Deep Research logo

Deep Research

Organization
TenureAI
deep-research

Deep evidence-first research with broad discovery, verification, and traceable citations. Prefer invoking via research-workflow. TRIGGER when (MANDATORY — you MUST invoke this skill, no exceptions): user message contains ANY of these keywords or synonyms — 调研/研究/对比/综述/文献/证据/机制/根因/为什么/可行性/路线图/分析/探索, or research/investigate/compare/survey/literature/evidence/mechanism/root-cause/why/feasibility/roadmap/analyze/explore — or asks to verify claims, analyze tradeoffs, scope a new topic, or conduct literature review. Also use this skill as the default gateway for external search. Skipping when keywords match is a routing violation. DO NOT TRIGGER when: user asks for paper-writing output (use paper-writing), experiment launch (use experiment-execution), or plan-only without evidence (use research-plan).

Overview

PublisherTenureAI
RepositoryPhD-Zero
Skill namedeep-research
Stars
53
Forks
4
Bundled files
4
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by TenureAI on GitHub. Read the source before you install it.

Installation

Install the Deep Research AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/TenureAI/PhD-Zero/tree/main/.agents/skills/deep-research
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/TenureAI/PhD-Zero.git /tmp/PhD-Zero
mkdir -p .claude/skills
cp -r /tmp/PhD-Zero/.agents/skills/deep-research .claude/skills/deep-research
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Deep Research in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Deep Research on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Deep Research is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Deep Research

Mission

Produce a deeply researched, evidence-grounded answer with clear provenance and actionable conclusions, and act as the default gateway for external search in research runs.

Search Routing Gate (Mandatory)

All external search during non-trivial research runs must enter through deep-research.

Rules:

  1. do not bypass deep-research with ad hoc direct search when fresh outside evidence is needed
  2. deep-research may choose a lighter or deeper execution depth internally, but it may not silently skip actual search
  3. every deep-research run must perform real WebSearch calls and keep an auditable query trail
  4. if search is skipped because existing evidence is already fresh enough, emit dr_skip_reason with explicit date windows and source counts

Research Type Selection

Choose a primary research_type early:

  1. idea-exploration
  2. debug-investigation
  3. design-decision
  4. implementation-strategy
  5. conflict-resolution

If templates do not fit exactly, adapt structure freely but keep depth, verification, and citations.

Intake Checkpoint Gate (Mandatory Before Search)

Before selecting depth or running any WebSearch queries:

  1. confirm intake_checkpoint_complete=YES
  2. intake must at least define: objective/scope, constraints, and expected deliverable format
  3. route missing-information requests through human-checkpoint
  4. in moderate or detailed, prefer built-in user-question tool (request_user_input)
  5. if built-in tool is unavailable, degrade to concise plain-text questions
  6. if intake is incomplete, remain in clarification phase and do not run search, decomposition, or synthesis

Frontier-First Scout (Mandatory)

Every deep-research run must begin with a frontier-first scout before final depth selection.

Scout requirements:

  1. run at least 6-10 queries total
  2. cover at least:
    • bleeding-edge topic queries
    • frontier topic queries
    • one verification query family
    • one counter-evidence or criticism query family
  3. capture representative freshness, source quality, and contradiction density
  4. use scout evidence to choose final depth

Scout rules:

  1. scout is mandatory even when a lighter depth is later selected
  2. scout counts toward total query budget
  3. scout may justify upgrading to deep or downgrading to light
  4. scout may not justify "no search"

Default Workflow

Iterate until evidence quality is sufficient:

  1. confirm intake checkpoint is complete
  2. restate objective and success criteria
  3. set explicit As of: YYYY-MM-DD
  4. run the mandatory frontier-first scout
  5. select execution depth
  6. run staged time-window search with Codex WebSearch
  7. extract claim-level evidence
  8. build key-work cards when the topic is paper-centric
  9. verify high-impact claims independently
  10. run contradiction/counter-evidence checks
  11. synthesize and produce final report

When the topic has implementation, benchmark, reproduction, or planning implications, also apply references/codebase-and-data-research-rules.md.

Re-entry Policy (Mid-Run)

When called during an ongoing run:

  1. treat invocation as valid and do not require starting a new run by default
  2. recompute objective delta versus current stage plan
  3. if objective changed materially, reset research focus and run fresh query batches
  4. if objective is similar, perform incremental deep research using existing evidence as baseline
  5. if skipped due to sufficient evidence freshness, emit dr_skip_reason with explicit date windows and source counts

Scoping-to-Planning Handoff Policy

When deep research is used for open-ended scoping (idea-exploration), hand off findings to research-plan as the required default next step. Skip only if the user explicitly opts out.

