# Survey

> Literature survey assistant with four modes: /survey intent (capture student goals, expertise, and success criteria), /survey triage (landscape mapping via NotebookLM), /survey deepen (structured reading with craft and visualization extraction), /survey synthesize (cross-paper analysis for related work, area exams, gap identification). Also: /survey expand (corpus growth proposals). Use when stud…

- **Type:** Skill
- **Install:** `agentstack add skill-snl-ucsb-literature-survey-skill-literature-survey-skill`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [SNL-UCSB](https://agentstack.voostack.com/s/snl-ucsb)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [SNL-UCSB](https://github.com/SNL-UCSB)
- **Source:** https://github.com/SNL-UCSB/literature-survey-skill

## Install

```sh
agentstack add skill-snl-ucsb-literature-survey-skill-literature-survey-skill
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# Literature Survey Skill — Intent → Triage → Deepen → Synthesize

This skill helps PhD students build a deep, synthesized corpus of research insights through a structured, cognitively-aware workflow. It combines Kahneman's dual-process theory, Keshav's three-pass reading method, first-principles analysis, and NotebookLM as a backend query engine.

**Architecture:** All student work lives locally (Obsidian/filesystem). NotebookLM is a query backend only — papers go in, grounded answers come out.

**Modes:**
- `/survey intent` — Capture what you're trying to learn and where you're starting from
- `/survey triage` — Map the landscape, prioritize reading depth
- `/survey deepen` — Structured reading with craft and visualization extraction
- `/survey synthesize` — Cross-paper analysis for deliverables
- `/survey expand` — Structured corpus growth proposals

If the student invokes `/survey` without a mode, ask which mode they want. If they seem unsure or are starting a new survey, begin with intent.

**Prerequisites:** NotebookLM MCP CLI must be configured. See `reference/notebooklm_tools.md` for setup.

---

## Mode 1: Intent — "What are you trying to learn?"

**Purpose:** Capture the student's survey goals, expertise level, and success criteria BEFORE any paper is read. This shapes all subsequent modes.

**Do NOT create any NotebookLM notebooks in this mode. Do NOT ingest any papers. The goal is purely clarifying intent.**

### Step 1: Identify the survey archetype

Ask the student which best describes their situation:

> Before we look at any papers, I need to understand what kind of survey this is. Which best describes you?
>
> **A. Explorer** — "I'm entering a new area and need to understand the landscape."
> **B. Investigator** — "I have specific questions I need answered from the literature."
> **C. Validator** — "I think I've found a gap/idea and want to confirm it's novel."
> **D. Examiner** — "I need to demonstrate comprehensive mastery for an exam or survey paper."

Each archetype has different defaults:

| Archetype | Triage scope | Deepen targets | Pass 3 count | Synthesize output |
|-----------|-------------|----------------|-------------|-------------------|
| Explorer | 50-100+ papers | 8-15 at Pass 2 | 2-3 | Landscape overview |
| Investigator | 10-25 papers | 5-10 at Pass 2 | 3-5 | Technique comparison |
| Validator | 15-30 papers | 3-5 closest at Pass 2+3 | 3-5 | Positioning argument |
| Examiner | 80-150+ papers | 15-25 at Pass 2 | 5-8 | Full narrative survey |

### Step 2: Assess expertise level

Ask calibration questions for the chosen topic:

> 1. **Vocabulary check:** Can you name 3 key technical terms in this area and define each in one sentence?
> 2. **Landmark papers:** Can you name any papers, authors, or research groups you associate with this area?
> 3. **Current mental model:** In 2-3 sentences, what's your current understanding of the main problem and approaches?
> 4. **Known unknowns:** What specific questions do you hope the literature will answer?

**Cognitive purpose:** These questions create a baseline for later comparison. After triage, the student will see how their mental model changed — making System 1's invisible anchoring effects visible.

### Step 3: Define success criteria

> 1. **Deliverable:** Related work section? Area exam presentation? Gap analysis? Idea validation?
> 2. **Scope:** Approximately how many papers do you expect to cover?
> 3. **Timeline:** When do you need the output?
> 4. **Time budget:** How many hours per week can you invest?

### Step 4: Check for advisor input

> Has your advisor or collaborator recommended specific papers or threads to explore? Any "must-read" papers with suggested reading depth?

### Step 5: Generate the intent profile

Write a `survey_intent.md` file using the template from `templates/survey_intent_template.md`. Save it to the student's survey directory:

