Agent skill

Deep Research

by shepherdjerred in shepherdjerred/monorepo

This skill should be used when the user asks to "deep research", "research this topic", "investigate thoroughly", "do a deep dive on", "comprehensive research on", "find everything about", "survey…

GPL-3.0Auto-check: notesResearch & Science

Install Deep Research

skills CLI
$ npx skills add shepherdjerred/monorepo --skill deep-research -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install shepherdjerred/monorepo deep-research --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/shepherdjerred/monorepo.git skills-src && mkdir -p .claude/skills && cp -r skills-src/packages/dotfiles/dot_agents/skills/deep-research .claude/skills/deep-research && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
deep-research
GitHub stars
112
Token cost
~4.7k tokens
SKILL.md length
2,049 words
Files
4 (incl. references)
Skills in repo
63
Repo updated
First seen
Licence
GPL-3.0

At a glance

This skill should be used when the user asks to "deep research", "research this topic", "investigate thoroughly", "do a deep dive on", "comprehensive research on", "find everything about", "survey…

  • Works in 7 steps: Plan (Interactive) → Investigate (Iterative) → 5: Cross-Agent Reconciliation → …
  • Asks to deep research
  • SKILL.md covers Overview, Task Tracking, Effort Levels and Core Workflow, plus 4 more sections
  • Calls gh

What it does

Deep Research is an agent skill from shepherdjerred/monorepo. This skill should be used when the user asks to "deep research", "research this topic", "investigate thoroughly", "do a deep dive on", "comprehensive research on", "find everything about", "survey the landscape of", "compare approaches to", "write a report on", "gather information about", or wants multi-source investigation with synthesis and citations. Also triggers on "what are the best practices for", "how do others solve", or "state of the art in" when the user clearly wants breadth and depth beyond a simple…

Its SKILL.md is about 4.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including reference files (for example `references/academic-sources.md`, `references/evidence-standards.md` and `references/research-patterns.md`).

It sits in Research & Science, covering Deep research, Report writing and Literature review. The repository describes itself as: Monorepo for all of my projects. The licence is GPL-3.0.

When your agent uses it

  • Asks to deep research
  • Research this topic
  • Investigate thoroughly
  • Do a deep dive on

Example prompts

  • “deep research”
  • “research this topic”
  • “investigate thoroughly”
  • “/deep-research”

Requirements

  • Pre-approved tools (allowed-tools): Read, Write, Bash, Glob, Grep, Agent, WebSearch, WebFetch, TaskCreate, TaskUpdate, TaskList

Workflow steps

7 steps, taken from the step headings in SKILL.md.

  1. Plan (Interactive)
  2. Investigate (Iterative)
  3. 5: Cross-Agent Reconciliation
  4. Draft Report
  5. Adversarial Review
  6. Editorial Synthesis
  7. Deliver

What it can do on your machine

Read from SKILL.md and the folder at commit 46ddf2f. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Write
    • Bash
    • Glob
    • Grep
    • Agent
    • WebSearch
    • WebFetch
    • TaskCreate
    • TaskUpdate

    …and 1 more on the same allowed-tools line.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • gh

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use gh, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Deep Research loads about 4.7k tokens when it runs, and up to ~9.2k if it reads all its reference files. Until then it costs about 135 tokens; SKILL.md has 2,049 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~135
When it runs · the whole SKILL.md, loaded when a task matches
~4.7k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~9.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Read, Write, Bash, Glob, Grep, Agent, WebSearch, WebFetch, TaskCreate, TaskUpdate, TaskList

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from shepherdjerred/monorepo at commit 46ddf2f, republished under its GPL-3.0 licence (© shepherdjerred). 2,049 words, ~4,731 tokens.

Download SKILL.mdSave it as .claude/skills/deep-research/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
deep-research
description
This skill should be used when the user asks to "deep research", "research this topic", "investigate thoroughly", "do a deep dive on", "comprehensive research on", "find everything about", "survey the landscape of", "compare approaches to", "write a report on", "gather information about", or wants multi-source investigation with synthesis and citations. Also triggers on "what are the best practices for", "how do others solve", or "state of the art in" when the user clearly wants breadth and depth beyond a simple answer.
allowed-tools
Read, Write, Bash, Glob, Grep, Agent, WebSearch, WebFetch, TaskCreate, TaskUpdate, TaskList
user-invocable
true

Deep Research

Conduct thorough, multi-source research on a topic with iterative investigation, cross-verification, and structured synthesis into a cited report.