Handoff expectations:

  1. preserve core hypotheses, constraints, and evidence-backed tradeoffs
  2. identify recommended direction and at least one fallback direction
  3. convert conclusions into executable planning inputs (experiments, implementation prerequisites, data/workload requirements, risks)

Completion Gate (Mandatory)

Do not output final conclusions until all gate checks pass.

Before synthesis, print:

  1. intake_checkpoint_complete=YES|NO
  2. intake_channel=request_user_input|plain-text-fallback|none
  3. search_entry=deep-research
  4. frontier_first_scout=YES|NO
  5. selected_depth=light|default-auditable|deep
  6. depth_reason=
  7. dr_degrade_reason=
  8. total_queries=
  9. scout_queries=
  10. bleeding_edge_queries=
  11. frontier_queries=
  12. recent_queries=
  13. mid_term_queries=
  14. classic_queries=
  15. degrade_used=YES|NO
  16. gate_pass=YES|NO

If degrade_used=YES, also print:

  1. degrade_from=
  2. degrade_to=
  3. degrade_gap=
  4. degrade_queries_run=
  5. degrade_reason=

If gate_pass=NO, continue searching and do not finalize.

Search Depth Rules

Support three execution depths:

  1. light
    • only for narrow, low-ambiguity verification after scout
    • total: 12-24 queries
    • stage minimums: bleeding-edge >= 3, frontier >= 3, recent >= 2, mid-term >= 1, classic >= 1
  2. default-auditable
    • default for bounded but non-trivial research questions
    • total: target 50-80 queries
    • stage minimums: bleeding-edge >= 12, frontier >= 10, recent >= 10, mid-term >= 8, classic >= 6
  3. deep
    • use for broad or open-ended exploration, roadmap design, deep comparisons, or high-uncertainty topics
    • total: 100-140 queries
    • stage minimums: bleeding-edge >= 28, frontier >= 22, recent >= 20, mid-term >= 16, classic >= 10

Selection rules:

  1. user override wins if explicitly specified
  2. if the user does not specify, default to default-auditable
  3. select deep when scope is broad, open-ended, contradiction-heavy, or asks for landscape plus recipe or mechanism analysis
  4. light is not a default mode
  5. light is allowed only when scout confirms the task is narrow, directly verifiable, and low-ambiguity
  6. if ambiguous, do not choose light
  7. if the prompt mentions 2 or more research-intent terms, do not choose light unless the user explicitly forces it

Depth Sanity Check (Mandatory Before Full Search)

Print this mini-check immediately after selecting depth:

  1. depth_candidate=
  2. light_disqualifiers_hit=
  3. open_ended_exploration=YES|NO
  4. paper_centric=YES|NO
  5. depth_sanity_pass=YES|NO

Rules:

  1. if light_disqualifiers_hit is non-empty and depth_candidate=light, set depth_sanity_pass=NO and reselect before more search
  2. if open_ended_exploration=YES and user did not explicitly force light, do not use light
  3. if paper_centric=YES and the user asks for mechanisms/recipes/comparisons, do not use light

Search Execution Policy (Codex Native)

  1. use Codex WebSearch directly in-session; do not require external browser interaction
  2. do not depend on external search APIs for baseline operation
  3. treat date text in query strings as recall hints only; do not rely on parser-specific after:/before: behavior for final stage assignment
  4. use date-window targeting during retrieval, then assign stage by published-date validation
  5. compute days_from_as_of for each source and map to exactly one stage using the stage boundary rules below
  6. if source date is unknown, keep with uncertainty label and lower priority
  7. do not claim deep-research completion without actual WebSearch calls and auditable query logs
  8. prioritize bleeding-edge, then frontier, then recent whenever the user cares about the latest or fastest-moving evidence

Staged Time Windows

Use five mandatory evidence stages and record source counts for each. Define days_from_as_of = as_of_date - published_date (integer days). Stages are mutually exclusive:

  1. bleeding-edge (0-90 days): 0 <= days_from_as_of <= 90
  2. frontier (91-180 days): 91 <= days_from_as_of <= 180
  3. recent (181-365 days): 181 <= days_from_as_of <= 365
  4. mid-term (366-730 days): 366 <= days_from_as_of <= 730
  5. classic (>730 days): days_from_as_of > 730

Freshness floor:

  1. bleeding-edge + frontier >= 35% for normal runs
  2. bleeding-edge + frontier + recent >= 60% for all finalized runs

Stage Search Sequence

Per stage, run at least these query families:

  1. canonical topic terms
  2. synonym or alias expansion
  3. counter-evidence and criticism
  4. verification queries for high-impact claims

Use dynamic query-family expansion:

  1. build seed terms from user question terms and canonical topic terms
  2. expand with aliases discovered from high-confidence retrieved sources
  3. do not hard-code universal mandatory method keywords for all topics

Minimum rounds by depth:

  1. light: bleeding-edge/frontier/recent >= 1, mid-term/classic >= 1
  2. default-auditable: bleeding-edge/frontier/recent >= 3, mid-term >= 2, classic >= 1
  3. deep: bleeding-edge/frontier/recent >= 4, mid-term >= 3, classic >= 2

Stage Deficit Degrade Policy

If a stage minimum is not met, allow controlled degradation only after an exhaustion pass.

