Survey Generator

Survey Generator Skill

When to Use

Use when this workflow matches the user request: Use this skill for its documented workflow.

Source: dair-ai/dair-academy-plugins (MIT).

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.

Step 6. Preview and iterate

Open the HTML file locally. It is a fully self-contained HTML document, so you can also serve it from any static host, embed it in a dashboard, or hand it to any artifact-preview mechanism your agent exposes.

If figures look weak, sharpen style_spec.json (the required_figures and figure_quality_note keys) and rerun. If prose is thin or sections are missing, tighten the section guidance fields in research_bundle.json. Do not edit the Kimi output directly; iterate on inputs.

Common figure failure modes and the style_spec patterns that fix them:

• Nodes from different panels collapsing into one panel: require <g transform="translate(OFFSET,0)"> groups with panel-local coordinates (enforced for Figure 2).
• Leaf rects overlapping vertically so labels get clipped: enforce rect_pitch greater than rect_height with an explicit formula and a sanity check (enforced for Figure 1).
• Root label overflowing its pill: pin minimum rect width in the spec (enforced for Figure 1, width=200).
• Sibling nodes in a row overlapping horizontally (e.g. Worker A, Worker B, Worker C in an orchestrator-workers panel): enforce a deterministic rect_width and center_x formula for N nodes in a fixed-width panel, with a minimum horizontal gap between adjacent rects (enforced for Figure 2 multi-node rows).
• Panel contents drifting to the left or right edge instead of sitting in the middle of the panel background: pin each group's translate offset to match the panel background's x position (10, 270, 530) and center all content on panel-local x=120 (enforced for Figure 2).
• Figures emitted in the wrong numeric order because the model preferred a different narrative flow: require the captions to use the exact IDs from required_figures in sequence (Figure 1 before Figure 2 before Figure 3), even if it means placing two figures in the same section (enforced via hard_rules_for_generation).
• Right-side labels on the stack diagram getting clipped at the viewport edge: widen the stack SVG viewport to 720 and require role-text tspans to fit within x=710 (enforced for Figure 3).

When adding a new figure or changing an existing one, follow the same pattern: declare an absolute viewport, per-element coordinates or a deterministic formula, and a hard-invariant check clause at the end of the description.

Files in this skill

• SKILL.md - this file.
• build_artifact.py - Python script that calls Fireworks.
• style_spec.json - visual and structural spec (topic-agnostic).
• templates/research_bundle_template.json - empty template for new topics.
• examples/agentic-engineering/ - reference 100-paper run (research_bundle.json + survey.html).

Hard rules the agent must follow

1. Never invent bibliography entries. Every cited paper must be a real work with a real venue.
2. Every section's papers array must reference keys in the bibliography.
3. Never edit the generated HTML. Iterate on research_bundle.json or style_spec.json and rerun.
4. Do not modify the hard rules in style_spec.json.hard_rules_for_generation.
5. Keep the style_spec topic-agnostic. Topic-specific content lives only in research_bundle.json.
6. Do not use em dashes or arrow symbols in the research bundle prose fields.

Limitations

• Requires the upstream tool, account, API key, or local setup when the workflow names one.
• Does not authorize destructive, production, paid, or external-message actions without explicit user approval.
• Validate generated artifacts or recommendations against the user's real sources before treating them as final.