Install any skill in seconds. Free to start, no credit card required.
Get Started Free →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",
| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-11 | ✗→✓ | ▲ Improved | 69% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 67% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 21% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 70% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 52% | 0% |
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.
| 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 |
/wiki research runs: gives the loop a high-quality seed bundle| Input | Required | Description | |-------|----------|-------------| | topic | yes | "Reasoning Models", "Agentic Engineering" | | source_url | yes | Public anchor: arXiv survey, GitHub awesome-list, canonical blog post | | --wiki <slug> | yes | Target wiki for the artifact | | --bibliography-size N | no | Default 20. 40-50 comprehensive, 80-100 exhaustive | | --section-count N | no | Default 6-10 numbered sections | | --provider name | no | Override provider (default: first env var found) | | --model id | no | Override model |
WebFetch source_url. Extract subtopics + cited papers. For GitHub awesome-lists, walk README + linked papers files. For arXiv survey PDFs, use abstract + ToC.
Use templates/research_bundle.template.json as scaffold. Required keys:
json{ "topic": "...", "anchor_source": "...", "abstract_hints": ["..."], "taxonomy": [{"branch": "...", "children": [{"name": "...", "description": "..."}]}], "sections": [{"title": "...", "guidance": "...", "papers": ["key1","key2"]}], "bibliography": [{"key": "author-year-shortname", "authors": "...", "year": 2024, "title": "...", "venue": "...", "summary": "..."}] }
Hard rules:
bibliography must be real. No invented entries.key referenced in sections[].papers must exist in bibliography.bashnode $SKILL_ROOT/scripts/build-survey.js \ --bundle <path-to-research_bundle.json> \ --wiki <slug> \ [--provider anthropic|openai|openrouter|fireworks|custom] \ [--model <id>]
Generator:
[^paper-key] citations, no HTML).<wiki>/derived/surveys/<topic-slug>.md.<wiki>/sources.md (deduped by key).wiki-cli.js page to upsert into FTS5 index.If prose is thin: tighten sections[].guidance and rerun. Output filename versions automatically (<slug>-v2.md, <slug>-v3.md).
To compare providers:
bashnode 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
Each writes a separate versioned file; diff them.
text<wiki-root>/ ├── sources.md # bibliography rows appended (deduped) └── derived/surveys/ └── <topic-slug>-v1.md # the survey # title (h1) # ## 1. Introduction # ## 2. Foundations # ... # ## References # [^src-bib-<slug>] author year. title. venue.
papers array references keys in bibliography.sources.md use the slug-style id src-bib-<slug> (derived from the bibliography key); cite as [^src-bib-<slug>]. Manual non-bibliography sources continue to use src-NNN.research_bundle.json), not on the generated output.bash/wiki init reasoning-models --title "Reasoning Models" --flavor research # 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: # (edit reasoning-models/wiki.config.md, set auto_research.enabled: true) 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
Other measured skills in the registry, with their headline benchmark lift.