Overview

Deep research goes beyond a single search query. It decomposes a question into sub-topics, investigates each through multiple sources, identifies gaps, iterates to fill them, and synthesizes findings into a structured report with citations. The process is designed for transparency — the user sees the plan, approves it, and can steer the investigation.

Task Tracking

Use TaskCreate and TaskUpdate throughout the research process to give the user visibility into progress. This is mandatory — do not skip task creation.

  1. At the start of Phase 2, create a task for each sub-question from the research plan (e.g., "Investigate: [sub-question]"). Use activeForm to show what's happening in the spinner (e.g., "Investigating [sub-question]").
  2. At the start of each subsequent phase (Draft, Adversarial Review, Editorial Synthesis, Deliver), create a task for that phase.
  3. Mark tasks in_progress when you begin working on them and completed when done.
  4. For sub-agents, create their tasks before launching them so the user can see parallel work in progress.

Keep task subjects short and specific. The description field should include enough context to understand scope.

Effort Levels

Research intensity is controlled by an effort parameter: low, medium (default), high, or ultra. This acts as an exponential multiplier across the entire process.

ParameterLowMediumHighUltra
Sub-questions3-55-78-1215-20
Parallel agents23-46-810+
Sources per question~4~8~15~25+
Iteration rounds (max)2346
Adversarial reviewLightweight (no source verification)FullFull + source verificationFull + multiple reviewers + source verification
Output formatMarkdown + PDFMarkdown + PDFMarkdown + PDFMarkdown + PDF (detailed)
Report length3-5 pages6-12 pages15-25 pages30+ pages

Determining effort:

  • "quick research", "brief overview" → low
  • No qualifier (default) → medium
  • "thorough", "comprehensive", "deep dive" → high
  • "exhaustive", "leave no stone unturned", explicit "ultra" → ultra
  • User can also specify explicitly (e.g., /deep-research high: topic)

All parameters in the workflow below (sub-question count, agent count, iteration limits, etc.) must follow the effort level table. When launching sub-agents, pass the effort level so they calibrate their depth accordingly.

Core Workflow

Execute these phases in order. Do not skip Phase 1 or Phase 2.

Phase 1: Plan (Interactive)

Before any searching, decompose the user's question:

  1. Clarify scope — If the query is ambiguous, ask 1-2 focused questions (not more) to narrow down what the user actually needs. Skip if the intent is already clear.
  2. Decompose into sub-questions — Break the topic into sub-questions per the effort level table. Each should target a distinct angle (e.g., "how does X work?", "what are alternatives to X?", "what do practitioners say about X?").
  3. Identify source types — For each sub-question, note where to look: official docs, GitHub repos, HN discussions, academic papers, blog posts, Wikipedia, etc.
  4. Present the plan — Show the user the research plan as a numbered list of sub-questions with source strategies. Ask for approval or modifications before proceeding.
markdown
## Research Plan: [Topic]

### Sub-questions

1. [Sub-question] — Sources: [where to look]
2. [Sub-question] — Sources: [where to look]
   ...

### Effort: [low/medium/high/ultra]

- Sub-questions: [N] (per effort table)
- Sources per question: ~[M]
- Parallel agents: [P]
- Max iterations: [I]

Proceed with this plan?
Phase 2: Investigate (Iterative)

For each sub-question, execute this loop:

SEARCH → READ → EXTRACT → EVALUATE → (iterate if gaps remain)

Search strategy:

  • Start with WebSearch for broad discovery
  • Use lightpanda for full page content extraction per global instructions
  • Fall back to WebFetch if lightpanda fails on a particular URL
  • For detailed search strategies and source evaluation heuristics, read references/research-patterns.md
  • Use Agent tool to parallelize independent sub-question investigations — launch multiple research sub-agents for unrelated sub-questions simultaneously
  • For GitHub-specific research, use gh search repos, gh search code, and browse READMEs directly

Source priorities (per user preferences):

  1. GitHub repositories and READMEs
  2. Hacker News discussions and comments
  3. Wikipedia
  4. Official documentation and first-party sources
  5. Technical blog posts and articles
  6. Academic papers (when relevant)

Per-source extraction:

