claude-adaptive-research
Research that adapts to YOUR projects. Autonomous loops that get smarter with every run.
Set a topic. Walk away. Come back to a quality-gated report — with findings mapped directly to your projects, role, and goals.
One command. Personalized research. Compound learning.
What it does
Most AI research workflows are manual: you ask a question, read the answer, ask another. claude-adaptive-research automates the entire loop. You give it a topic, it researches autonomously across the web, writes a structured report, scores it for quality, and adapts every finding to YOUR projects and goals.
The plugin learns your context once (a 2-minute setup) and then every report speaks directly to your work — not generic advice, but specific adaptations.
/auto-run "How do ant colony optimization patterns apply to database sharding?"
Claude researches, analyzes, writes a report, checks quality, and delivers — all without you touching the keyboard.
Quick Start
# Install the plugin
claude plugins install primeline-ai/claude-adaptive-research
Then in any Claude Code session:
/auto-run
First run triggers a guided setup:
- See examples of what's possible (domains, presets, free-text)
- Choose your research domains (e.g., psychology, biology, finance)
- Quick profile interview (your projects, role, goals)
- Done — start researching
Features
Autonomous Research Loop
Claude researches your topic independently — searching the web, analyzing sources, synthesizing findings. No babysitting required. The loop continues across multiple iterations until the report meets quality standards.
Personalized Adaptations
Every report includes an Adaptations section that maps findings to YOUR projects. A biology finding about swarm intelligence doesn't just explain the concept — it shows how it applies to your specific SaaS architecture or your open-source library.
Quality Gate
Reports are scored on 4 criteria (structure, depth, originality, findings count). Score below 50? Claude automatically improves the report before completing. No half-baked outputs.
Premium rubric (v2, optional): For stricter premium-by-default scoring, run python3 scripts/quality_gate_v2.py path/to/report.md. v2 checks 5 metrics (citation density, DSV evidence, gap disclosure, ECP section, cross-track convergence) and assigns a tier: Premium (5/5), Standard (3-4/5), Reject (<3/5). See knowledge/quality-gate-v2.md. Requires Python 3.9+.
Cross-Track Aggregation (multi-track runs)
Run multiple research tracks in parallel on the same problem (one track per angle: technical, market, prior-art, etc.). After all tracks finish, scripts/cross_track_aggregator.py detects where independent tracks converged on the same idea (Jaccard similarity over content tokens, Union-Find clustering). Convergence across independent observers is a stronger signal than a single track. See knowledge/cross-track-aggregation.md. Requires Python 3.9+.
Compound Learning
Each run makes the next one smarter. Keywords, patterns, and follow-up questions discovered during research are saved and injected into future runs. Run 1 finds keywords → Run 2 searches deeper → Run 3 connects cross-domain. This is what makes it adaptive, not just autonomous.
Compound Score
Track your research progress: total runs, findings discovered, streak days. Research becomes a habit with visible momentum.
Research Domains
Organize your research into knowledge areas. Pick from examples or create your own:
| Domain | What it covers | Adapts to |
|---|
| Psychology | Cognition, bias, motivation | UX, conversion, agent behavior |
| Biology | Swarm, evolution, networks | Algorithms, architecture |
| Physics | Entropy, resonance, networks | System optimization |
| Finance | Income, pricing, monetization | Your business model |
| Engineering | Patterns, control theory | Code quality, DevOps |
| Everyday Life | Habits, heuristics, systems | Productivity, workflows |
Presets
Pre-configured research strategies for common needs:
