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ARIS-inspired deep research workflow with adversarial executor/reviewer loops, persistent memory, and claim-evidence assurance checks.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
Runs scoped research as a composite skill through the shared skill registry and router.
Create and maintain paper research packets in this repo with the required research/<paper>/ layout, architecture docs, and experiment implementations aligned to the agent-browser TypeScript stack. Use this whenever the user asks to add a paper, summarize research, create experiment plans, or implement a paper capability as a reference architecture.
Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.
Operating guide for the agent-browser workspace shell. Use this whenever the user asks how to inspect, navigate, or modify the active agent-browser workspace, Files surface, browser pages, sessions, clipboard history, render panes, or WebMCP tool flows. Prefer it before improvising tool chains because the active workspace, workspace files, and mounted session drives have specific semantics in this project.
Create an AgentEvals-style eval suite for a named agent under .agents/<agent-name>/.evals/. Use this whenever the user asks for an eval, regression suite, benchmark, or repeatable acceptance test for a workspace agent. Prefer it even when the user asks for a smoke test or acceptance check without naming AgentEvals directly.
Create a scoped agent folder with an AGENTS.md file under .agents/<agent-name>/. Use this whenever the user asks for a new agent, reusable agent instructions, a workspace-scoped AGENTS.md, or a named automation persona inside the current workspace. Prefer it even when the user only describes the role and not the file layout.
| name | deep-research-harness |
| description | ARIS-inspired deep research workflow with adversarial executor/reviewer loops, persistent memory, and claim-evidence assurance checks. |
DeepResearch.start(topic, constraints)DeepResearch.review(artifactPath)DeepResearch.audit(reportPath, ledgerPath)DeepResearch.revise(reportPath, reviewerFeedbackPath)Every substantive claim must include:
supported, partially-supported, unsupported).research/wiki/*.md persistent notes and source summaries..research/claim-ledger.jsonl append-only claim log..research/reports/final-report.md user-facing deliverable..research/reports/audit-report.md assurance findings.