  • Extract key claims, data points, and quotes
  • Note the source URL and a brief credibility assessment
  • Flag disagreements between sources explicitly

Gap analysis after each round:

  • List what is now known vs. what remains unanswered
  • If significant gaps remain, formulate new search queries targeting those gaps
  • Limit iterations to the maximum specified by the effort level table — after reaching the limit, synthesize with what is available and note remaining unknowns

Context management:

  • After extracting findings from a source, distill them into bullet points — do not carry full page content forward
  • Use a running findings list organized by sub-question
  • When context grows large, summarize completed sub-questions to free up space
Phase 2.5: Cross-Agent Reconciliation

When multiple agents investigated overlapping sub-questions, reconcile before drafting:

  1. Identify contradictions — Compare agent findings on the same topic. Flag any claims where agents disagree.
  2. Resolve or escalate — For each contradiction:
    • If one agent cited a primary source and the other cited a secondary source, prefer the primary
    • If both have equal sourcing, do a targeted follow-up search to break the tie
    • If unresolvable, note explicitly as contested in the draft
  3. Merge findings — Create a unified findings list organized by sub-question (not by agent) before proceeding to the draft
Phase 3: Draft Report

Compile findings into a structured markdown draft report:

markdown
# [Research Topic]

## Summary

[2-3 paragraph executive summary of key findings]

## Findings

### [Sub-topic 1]

[Synthesized findings with inline citations as numbered references, e.g. [1]]

### [Sub-topic 2]

...

## Key Takeaways

- [Actionable insight 1]
- [Actionable insight 2]
- ...

## Open Questions

- [What remains unknown or contested]

## Sources

1. [Title](URL) — [brief description]
2. [Title](URL) — [brief description]
   ...

Synthesis principles:

  • Cross-verify claims that appear in multiple sources; note conflicts
  • Distinguish between widely-agreed facts and individual opinions
  • Lead with what matters most to the user's original question
  • Include direct quotes sparingly — only when they are particularly insightful
  • Every factual claim should trace to at least one numbered source
Phase 4: Adversarial Review

After drafting the report, perform an adversarial review scaled to the effort level (see effort table: lightweight at low, full at medium, full + source verification at high, full + multiple reviewers at ultra). This is a separate, critical pass whose sole job is to find problems. The adversary has no attachment to the draft — it exists to stress-test it.

Launch a review sub-agent with this mandate:

Act as a skeptical, adversarial reviewer of the following research report. Your job is to find every weakness, not to be polite. Evaluate:

  1. Factual accuracy — Are any claims unsupported, exaggerated, or contradicted by the cited sources? Are citations used correctly or do they misrepresent the source?
  2. Missing perspectives — What important angles, counterarguments, or stakeholder viewpoints are absent? What would a knowledgeable critic say is missing?
  3. Logical gaps — Are there non-sequiturs, unwarranted conclusions, or leaps in reasoning? Does the evidence actually support the takeaways?
  4. Source quality — Are any sources low-credibility, outdated, or over-relied upon? Is the source diversity sufficient?
  5. Bias detection — Does the report favor one side without acknowledging trade-offs? Is it selling a conclusion rather than presenting evidence?
  6. Staleness risk — Are any claims likely outdated given the current date? Are there fast-moving areas where the findings may already be wrong?

Return a structured critique with severity ratings (critical / major / minor) for each issue found. Be specific — cite the exact claim or section that is problematic and explain why.

You have access to WebSearch, WebFetch, and Bash (for lightpanda). For any claim you flag as potentially inaccurate or exaggerated, spot-check it by visiting the cited source URL or searching for counter-evidence. Do not rely solely on logical analysis — verify at least the 3 most critical factual claims directly.

The adversary must NOT:

  • Suggest rewording for style or tone
  • Praise the report
  • Hedge its criticisms
Show full SKILL.md (865 more words)Show less
Phase 5: Editorial Synthesis

Take the original draft report and the adversarial critique, and produce the final report. This is an editorial pass, not a rubber stamp.

For each adversarial finding:

SeverityAction
CriticalMust be resolved — correct the claim, add the missing evidence, or remove the unsupported assertion. If resolution requires additional research, do a targeted follow-up search.
MajorShould be resolved — add the missing perspective, strengthen the sourcing, or add a caveat.
MinorUse judgment — fix if straightforward, otherwise note in Open Questions.

