $0.00225
Sonnet 5 $0.00004 $0.00090
Haiku 4.5 $0.00002 $0.00045

Measured 2d ago against content hash 4f3638352827, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

deep-research 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 2d 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.

src/skills/builtin/deep-research/SKILL.md · 53 lines

What it actually says

You conduct iterative, multi-source deep research and produce a reusable cited research report.

Scope The Question

  1. Restate the user's research topic or question in concrete terms.
  2. Identify 4-8 subquestions that would fully answer it.
  3. Ask at most one clarifying question only when the topic is too ambiguous to research safely.
  4. Track the research phases and subquestions with todo_write.

Gather Evidence

For each subquestion:

  1. Use web_search to discover current sources.
  2. Use fetch_url to read the strongest sources instead of relying on snippets.
  3. Prefer primary sources, official documentation, papers, standards, release notes, or direct project/company material.
  4. Record each source URL, source title, publication date when available, fetched date, and confidence.
  5. Look for disagreement, stale claims, and missing context.
  6. Continue pulling threads until every subquestion is answered or the gap is explicitly documented.

Use tool_search to find available agent, task, or parallel research tools when the research topic is broad enough to benefit from delegation.

Synthesize

  1. Cross-check load-bearing facts against at least two independent sources when possible.
  2. Note whether evidence is recent, historical, speculative, or contradicted.
  3. Resolve contradictions when the evidence supports a resolution; otherwise flag them.
  4. Keep unverified claims out of the report.

Report

Write a self-contained markdown report to the path supplied by the slash command.

Required sections:

  • # <clear title>
  • ## Summary
  • ## Findings
  • ## Open questions / uncertainty
  • ## Sources

Findings must be organized by theme or subquestion and include inline numbered citations like [1].

The Sources section must number every cited source and include title, URL, publication date if known, and fetched date when useful.

Do not stop until the report is saved with write_file and the final response includes the exact saved path.

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. 2d ago First seen · 53 lines · 21 tokens per session scan A 4f3638352827

Subscribe to this mod's changes

deep-research is a skill published in the GitHub repository autohandai/code-cli (181 stars, last pushed 6d ago), licensed Apache-2.0. It adds 21 tokens to every session and 449 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.

Related

Other skills, from other repositories

neuron-test-engineer

Write tests for Neuron AI agents, RAG systems, workflows, and tools using the built-in testing utilities. Use this skill when the user mentions testing agents, writing unit tests, mocking AI providers, testing tool execution, verifying RAG retrieval, testing workflow behavior, or creating test cases for Neuron AI…

neuron-core/neuron-ai · 94 tokens

neuron-evaluation-engineer

Create and run AI evaluations with datasets, assertions, and output drivers in Neuron AI. Use this skill whenever the user mentions evaluation, testing AI systems, creating evaluators, dataset-driven testing, assertion-based validation, or wants to measure AI system performance. Also trigger for tasks involving…

neuron-core/neuron-ai · 77 tokens

neuron-structured-output

Design and implement structured output classes for Neuron AI agents using SchemaProperty attributes and validation rules. Use this skill when the user mentions structured output, JSON schema extraction, data validation, output classes, DTOs for AI responses, extracting structured data from LLM, or configuring property…

neuron-core/neuron-ai · 104 tokens

neuron-agent-builder

Create and configure Neuron AI agents with providers, tools, instructions, and memory. Use this skill whenever the user mentions building agents, creating AI assistants, setting up LLM-powered chat bots, configuring chat agents, or wants to create an agent that can talk, use tools, or handle conversations. Also…

neuron-core/neuron-ai · 89 tokens

neuron-debugger

Debug and monitor Neuron AI applications with Inspector APM, event observability, logging, and performance analysis. Use this skill whenever the user mentions debugging, monitoring, observability, performance analysis, tracing, Inspector, or needs to understand why an agent is behaving a certain way. Also trigger for…

neuron-core/neuron-ai · 90 tokens

neuron-rag-specialist

Implement RAG (Retrieval-Augmented Generation) with Neuron AI including vector stores, embeddings providers, document loaders, and retrieval strategies. Use this skill whenever the user mentions RAG, retrieval, vector search, document retrieval, semantic search, knowledge bases, chat with documents, or wants to build…

neuron-core/neuron-ai · 95 tokens