Survey Paper Generator

Survey Generator Skill

Generate an academic-style survey paper as a single self-contained HTML file.

What this skill does

Given a topic and a public anchor resource, this skill:

1. Reads the anchor resource and extracts the landscape of relevant work.
2. Builds a structured research_bundle.json (title, taxonomy, sections, bibliography of real papers).
3. Calls Kimi K2.6 via the Fireworks chat completions API with the research bundle and a fixed style_spec.json.
4. Writes a single-file HTML artifact with inline SVG figures, an academic layout, numbered sections, and a References list.

The agent using this skill is responsible only for research curation. All prose, figures, and HTML are generated by Kimi K2.6 in one API call.

Inputs from the user

The user invokes this skill with at minimum:

• topic: a concise survey topic, for example "Agentic Engineering" or "Reasoning Models".
• source_url: a public anchor resource. Any curated list, canonical blog post, arXiv survey, GitHub awesome-list, or index page works. Suggested starting points: DAIR.AI AI Papers of the Week (a continuously updated open-source index of notable AI/ML papers, well suited for broad topics), a GitHub awesome-* repo, an arXiv survey PDF, or a well-maintained papers page.

Optional:

• bibliography_size: target bibliography size. Default 20 for a quick survey. Use 40 to 50 for a comprehensive survey, 80 to 100 for an exhaustive one. Section length and token budget scale with this.
• section_count: number of sections, default 6 to 10.

If the user has not provided these, use AskUserQuestion to collect them before proceeding.

Requirements

• FIREWORKS_API_KEY exported in the environment. The build script reads it from os.environ.
• Python 3 with stdlib only (urllib). No external dependencies.

Workflow for the agent

Follow these steps in order. Do not skip steps.

Step 1. Read the anchor resource

Fetch and read source_url. If it is a GitHub repo, fetch the README and any relevant README-*.md or papers.md indices. If it is an arXiv survey, use the abstract, figures, and section headings. If it is a blog post, read it in full. Extract the key subtopics and the papers or systems it references by name.

For broad AI/ML topics, DAIR.AI AI Papers of the Week is a particularly rich anchor: it has weekly issues going back years, each with short summaries of 6 to 10 notable papers, so it is easy to scan across time and filter to the subset that matches your topic.

If a paper-search tool is available to your agent (a Papers-of-the-Week MCP, arXiv search, Semantic Scholar, Google Scholar, an organization's internal index, etc.), use it to expand the candidate pool beyond what the anchor resource cites directly.

Step 2. Define the taxonomy and sections

Draft a taxonomy rooted at the topic with 4 to 8 branches, each with 2 to 4 children. Branches should cover distinct subareas of the topic, not overlap. Draft 6 to 10 numbered sections that match the taxonomy progression: introduction, foundations, methods, evaluation, open problems. Figure 1's viewport height scales automatically with the total leaf count via the geometry contract in style_spec.json, so deeper taxonomies render cleanly.

Step 3. Curate the bibliography

Pick real papers sized to bibliography_size. For a comprehensive survey, 40 to 50 entries is the sweet spot; the skill has been tested up to 100 entries with max_tokens=81920 in build_artifact.py. Every entry must have: key, authors, year, title, venue, and a 1 to 2 sentence summary. Do not invent papers. Every section's papers array must reference keys that exist in the bibliography.

Step 4. Write research_bundle.json

Write research_bundle.json in the skill directory (next to build_artifact.py). Use templates/research_bundle_template.json as the structural scaffold. Required top-level fields: title, authors_placeholder, anchor_source, abstract_hints, taxonomy, paradigms, stack, sections, table, bibliography. See examples/agentic-engineering/research_bundle.json for a complete worked example.

Step 5. Run the generator

python3 build_artifact.py

Run this from the skill directory. The script reads research_bundle.json and style_spec.json, calls Kimi K2.6 on Fireworks, and writes output/survey_kimi-k2p6_v{N}.html. Each run produces a new versioned file.

To use a different Fireworks model (for example Kimi K2.5 for side-by-side comparison):

FIREWORKS_MODEL=accounts/fireworks/models/kimi-k2p5 python3 build_artifact.py

Output filenames are slugged by model so you can compare versions across models.