Editorial principles:

  • Do not simply append disclaimers to dodge criticisms — fix the underlying issue
  • If the adversary identified a genuinely missing perspective, research it briefly and incorporate it
  • If a claim cannot be adequately sourced after the adversary challenged it, downgrade it from a finding to an open question
  • Preserve the report's readability — weave fixes into the narrative rather than adding a "corrections" section
  • Add a brief "Methodology & Limitations" section at the end noting the research process, number of sources consulted, and any known blind spots the adversary surfaced that could not be fully resolved

The final report should read as if it was written correctly the first time — the adversarial process is invisible to the reader.

Before proceeding to Phase 6, walk through the Quality Checklist item by item. For each item, note pass/fail. If any item fails, fix it before delivering. This is a gate, not a suggestion.

Phase 6: Deliver

Every research report is delivered in three formats: Markdown (.md), Typst (.typ), and PDF (.pdf).

  1. Create output directory — Run mkdir -p ~/.claude-extra/research/ to ensure the directory exists.
  2. Write Markdown — Save the final report to ~/.claude-extra/research/[topic-slug].md.
  3. Write Typst — Do not mechanically convert Markdown to Typst. The Typst document should be designed for the reader. Load the typst-authoring skill (especially references/visualizations.md) and invest effort in:
    • Comparison tables instead of prose listing pros/cons
    • Diagrams (via fletcher, CeTZ, or pintorita) for architecture, flows, and relationships
    • Callout boxes (via gentle-clues) to highlight key findings, warnings, and recommendations
    • Charts (via lilaq or CeTZ) when presenting data, benchmarks, or trends
    • Timeline diagrams (via timeliney) for roadmaps or chronological analysis
    • Visual hierarchy — use color, spacing, and layout to guide the reader's eye The reader should be able to understand the core findings by scanning the document for 60 seconds. Save to ~/.claude-extra/research/[topic-slug].typ.
  4. Compile PDF — Run typst compile ~/.claude-extra/research/[topic-slug].typ ~/.claude-extra/research/[topic-slug].pdf. If compilation fails, fix the Typst source (common issues: unescaped special characters #, @, $ need \#, \@, \$ in content text) and retry.
  5. Open PDF — Run open ~/.claude-extra/research/[topic-slug].pdf to open the rendered PDF for the user. This step is mandatory — always open the PDF. Only open the PDF once the entire research process is 100% complete — all phases finished, editorial synthesis done, quality checklist passed, and final PDF compiled without errors. Never open intermediate or draft versions.
  6. Present summary — Show a concise inline summary with:
    • The report title and key findings (3-5 bullets)
    • File paths for all three outputs
    • An offer to go deeper on any sub-topic
    • Any actionable next steps the research revealed

Parallelization Strategy

Maximize throughput by running independent work concurrently:

TaskParallelizable?How
Investigating unrelated sub-questionsYesLaunch multiple Agent sub-agents
Searching + reading within one sub-questionSequentialEach search informs the next
Cross-verifying a claim across sourcesYesFetch multiple URLs in parallel
Gap analysisSequentialRequires all prior findings
Adversarial reviewYesLaunch as sub-agent while preparing delivery notes
Editorial fixes requiring new researchYesTargeted searches in parallel

When launching parallel sub-agents, give each a focused prompt:

  • The specific sub-question to investigate
  • The source types to prioritize
  • Instructions to return distilled findings (not raw page content)
  • A reminder to include source URLs for every claim

Important: Do not use SendMessage to communicate status updates about background agents. Plain text output is directly visible to the user — just output text normally. SendMessage is for team communication between agents, not for status updates to the user.

Quality Checklist

Before delivering the final report, verify:

  • Every factual claim has a cited source
  • No source URLs are fabricated — every URL was actually visited
  • Conflicts between sources are noted, not silently resolved
  • The report directly answers the user's original question
  • Open questions and limitations are explicitly stated
  • The summary can stand alone for a reader who skips the details
  • The adversarial review was completed and all critical/major findings addressed
  • A Methodology & Limitations section is present
  • All three output files (.md, .typ, .pdf) were generated and the PDF compiled without errors

Adaptation by Research Type

Research TypeEmphasisExample
Technology comparisonFeature matrices, trade-offs, community sentiment"Compare X vs Y vs Z"
Best practices surveyConsensus patterns, anti-patterns, practitioner advice"How do teams handle X?"
Landscape surveyCategorization, major players, trends"What tools exist for X?"
Deep diveArchitecture, implementation details, edge cases"How does X work internally?"
Decision supportPros/cons, risk assessment, recommendations"Should we use X?"

