From agent-loop
AI-first investigative research using the orchestrator-worker pattern — decompose a question, fan out parallel subagent searches across multiple source angles, triangulate across independent sources, adversarially refute, synthesize a CITED answer with confidence, and loop until saturation. Use for open questions needing a well-supported answer. (For validating a hypothesis against data, use the experiment profile instead.)
How this skill is triggered — by the user, by Claude, or both
Slash command
/agent-loop:deep-researchThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Investigate an open question to a well-supported, **cited** answer. Modeled on the orchestrator-worker research
Investigate an open question to a well-supported, cited answer. Modeled on the orchestrator-worker research system (a lead plans → parallel subagents gather in their own contexts → lead synthesizes → a citation pass), which beats single-agent research because breadth is parallelized and each angle is blind to the others.
{claim, source, confidence}. They're blind to each other — that's the point.unverified.
Prefer primary sources; down-rank low-credibility ones. Note disagreements explicitly./grill-ai + /doubt-driven-development on the surviving claims — try to break each.
Confidence-filter: verify a weak claim up, or drop it. Better 3 solid findings than 30 maybes.UNKNOWN — needs X where evidence is thin. A report, not a link dump.decided ledger (key=finding) so later rounds never re-search the same ground.UNKNOWN.npx claudepluginhub andonmitev/agent-loop --plugin agent-loopProvides behavioral guidelines to reduce common LLM coding mistakes, focusing on simplicity, surgical changes, assumption surfacing, and verifiable success criteria.
Searches, retrieves, and installs Agent Skills from prompts.chat registry using MCP tools like search_skills and get_skill. Activates for finding skills, browsing catalogs, or extending Claude.
Creates, edits, and optimizes skills for Claude Code, including drafting, evaluating with test prompts, iterating on performance, and improving skill descriptions for better triggering accuracy.