```
literature-survey/surveys//survey_intent.md
```

Also create the directory structure:
```
surveys//
├── survey_intent.md
├── pdfs/
├── papers/
│   └── figures/
├── synthesis/
├── corpus_log.md
├── backlog.md
└── nlm_config.md
```

Tell the student: "Your survey intent is captured. Run `/survey triage` when you're ready to start mapping the landscape."

---

## Mode 2: Triage — "What's in this pile?"

**Purpose:** Rapid Pass 1 over a corpus of papers using NotebookLM. Map the landscape and decide where to invest deeper reading. The archetype from intent mode shapes scope and clustering.

### Step 1: Set up NotebookLM backend

Read the student's `survey_intent.md` and `nlm_config.md`. If no notebook exists yet:

```
notebook_create(title="survey-")
chat_configure(notebook_id=, goal="custom",
    custom_prompt="You are a research corpus query engine for a PhD student
        surveying [topic]. The student is an [archetype] with goals: [from intent].
        Always cite specific papers and quote relevant passages.",
    response_length="longer")
tag(action="add", notebook_id=, tags=["survey", "", ""])
```

Save the notebook_id to `nlm_config.md`.

### Step 2: Seed the corpus

Ask the student for their initial papers (PDFs, URLs, BibTeX). For each paper:

1. **Acquire the PDF locally** using the priority chain:
   - Student already has the file → copy to `pdfs/YYYY_author_shorttitle.pdf`
   - Open-access URL (Arxiv, university repo) → download via `wget`
   - Semantic Scholar API → query for open-access PDF URL
   - Prompt the student for upload or URL

2. **Ingest into NotebookLM** (async for large batches):
   ```
   source_add(notebook_id=, source_type="file", file=, wait=False)
   ```

3. **Log in `corpus_log.md`** with status "ingesting".

While papers load (3-5 min for 15+ papers), use the time productively — refine intent, discuss the topic, let the student start reading local PDFs if they want.

### Step 3: Generate Pass 1 summaries

Once sources are ready, for each paper query NLM:

```
notebook_query(notebook_id=,
    query="For [title] by [author]: provide CATEGORY (measurement/systems-building/theory/survey),
    PROBLEM (one sentence), CONTRIBUTION (exact quote of main claim), EVALUATION (system/dataset/testbed),
    KEY REFERENCES (3-5 most cited references), RELEVANCE to survey goals (high/medium/low)")
```

Write each response to a local file: `papers/YYYY_author_shorttitle.md` with a `[Pass 1]` header.

### Step 4: Generate landscape map

Query NLM for a cross-corpus landscape:

```
notebook_query(notebook_id=,
    query="Group all papers by PROBLEM addressed (3-5 major threads). For each thread, list papers
    and methodology. Identify chronological patterns. WYSIATI CHECK: What problem areas or
    methodologies are absent? What would a skeptical reviewer say is missing?")
```

Write to `survey_triage.md`.

### Step 5: WYSIATI check and intent comparison

**This is a System 2 checkpoint.** Show the student their initial mental model (from `survey_intent.md`) alongside the landscape map:

> "Here's what you thought the area looked like when you started. Here's what the corpus actually shows. What's different? What surprised you?"

### Step 6: Prioritize reading depth

Help the student categorize each paper against their time budget:
- **Pass 1 only** — background knowledge
- **Pass 2 recommended** — relevant to core thread
- **Pass 3 required** — foundational, must deeply understand

Update `survey_triage.md` with the prioritized reading list.

### Step 7: Surface expansion candidates

