tzachbon avatar

Ralph Specum Research

This is the research phase coordinator for Ralph Specum, a spec-driven development workflow. It kicks in when you explicitly invoke it or ask Ralph to run research. The skill itself doesn't do the analysis work. Instead, it resolves which spec you're working on, gathers context from your codebase and progress notes, runs an optional brainstorming interview, then delegates the actual research generation to a sub-agent. You get a research.md file that covers patterns, constraints, risks, and verification notes. By default it stops and waits for your approval before moving to requirements, unless you use quick mode. It's opinionated about file structure and state management, which is helpful if you're already in the Ralph ecosystem but means there's a learning curve upfront.

GitHub stars
340
last updated
May 19, 2026

Install

$ npx -y skills add tzachbon/smart-ralph --skill ralph-specum-research --agent claude-code

Installs into .claude/skills of the current project.

Files

Ralph Specum Research

You are a coordinator, not a researcher -- delegate ALL work to a research-analyst sub-agent.

Derive RALPH_CODEX_PLUGIN_ROOT from this loaded skill by resolving two parent directories from the SKILL.md directory. Never derive it from the project working directory.

Contract

  • Resolve the active spec by explicit path, exact name, or .current-spec
  • Respect .claude/ralph-specum.local.md when present
  • Default specs root is ./specs
  • Keep the canonical Ralph file names
  • Merge state fields only

Action

Sponsors

Details

Repository
github.com/tzachbon/smart-ralph
Categories
Data Science & ML, Automation & Workflows
License
MIT
First seen
Jun 11, 2026

More from tzachbon/smart-ralph

All 28
1lingzhi227 avatarliterature-searchlingzhi227/agent-research-skillsSearch academic literature using Semantic Scholar, arXiv, and OpenAlex APIs. Returns structured JSONL with title, authors, year, venue, abstract, citations, and2.7Kskills.sh installs
2lingzhi227 avatarexperiment-codelingzhi227/agent-research-skillsWrite ML experiment code with iterative improvement. Generate training/evaluation pipelines, debug errors, and optimize results through code reflection. Use whe1.6Kskills.sh installs
3orchestra-research avatarmlfloworchestra-research/ai-research-skillsTrack ML experiments, manage model registry with versioning, deploy models to production, and reproduce experiments with MLflow - framework-agnostic ML lifecycl1.1Kskills.sh installs
4orchestra-research avataraudiocraft-audio-generationorchestra-research/ai-research-skillsPyTorch library for audio generation including text-to-music (MusicGen) and text-to-sound (AudioGen). Use when you need to generate music from text descriptions1.1Kskills.sh installs
5orchestra-research avatarweights-and-biasesorchestra-research/ai-research-skillsTrack ML experiments with automatic logging, visualize training in real-time, optimize hyperparameters with sweeps, and manage model registry with W&B - collabo1.1Kskills.sh installs
6orchestra-research avatarevaluating-code-modelsorchestra-research/ai-research-skillsEvaluates code generation models across HumanEval, MBPP, MultiPL-E, and 15+ benchmarks with pass@k metrics. Use when benchmarking code models, comparing coding 1Kskills.sh installs