skills/survey-generator/SKILL.md
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 skillsauth add rohitg00/pro-workflow survey-generatorInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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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:
{
"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.node $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:
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
Each writes a separate versioned file; diff them.
<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./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
devops
SkillOpt-flavored offline training loop for any SKILL.md. Treats accumulated learn-rule corrections as training trajectories, proposes bounded patches via an optimizer LLM, gates each candidate against a held-out validation set built from the user's own past corrections, and ships only candidates that demonstrably improve the score. Inspired by Microsoft SkillOpt's ReflACT pipeline (rollout → reflect → aggregate → select → update → evaluate) adapted to pro-workflow's SQLite store. Use when a skill has accumulated 8+ learn-rule rows and the user wants the skill itself to get better, not just longer.
tools
Prevent destructive operations using Claude Code hooks. Three modes — cautious (warn on dangerous commands), lockdown (restrict edits to one directory), and clear (remove restrictions). Uses PreToolUse matchers for Bash, Edit, and Write.
development
Complete AI coding workflow system. Orchestration patterns, 18 hook events, 5 agents, cross-agent support, reference guides, and searchable learnings. Works with Claude Code, Cursor, and 32+ agents.
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Analyze permission denial patterns and generate optimized alwaysAllow and alwaysDeny rules. Use when permission prompts are slowing you down or after sessions with many denials.