survey-generation

Writing Community

Repo stars 90
Author repo agent-research-skills

Interpretation is structured for decision-making; original keeps the upstream SKILL.md unchanged.

Decide Fit First

Core job: Generate complete academic survey papers using multi-LLM parallel outline generation, RAG-based subsection writing, citation val…
Best fit: Use it when the task has reusable inputs, steps, and validation criteria rather than a one-off answer.
Avoid forcing it: If the source lacks commands, platform support, or external-service evidence, keep those fields unknown instead of guessing.

Design Intent

Structure: The skill is organized around “Input”, “Scripts”, “Literature search”, “References”, showing how the author expects the agent to judge fit, collect context, and produce verifiable output.
Trigger evidence: Prioritize the author’s wording around when to use it, what context to collect, and what output shape to produce.
Evidence boundary: Author text states facts, repository files prove commands and paths, and Fluxly only adds fit, limits, and usage judgment.

How To Use It

Inputs: Provide target material, scope, expected result, forbidden changes, and validation method.
Invocation: Name survey-generation directly; if the source includes slash commands, start with the command and then add task context.
Validation: Start small and check whether the result follows “Input / Scripts / Literature search” before expanding.

Boundaries And Review

Dependencies: It usually needs no extra API key, so start with a small validation task.
Permissions: Declared permissions include read / write; ask the agent to state file, command, and rollback boundaries before acting.
Quality bar: A useful result names the deliverable, evidence, and next action. Generic prose means the task needs tighter context.

Survey Generation

Generate complete academic survey papers with structured outline, RAG-based writing, and citation validation.

Input

$0 — Survey topic or research area

Scripts

Literature search

python ~/.claude/skills/deep-research/scripts/search_semantic_scholar.py \
  --query "relevant search query" --max-results 50

References

Survey prompts (outline, writing, citation, coherence): ~/.claude/skills/survey-generation/references/survey-prompts.md

Workflow (from AutoSurvey)

Step 1: Collect Papers

Search Semantic Scholar / arXiv for papers on the topic
Collect 50-200 relevant papers with titles and abstracts
Filter by relevance and citation count

Step 2: Generate Outline (Multi-LLM Parallel)

Generate N rough outlines independently (parallel)
Merge outlines into a single comprehensive outline
Expand each section into subsections
Edit final outline to remove redundancies

Step 3: Write Subsections (RAG-Based)

For each subsection:

Retrieve relevant papers for the subsection topic
Generate content with inline citations [paper_title]
Enforce minimum word count per subsection
Only cite papers from the provided list

Step 4: Validate Citations

For each subsection:

Check that cited paper titles are correct
Verify citations support the claims made
Remove or correct unsupported citations
Use NLI (Natural Language Inference) for claim-source faithfulness

Step 5: Enhance Local Coherence

For each subsection:

Read previous and following subsections
Refine transitions and flow
Preserve core content and citations
Ensure smooth reading experience

Step 6: Convert Citations to BibTeX

Replace [paper_title] with \cite{key}
Generate BibTeX entries for all cited papers
Validate all citation keys exist in .bib file

Output Structure

survey/
├── main.tex          # Complete survey paper
├── references.bib    # All citations
├── outline.json      # Survey outline
└── sections/         # Individual section files

Rules

Only cite papers from the collected paper list — never hallucinate citations
Each subsection must meet minimum word count
No duplicate subsections across sections
Citation validation is mandatory before final output
Local coherence enhancement must preserve all citations
The survey should be comprehensive and logically organized

Upstream: deep-research, literature-search, literature-review
See also: related-work-writing

Fluxly profile Author and license come from source; runtime, permissions, and network are Fluxly detections or estimates

Fluxly category: Writing
Author-declared agents: No explicit declaration found; this is not inferred or tested compatibility
Static check: 88 / 100 · heuristic scan, not runtime safety proof
Author / version / license: @lingzhi227 · no license declared
Fluxly token estimate: Lean
Fluxly setup estimate: Plug-and-play
External API key: No requirement detected
Detected OS requirements: Unspecified
Runtime requirements: Python
Detected file/system behavior: Read-only
Write / modify
Detected network behavior: Local-only
Install commands: None (reference only)

Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.

Heads up: 未限定 allowed-tools，默认拥有全部工具权限。

Output preview survey-generation.preview

The current SKILL.md does not define a fixed output example.

SKILL.md SKILL.md

# Survey Generation

Generate complete academic survey papers with structured outline, RAG-based writing, and citation validation.

## Input

- `$0` — Survey topic or research area

## Scripts

### Literature search
```bash
python ~/.claude/skills/deep-research/scripts/search_semantic_scholar.py \
  --query "relevant search query" --max-results 50
```

## References

- Survey prompts (outline, writing, citation, coherence): `~/.claude/skills/survey-generation/references/survey-prompts.md`

## Workflow (from AutoSurvey)

### Step 1: Collect Papers
1. Search Semantic Scholar / arXiv for papers on the topic
2. Collect 50-200 relevant papers with titles and abstracts
3. Filter by relevance and citation count

### Step 2: Generate Outline (Multi-LLM Parallel)
1. Generate N rough outlines independently (parallel)
2. Merge outlines into a single comprehensive outline
3. Expand each section into subsections
4. Edit final outline to remove redundancies

### Step 3: Write Subsections (RAG-Based)
For each subsection:
1. Retrieve relevant papers for the subsection topic
2. Generate content with inline citations `[paper_title]`
3. Enforce minimum word count per subsection
4. Only cite papers from the provided list

### Step 4: Validate Citations
For each subsection:
1. Check that cited paper titles are correct
2. Verify citations support the claims made
3. Remove or correct unsupported citations
4. Use NLI (Natural Language Inference) for claim-source faithfulness

### Step 5: Enhance Local Coherence
For each subsection:
1. Read previous and following subsections
2. Refine transitions and flow
3. Preserve core content and citations
4. Ensure smooth reading experience

### Step 6: Convert Citations to BibTeX
1. Replace `[paper_title]` with `\cite{key}`

…

Evidence boundary and execution chain

Author text anchors workflow facts; Fluxly only indexes current sections, terms, files, and commands.

sections -> Input → Scripts → Literature search → References → Workflow (from AutoSurvey) → Step 1: Collect Papers
terms    -> Generate complete academic survey papers with structured outline, RAG-based writing, and citation validation.
files/cmd -> ~/.claude/skills/survey-generation/references/survey-prompts.md · [papertitle] · \cite{key} · .claude/skills/deep-research/scripts/searchsemanticscholar.py · .claude/skills/survey-generation/references/survey-prompts.md · outline.js · ../deep-research · ../literature-search
body sha256 -> 66a169fa69cc

Decide Fit First

Design Intent

How To Use It

Boundaries And Review

Survey Generation

Input

Scripts

Literature search

References

Workflow (from AutoSurvey)

Step 1: Collect Papers

Step 2: Generate Outline (Multi-LLM Parallel)

Step 3: Write Subsections (RAG-Based)

Step 4: Validate Citations

Step 5: Enhance Local Coherence

Step 6: Convert Citations to BibTeX

Output Structure

Rules

Related Skills

Discussion