```
notebook_query(notebook_id=,
    query="Which references appear in 3+ papers but are NOT sources in this notebook?
    For each, explain why it might be important to add.")
```

Append candidates to `corpus_log.md` with reason=FOUNDATIONAL. Present to student for Add/Bookmark/Skip decision (see `/survey expand` protocol).

---

## Mode 3: Deepen — "What does this paper really say?"

**Purpose:** Structured Pass 2 and Pass 3 reading for individual papers. Forces System 2 engagement, captures insights locally, uses NLM for grounded extraction and calibration.

### Step 1: Select a paper

Ask which paper from the triage reading list. Read their existing paper note if one exists.

### Step 2: Pass 2 protocol

Run four NLM queries and write results to the local paper note:

**a. Claim extraction:**
```
notebook_query: "For [title]: Quote the 3 most important claims with section/page references."
```

**b. Evidence audit:**
```
notebook_query: "For [title]: For each claim, what evidence is provided? Rate as strong/moderate/weak."
```

**c. Methodology probe:**
```
notebook_query: "For [title]: Describe evaluation setup. What explicit and implicit assumptions?
What would break if workload/scale/topology changed?"
```

**d. Dependency extraction:**
```
notebook_query: "For [title]: What results from other papers does this depend on?
Which dependencies might not hold in other contexts?"
```

Write all responses to the paper's local note as a `[Pass 2]` section.

### Step 3: Calibration check (System 2 prosthetic)

After the student has read the paper themselves, compare their understanding with NLM's grounded extraction:

1. Ask the student: "In one sentence, what is this paper's main contribution?"
2. Query NLM: "Quote the exact main contribution claim from the abstract or introduction."
3. Show both side by side in a `[Calibration]` section. Highlight the specificity gap.

### Step 4: First-principles decomposition

```
notebook_query: "For [title], analyze along four dimensions:
    STATE: What state does the system manage?
    TIME: What timescales matter?
    COORDINATION: How do components coordinate?
    INTERFACE: What are the boundaries between components?
    Quote specific passages as evidence."
```

Write to the paper note as `[First-Principles]` section.

### Step 5: Pass 3 — Virtual re-implementation (foundational papers only)

Ask the student:
- "If you had to build this system from scratch with the same goals, what would your design look like?"
- "What assumptions are never explicitly stated?"
- "Write three critical questions a skeptical PC member would ask."

### Step 6: Writing craft extraction (Pass 3+ papers the student admires)

Read `reference/writing_craft_moves.md` for the full framework. Query NLM and guide the student through:

**a. Introduction anatomy — the six-move formula:**
```
notebook_query: "For [title], analyze the INTRODUCTION:
    MOVE 1 (Stakes): How does it open? Specific actors/applications/dollar amounts?
    MOVE 2 (Problem Gap): Structural or quantitative? Numbered limitations?
    MOVE 3 (Key Abstraction): Does it coin a memorable, citable term?
    MOVE 4 (Design Intuition): One-paragraph mental model? Overview figure?
    MOVE 5 (Contributions): Claims with evidence, or process descriptions? Numbered?
    MOVE 6 (Results Preview): Concrete headline numbers?
    Quote specific passages."
```

**b. Evaluation architecture:**
```
notebook_query: "For [title], analyze the EVALUATION:
    CLAIM-EVIDENCE MAP: List every intro claim → evaluation subsection → figure/table.
    SETUP: Compressed or technical report?
    DEEP DIVE: Results disaggregated by meaningful dimensions?
    TAKEAWAYS: Explicit takeaway after every experiment cluster?
    ABLATION: Shows each component contributes?"
```

**c. Design section craft:**
```
notebook_query: "For [title], analyze the DESIGN:
    Opens with abstraction or implementation?
    'Why' move — justification via negative result?
    Named components? Key configurable 'knob'?"
```

**d. Related work positioning:**
Ask the student: How many categories? Structural or quantitative limitations? Explicit positioning sentence?

**e. Peak observation:** What is the single most memorable insight — the thing you'd cite 10 years from now?

**f. Lessons for my writing:** The student captures what they want to adopt for their own papers.

Write to paper note as `[Craft]` section. Also append key lessons to `synthesis/writing_craft_corpus.md`.

### Step 7: Visualization extraction (all Pass 2+ papers)

Read `reference/viz_analysis_guide.md`. Query NLM and guide the student through:

**a. Figure inventory:**
```
notebook_query: "For [title]: List every figure and table. For each: caption, role
    (overview/comparison/deep-dive/ablation/case-study), claim it supports, encoding used.
    Which is the headline figure?"
```

**b. Visual argument analysis (2-3 key figures):**
```
notebook_query: "For [title], for the 2-3 most important figures:
    What claim does each support? Describe encoding choices (axes, scale, color, faceting).
    WHY those choices — does the encoding serve the argument?
    What does the figure NOT show that would be useful?"
```

**c. Figure extraction from local PDF:** If the student wants key figures extracted, use PyMuPDF on the local PDF in `pdfs/`, or prompt for manual screenshots. Save to `papers/figures/`.

Write to paper note as `[Visualization]` section. Append best-practice examples to `synthesis/viz_patterns.md`.

### Output

The local paper note (`papers/YYYY_author_shorttitle.md`) grows through the mode:
`[Pass 1]` → `[Pass 2]` → `[Calibration]` → `[First-Principles]` → `[Craft]` → `[Visualization]` → `[My Notes]`

---

## Mode 4: Synthesize — "What connects all of this?"

**Purpose:** Cross-paper synthesis for a specific deliverable. Uses NLM cross-corpus queries, writes all results locally.

### Step 1: Select synthesis goal

The student chooses (informed by their intent profile):
- **Related work section** — organized thematic narrative
- **Area exam presentation** — breadth + depth + frontier identification
- **Research gap identification** — systematic analysis of what's missing
- **New idea synthesis** — creative recombination of insights

### Step 2: Invariant matrix

