survey-generator
Compile a structured literature survey on any AI/ML topic. Agent curates a research bundle (taxonomy + sections + bibliography of real papers) from a public anchor resource, then a chosen LLM generates the survey artifact. Output target is a wiki page (markdown), not a one-off HTML — survey lands in `<wiki>/derived/surveys/<slug>.md` with full bibliography rows in `sources.md`. Provider-agnostic (Anthropic/OpenAI/OpenRouter/Fireworks/custom OpenAI-compat). Use when the user asks for a "survey", "literature review", "lit review", or "deep dive" on a technical topic.
npx skills add majiayu000/claude-skill-registry --skill survey-generator --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Survey Generator Provider-agnostic literature-survey artifact generator. Output flows into a pro-workflow wiki, not a standalone HTML file — survives sessions and indexes for FTS5 retrieval. ## Diff vs dair-academy version | dair | pro-workflow | |------|--------------| | Hardcoded Kimi K2.6 on Fireworks | Provider-agnostic (Anthropic/OpenAI/OpenRouter/Fireworks/custom) | | Output = single-file HTML with inline SVG | Output = wiki markdown page + bibliography rows in `sources.md` | | One-off artifact, no follow-up | Persists in FTS5 index; reused by `wiki-research-loop` | | Manual run only | Composable with `/wiki research` for auto-bibliography expansion | ## When to use - "Survey on <topic>" / "lit review on <topic>" - Onboarding a new domain — generate the map-of-the-field - After a wiki has 10-30 sources, compile a synthesis page over them - Pre-step before `/wiki research` runs: gives the loop a high-quality seed bundle ## Inputs | Input | Required | Description | |-------|----------|-------------| | `topic` | yes | "Reasoning Models", "Agentic Engineering" | | `source_url` | yes | Public anchor: arXiv survey, GitHub awesome-list, canonical blog post | | `--wiki <slug>` | ye
- Diff vs dair-academy version
- When to use
- Inputs
- Workflow (the agent runs these in order)
- Step 1 — Read the anchor
- Step 2 — Build researchbundle.json
- Step 3 — Run the generator
- Step 4 — Iterate
- Output structure
- Hard rules
- Composing with research loop
node $SKILL_ROOT/scripts/build-survey.js \ node build-survey.js --bundle bundle.json --wiki agent-memory --provider openai --model gpt-4o node build-survey.js --bundle bundle.json --wiki agent-memory --provider anthropic --model claude-opus-4-7 Manually compile a research_bundle.json node skills/survey-generator/scripts/build-survey.js --bundle bundle.json --wiki reasoning-models Now the wiki has a structured survey + 50 bibliography rows Enable auto-research to expand: node skills/wiki-research-loop/scripts/research-loop.js seed reasoning-models "chain-of-thought failure modes" --depth 0 node skills/wiki-research-loop/scripts/research-loop.js run reasoning-models
What does the survey-generator skill do?
Compile a structured literature survey on any AI/ML topic. Agent curates a research bundle (taxonomy + sections + bibliography of real papers) from a public anchor resource, then a chosen LLM generates the survey artifact. Output target is a wiki page (markdown), not a one-off HTML — survey lands in `<wiki>/derived/surveys/<slug>.md` with full bibliography rows in `sources.md`. Provider-agnostic (Anthropic/OpenAI/OpenRouter/Fireworks/custom OpenAI-compat). Use when the user asks for a "survey", "literature review", "lit review", or "deep dive" on a technical topic.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill survey-generator --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.