Exhaustion pass minimums per deficit stage:

  1. light: at least 6 additional stage-targeted queries
  2. default-auditable: at least 18 additional stage-targeted queries
  3. deep: at least 32 additional stage-targeted queries

Degrade rules:

  1. only adjacent fallback is allowed: bleeding-edge -> frontier, frontier -> recent, recent -> mid-term
  2. at most one degrade hop per stage
  3. borrowed amount cannot exceed 50% of the deficit stage minimum
  4. even after degradation, keep bleeding-edge + frontier >= 30% and bleeding-edge + frontier + recent >= 60%

Memory and Search Policy

  1. global memory bootstrap is mandatory for non-trivial runs
  2. before heavy search batches, use the current memory snapshot or retrieve relevant insight/procedure memory when it can reduce redundant search or contradiction cost
  3. when scout or full search uncovers a repeated issue already covered by memory, incorporate that memory explicitly rather than rediscovering it silently
  4. use search directly and aggressively when the topic is new, urgent, or time-sensitive
  5. if a lighter depth is chosen, report why default-auditable was not needed

Type-Aware Reporting Requirements

Always include:

  1. objective and scope
  2. evidence-based conclusions
  3. contradictions and uncertainties
  4. anchored citations
  5. research trail summary
  6. saved report path

Type-specific emphasis:

  1. debug-investigation
    • include error signature, reproduction context, fix candidates, validation outcomes
  2. design-decision
    • compare alternatives, constraints, and cost/risk tradeoffs
  3. implementation-strategy
    • include staged rollout options and operational prerequisites
  4. conflict-resolution
    • focus on disputed claims, source reliability tiers, and resolution rationale
  5. idea-exploration
    • include landscape, mechanisms, opportunities, and boundaries

Representative Works Deep-Dive Policy

Trigger this policy when user asks for any of:

  1. important works, representative papers, state of the art, or research landscape
  2. method comparison across papers
  3. roadmap or recipe requests grounded in prior work

When triggered, include a dedicated Key Works Deep Dive section and meet minimum coverage:

  1. light: 3-5 key works
  2. default-auditable: 6-10 key works
  3. deep: 10-15 key works

For each key work, provide:

  1. problem addressed
  2. method or training objective
  3. setup and data regime
  4. headline results and where they hold
  5. limitations or failure boundary
  6. why the work matters to the user's question
  7. primary citation

Evidence and Citation Policy

  1. cite in text as [[S#]](#ref-s#)
  2. keep references anchored with published and accessed dates
  3. distinguish fact, inference, and uncertainty
  4. prefer canonical primary sources
  5. do not rely on weak secondary sources for core conclusions

Quality Gate

Finalize only when:

  1. major claims are verified or clearly marked uncertain
  2. contradictions are addressed or left as explicit open issues
  3. citations are complete and internally consistent
  4. report depth matches task type
  5. language matches user language
  6. if paper-centric policy is triggered, key-work count meets selected depth minimum
  7. every finalized search run recorded scout plus full-query totals
  8. if degradation is used, exhaustion minimums and freshness floor are explicitly satisfied and reported
  9. the selected depth passed Depth Sanity Check

Persistence Policy

  1. always output full report in chat
  2. save exactly one final report file per deep-research run
  3. default save path under run logs:
    • <codex-cwd>/logs/runs/<run_id>/reports/deep-research-<slug>.md

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Deep Research AI skill do?

Deep evidence-first research with broad discovery, verification, and traceable citations. Prefer invoking via research-workflow. TRIGGER when (MANDATORY — you MUST invoke this skill, no exceptions): user message contains ANY of these keywords or synonyms — 调研/研究/对比/综述/文献/证据/机制/根因/为什么/可行性/路线图/分析/探索, or research/investigate/compare/survey/literature/evidence/mechanism/root-cause/why/feasibility/roadmap/analyze/explore — or asks to verify claims, analyze tradeoffs, scope a new topic, or conduct literature review. Also use this skill as the default gateway for external search. Skipping when key...

Why use Deep Research on TypingMind?

Because you install it once and use it with any model. Deep Research is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Deep Research in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/TenureAI/PhD-Zero/tree/main/.agents/skills/deep-research. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Deep Research?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Deep Research?

As many as you like. As long as a model supports skills, you can use Deep Research with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Deep Research AI skill free?

Yes. It is published on GitHub by TenureAI under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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