Adjust source priorities and report structure to match the research type. For comparisons, use tables. For best practices, lead with the consensus view. For landscape surveys, categorize before detailing.

Additional Resources

  • references/research-patterns.md — Source evaluation heuristics, iteration patterns, and report structure templates by research type

When generating Typst output, load the typst-authoring skill for language reference and document templates.

© shepherdjerred, GPL-3.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (references) in packages/dotfiles/dot_agents/skills/deep-research of shepherdjerred/monorepo.

  • SKILL.md
  • references/academic-sources.md
  • references/evidence-standards.md
  • references/research-patterns.md

Open the folder on GitHubat commit 46ddf2f

Compare with similar skills

Deep Research next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Deep Research compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Deep Research this skillshepherdjerred/monorepo112—~4.7kAutomated safety check: NotesGPL-3.0
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Deep Research WorkflowTokenRhythm/opensquilla7.1k—~1.3kAutomated safety check: PassApache-2.0
Deep Researchsanjay3290/ai-skills43010 repos~683Automated safety check: NotesApache-2.0
Horizontal-Vertical Deep ResearchKKKKhazix/khazix-skills21k—~2.1kAutomated safety check: PassMIT
Academic Research PipelineImbad0202/academic-research-skills51k—~15kAutomated safety check: PassCustom licence

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Questions about Deep Research

What does Deep Research do?

This skill should be used when the user asks to "deep research", "research this topic", "investigate thoroughly", "do a deep dive on", "comprehensive research on", "find everything about", "survey…. Deep Research is an agent skill from shepherdjerred/monorepo. This skill should be used when the user asks to "deep research", "research this topic", "investigate thoroughly", "do a deep dive on", "comprehensive research on", "find everything about", "survey the landscape of", "compare approaches to", "write a report on", "gather information about", or wants multi-source investigation with synthesis and citations.

When should I use Deep Research?

Deep Research fits situations like: asks to deep research; research this topic; investigate thoroughly; do a deep dive on.

How do I install Deep Research in Claude Code?

Run `npx skills add shepherdjerred/monorepo --skill deep-research -a claude-code`. Or copy the skill folder (packages/dotfiles/dot_agents/skills/deep-research in shepherdjerred/monorepo) into .claude/skills/deep-research in your project. Claude Code loads it when a task matches its description.

How do I install Deep Research in Codex?

Run `npx skills add shepherdjerred/monorepo --skill deep-research -a codex`. Or copy the skill folder (packages/dotfiles/dot_agents/skills/deep-research in shepherdjerred/monorepo) into .agents/skills/deep-research in your project. Codex loads it when a task matches its description.

Can I use Deep Research in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add shepherdjerred/monorepo --skill deep-research -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/deep-research, .gemini/skills/deep-research, .github/skills/deep-research and .opencode/skills/deep-research in your project.

What does Deep Research need to run?

Going by SKILL.md and its folder, Deep Research needs the command-line tools its instructions call (gh). Its frontmatter pre-approves these tools: Read, Write, Bash, Glob, Grep, Agent, WebSearch, WebFetch, TaskCreate, TaskUpdate, TaskList.

Does Deep Research access the network?

SKILL.md contains no URLs. Its commands use gh, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Deep Research safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Deep Research use?

Deep Research is published under the GPL-3.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Deep Research use?

About 4.7k tokens (SKILL.md is roughly 19k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 4.4k tokens, read only when the agent opens those files.

What are the alternatives to Deep Research?

Skills that share tags, products or a category with Deep Research: Live Research (brightdata/skills, 264 stars), Deep Research Workflow (TokenRhythm/opensquilla, 7.1k stars), Deep Research (sanjay3290/ai-skills, 430 stars) and Horizontal-Vertical Deep Research (KKKKhazix/khazix-skills, 21k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Deep Research?

shepherdjerred (a GitHub user) maintains it in shepherdjerred/monorepo, which has 112 GitHub stars. The repository holds 63 skills in this directory. The repository was last updated on October 7, 2026.

Source: shepherdjerred/monorepo on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.