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Survey SOP

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wentorai
Survey SOP

Standard operating procedure for research survey and project execution. Covers deep reading methodology, information synthesis, paper evaluation, citation integrity, cross-module coordination (Library↔Tasks↔Workspace↔Monitor), and research project lifecycle (Phase 2-4).

Overview

Publisherwentorai
RepositoryResearch-Claw
Skill nameSurvey SOP
Stars
859
Forks
117
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Survey SOP 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/wentorai/Research-Claw/tree/main/skills/survey-sop
  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/wentorai/Research-Claw.git /tmp/Research-Claw
mkdir -p .claude/skills
cp -r /tmp/Research-Claw/skills/survey-sop .claude/skills/wentorai-survey-sop
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Survey SOP 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 Survey SOP 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 Survey SOP 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.

Scope

Covers Phase 2-4 of the research workflow (after papers are found). Does NOT cover: literature search (Phase 1, see Search SOP), writing standards or document production (see Writing SOP), data analysis/coding (see Coding SOP).

Phase 2 -- Deep Reading

Systematic reading extracts structured knowledge from each paper.

Reading Workflow

  1. Select papers from library with library_search. Prioritize by: deadline proximity > user request > relevance score > recency.
  2. Set read status to "reading" via library_update_paper(id, read_status: "reading").
  3. Read systematically -- for each paper, extract:
    • Core findings: key results, quantitative outcomes, effect sizes
    • Methodology: study design, data sources, analytical approach, sample size
    • Limitations: stated limitations + unstated weaknesses you identify
    • Key citations: papers referenced that may warrant Phase 1 follow-up
  4. Save reading notes to workspace: workspace_save("outputs/notes/{paper-short-title}-notes.md", content)
  5. Update paper metadata: library_update_paper(id, read_status: "read", notes: "...")
  6. Flag gaps: if the paper cites important work not in your library, note it for a targeted Phase 1 search. Do not silently skip missing references.

Reading Depth Levels

  • Skim (triage, large batch): title, abstract, conclusion, figures
  • Focused (relevant to current question): methods + results + key citations
  • Deep (core project paper): full extraction, reproduce key calculations

Comparative Reading

When reading multiple papers on the same topic, build a comparison matrix (method, dataset, results, limitations). Note contradictions and consensus. Save via workspace_save("outputs/notes/comparison-{topic}.md", ...)

Phase 3 -- Analysis and Synthesis

Transform extracted knowledge into coherent research outputs.

Synthesis Workflow

  1. Organize findings by theme, not by paper. Group related results across multiple papers to identify patterns, contradictions, and gaps.
  2. Draft the synthesis following the user's preferred structure and citation style. Default to thematic organization with APA citations.
  3. Save drafts to workspace: workspace_save("outputs/drafts/{topic}-{type}.md", content) Types: literature-review, summary, analysis, report
  4. Generate bibliography: library_export_bibtex(paper_ids: [...]) or library_export_bibtex(paper_ids: [...], format: 'ris'). Save to: workspace_save("outputs/exports/bibliography-{project}.bib", bibtex)
  5. Link output to task if one exists (see Phase 4 Task-File Linking).

Synthesis Quality Gates

Before presenting a synthesis to the user:

  • Every claim has a citation traced to a paper in the library
  • Contradictory findings are explicitly noted, not silently omitted
  • Gaps in the literature are identified and flagged
  • Quantitative claims use exact numbers from the source papers

Phase 4 -- Task Management

Structure research work into trackable tasks with deadlines.

Task Lifecycle

task_create → task_link (papers/files) → task_note (progress)
  → task_complete (with summary)

Creating Tasks

task_create params: title (action statement), task_type ("human" | "agent" | "mixed"), priority ("urgent" | "high" | "medium" | "low"), deadline (ISO 8601).

Task-File Linking

When a task produces output: (1) workspace_save the file, (2) task_link_file(task_id, file_path), (3) task_complete(id, notes: "Output: ..."). For papers: task_link(task_id, paper_id).

Task Progress Tracking

Use task_note(task_id, note) for progress updates. Set status: "blocked" with blocker note when stuck. On completion, emit a progress_card.

Paper Evaluation Criteria

Evaluate five dimensions: Venue quality (reputable, peer reviewed?), Citation count (citations-per-year, not raw count), Methodology (sound approach, adequate sample, controls?), Reproducibility (sufficient detail?), Relevance (addresses user's question?).

Rate: high / medium / low. Only add high + medium to library.

Citation Integrity

Full citation rules (4 non-negotiable rules) are in the Writing SOP skill. Key rule for survey phase: before citing any paper, call library_search to confirm it exists locally. If not found, add it first (Cross-Module Rule 6).

Cross-Module Coordination

Six handoff rules keep data consistent across modules:

  1. monitor_report -> library: Present paper_card per finding. User selects which to add -> library_add_paper. Emit monitor_digest. Save digest to outputs/monitor/monitor-scan-{date}.md.
  2. monitor -> cron: After monitor_create, suggest cron scheduling. Each tick: monitor_get_context -> scan -> monitor_report -> monitor_note.
  3. library_add_paper + active project: Auto-call task_link(task_id, paper_id). Reversible, no confirmation needed.
  4. task_complete -> progress_card: Output progress_card with papers read, added, tasks completed, highlights.
  5. Phase transitions: After Phase 1 -> progress_card + suggest Phase 2. After Phase 2 -> summarize findings + suggest Phase 3.
  6. Cite -> verify: Before citing, library_search to confirm paper exists. If missing, library_add_paper first. Never cite unverified papers.

Workspace Integration

All research outputs MUST be persisted to workspace -- never only in chat.

Output File Naming

  • outputs/drafts/literature-review-{topic}.md -- literature reviews
  • outputs/notes/{paper-short-title}-notes.md -- reading notes
  • outputs/notes/comparison-{topic}.md -- comparison matrices
  • outputs/reports/analysis-{dataset}.md -- analysis reports
  • outputs/monitor/monitor-scan-{date}.md -- monitor digests
  • outputs/reports/weekly-report-{date}.md -- progress reports
  • outputs/exports/bibliography-{project}.bib -- BibTeX exports

Session Continuity

When resuming work, check: workspace_history (recent changes), workspace_list (current files), task_list (active tasks). Combine with MEMORY.md to reconstruct where the user left off.

RC Local Tools Reference

Key tool call chains for survey and project execution:

Deep Reading Chain

library_search -> library_update_paper(read_status:"reading")
  -> [read & extract] -> workspace_save(notes)
  -> library_update_paper(read_status:"read", notes)

Synthesis Chain

library_search(tags/collection) -> [organize by theme]
  -> workspace_save(draft) -> library_export_bibtex(paper_ids)
  -> workspace_save(bibliography)

Task Management Chain

task_create(title, deadline, priority) -> task_link(paper_id)
  -> task_note(progress) -> workspace_save(output)
  -> task_link_file(file_path) -> task_complete(notes)

Monitor Integration Chain

monitor_create -> cron(schedule) -> monitor_get_context
  -> [scan] -> monitor_report -> monitor_note
  -> library_add_paper (user-selected) -> task_link
  -> workspace_save(digest)

Related Research-Plugins Skills

For methodology and domain guidance beyond this SOP, browse these RP subcategories:

  • research/methodology/ -- research design, experimental design, grounded theory, mixed methods
  • research/deep-research/ -- systematic review (PRISMA), scoping review, meta-synthesis
  • research/paper-review/ -- paper summarization, peer review, quality assessment
  • research/funding/ -- grant writing (NSF/NIH), data sharing plans
  • research/automation/ -- end-to-end research pipelines, automated workflows
  • domains/ -- 16 discipline-specific subcategories (biomedical, cs, economics, law, humanities, etc.). Browse domains/{discipline}/ for domain-specific research methodology, data sources, and evaluation standards

Frequently asked questions

What does the Survey SOP AI skill do?

Standard operating procedure for research survey and project execution. Covers deep reading methodology, information synthesis, paper evaluation, citation integrity, cross-module coordination (Library↔Tasks↔Workspace↔Monitor), and research project lifecycle (Phase 2-4).

Why use Survey SOP on TypingMind?

Because you install it once and use it with any model. Survey SOP 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 Survey SOP in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/wentorai/Research-Claw/tree/main/skills/survey-sop. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Survey SOP?

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 Survey SOP?

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

Is the Survey SOP AI skill free?

It is published on GitHub by wentorai. Check the repository for licensing terms. 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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