deep-research

A process for investigating a question across many sources with parallel research agents, saved findings, and a final cited report.

In plain words
What is it for?
Use it for thorough multi-source investigations, evidence-based reports, and questions that need many sources or comparisons rather than a single quick lookup.
Why use it?
It helps organise broad research so evidence is gathered systematically, checked across sources, and kept available on disk during the work.

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/xiaomimimo/mimo-code/deep-research
Any agent
npx skills add XiaomiMiMo/MiMo-Code --skill deep-research
Clone the repo
git clone --depth 1 https://github.com/XiaomiMiMo/MiMo-Code

Made for: Claude Code, Codex.

Per session 109 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,489 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 $0.00109 $0.01489
Opus 5 $0.00055 $0.00745
Sonnet 5 $0.00022 $0.00298
Haiku 4.5 $0.00011 $0.00149

Measured 2d ago against content hash 0656515ea92f, 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.

packages/opencode/src/skill/builtin/.bundle/deep-research/SKILL.md · 107 lines

How it starts

The opening of the file, as written. The whole thing — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Deep Research

Orchestrate parallel research sub-agents, then write one coherent cited report. Research is parallel; writing is single-point — never let multiple agents write report sections.

Step 0 — Always first

  1. Run date +%Y-%m-%d via Bash. Never assume the current year from training data.
  2. Triage:
    • Answerable with 1-2 searches? → STOP, just use WebSearch directly. Do not use this skill.
    • Enumeration task (N items × M fields, e.g. "compare 20 frameworks")? → still this skill, but use table-oriented decomposition (one sub-agent per item batch).
    • Open-ended investigation? → continue below.
  3. Pick depth (default standard; user can override with words like "quick"/"exhaustive"):
Mode Sub-agents (round 1) Max follow-up rounds Sources target
quick 2-3 0 8+
standard 3-5 1 15+
deep 5-8 2 25+

These are hard budgets. Reflection (Phase 4) can spend them but never exceed them.

Workspace

All state lives on disk at ./research/<slug>/ — never only in context (survives compaction):

research/<slug>/
├── brief.md         # research brief — the single contract for all phases
├── findings/        # F1.md, F2.md ... one per sub-agent, structured evidence
└── REPORT.md        # final deliverable

On resume: re-read brief.md + list findings/, skip completed angles, continue.

Phase 1 — Scope

Ask at most one round of clarifying questions (AskUserQuestion), only if genuinely ambiguous: audience, time frame, region, decision at stake. If the user said "just run it" or intent is clear, skip asking and write assumptions into the brief instead.

Then write brief.md: refined question, scope boundaries (in/out), assumptions, depth mode, today's date. This brief — not the raw conversation — is what every later phase measures against.

Phase 2 — Plan

Decompose the brief into 3-8 independent research angles. Pull from these lenses as applicable: core facts/definitions · recent developments (last 12 months) · quantitative data/benchmarks · counter-arguments & failure cases · practitioner experience (forums, issues) · academic work · key players/alternatives.

Read the full file on GitHub · 107 lines

Files

What ships with it

3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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 · 107 lines · 109 tokens per session scan A 0656515ea92f

Subscribe to this mod's changes

deep-research is a skill published in the GitHub repository XiaomiMiMo/MiMo-Code (12,904 stars, last pushed 2d ago), licensed MIT. It adds 109 tokens to every session and 1,489 once invoked, about $0.0005 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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