```
notebook_query: "For every paper, build a comparison matrix:
    State management | Primary timescale | Coordination model | Interface design.
    Highlight where papers make fundamentally different choices."
```

Write to `synthesis/invariant_matrix.md`. Student annotates locally.

### Step 3: Dependency graph

```
notebook_query: "Identify cases where one paper's design DEPENDS ON an assumption
    another paper challenges. Quote the assumption and the challenging evidence."
```

Write to `synthesis/dependency_graph.md`.

### Step 4: Gap identification

```
notebook_query: "If [bandwidth/latency/scale] changed by 10x, which solutions still work?
    Which break? What new problems emerge that no paper addresses?"
```

Also ask: "Looking at the invariant matrix — are there combinations no paper explores? Missing methodologies?"

Write to `synthesis/gap_analysis.md`.

### Step 5: Cross-survey synthesis (if multiple survey notebooks exist)

```
cross_notebook_query(query="How do approaches to [dimension] differ between
    [survey A] and [survey B]?", tags=["survey"])
```

### Step 6: Narrative construction

Based on the synthesis goal, generate the deliverable draft:

- **Related work:** Thematic threads with intellectual arcs, not chronological lists. Each thread: "These papers address X by solving Y, but none handle Z — our contribution."
- **Area exam:** Breadth across subfield + depth on 2-3 foundational papers + frontier identification + student's own position.
- **Gap analysis:** Constraint-change analysis → candidate problems with first-principles justification.
- **New ideas:** "Paper A solves X under constraint C1. C1 is changing because of [trend]. Under C2, Paper A breaks because [dependency]. New approach needs [design principle]."

### Step 7: WYSIATI final audit

Before finalizing:
> "What perspectives are missing? What would someone from [adjacent field] say? Am I over-indexing on [one group/venue/methodology]?"

### Step 8: Generate artifacts from NLM Studio (optional)

```
studio_create(notebook_id=, artifact_type="report")  # or "slide_deck"
download_artifact(notebook_id=, artifact_type="report", output_path="synthesis/nlm_report.md")
```

These are starting points — the

…

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [SNL-UCSB](https://github.com/SNL-UCSB)
- **Source:** [SNL-UCSB/literature-survey-skill](https://github.com/SNL-UCSB/literature-survey-skill)
- **License:** MIT

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** yes
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-snl-ucsb-literature-survey-skill-literature-survey-skill
- Seller: https://agentstack.voostack.com/s/snl-ucsb
- Browse the marketplace: https://agentstack.voostack.com/browse

---
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