Research

Extracts reusable knowledge from work sessions and codifies it into Claude Code skills. Use when: (1) /claudeception command to review session learnings, (2) save this as a skill or extract a skill from this, (3) what did we learn?, (4) after non-obvious debugging, workarounds, or trial-and-error discovery. Evaluates whether current work contains extractable knowledge, checks for existing skills, and creates or updates skills following the skill-authoring best practices.

Single entry skill for open-websearch setup and focused live retrieval, preferring local CLI/daemon paths while remaining compatible with workspace-exposed MCP tools.

Use the deps.dev API, also called Open Source Insights, to fetch package metadata, versions, dependency graphs, advisories, project mappings, and hash lookups. Use this skill when the user mentions deps.dev, Open Source Insights, package or version lookups, dependency intelligence, resolved dependency graphs, purl lookups, advisory queries, or integrating the deps.dev API into code.

This skill should be used when the user asks to "optimize entity presence", "build knowledge graph", "improve knowledge panel", "entity audit", "establish brand entity", "Google does not know my brand", "no knowledge panel", or "establish my brand as an entity". Works standalone with public search and AI query testing; supercharged when you connect ~~knowledge graph + ~~SEO tool + ~~AI monitor for automated entity analysis. For structured data implementation, see schema-markup-generator. For ...

Use when the user asks to "plan my paid account structure", "pick Search vs PMax", "lay out ad groups / asset groups", or "audit paid-vs-organic cannibalization"; designs campaign-type selection, ad-group/asset-group layout, targeting + match types, negative/exclusion hygiene, and a paid↔organic overlap audit, and scores the ROAS A (Audience) dimension + structure. Not for computing the final RQS — use ad-account-auditor; not for budget split — use budget-optimizer; not for organic site archi...

Search arXiv for academic papers, preprints, and research using the arXiv API. Finds foundational research, state-of-the-art techniques, and academic context. Returns structured findings with links, abstracts, and citations.

Answers questions about a code repository using source file analysis. Use when the user asks a question about how something works, wants to understand a component, or needs help navigating the code...

Expert web researcher using advanced search techniques and

Advanced Scrum Master with data-driven team health analysis, velocity forecasting, retrospective insights, and team development expertise. Features comprehensive sprint health scoring, Monte Carlo forecasting, and psychological safety frameworks for high-performing agile teams.

An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal impl...

Execute autonomous multi-step research using Google Gemini Deep Research Agent. Use for: market analysis, competitive landscaping, literature reviews, technical research, due diligence. Takes 2-10 ...

Search and monitor arXiv papers. Query by topic, author, or category. Track new papers, download PDFs, and summarize abstracts for research workflows.

Compare your genome to George Church (PGP-1) and estimate ancestry composition

Answers questions about a code repository using source file analysis. Use when the user asks a question about how something works, wants to understand a component, or needs help navigating the code...

Expert web researcher using advanced search techniques and

An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal impl...

Search and monitor arXiv papers. Query by topic, author, or category. Track new papers, download PDFs, and summarize abstracts for research workflows.

Run the full stack smoke test (31 checks — pods, HTTP, Kafka, Debezium, admin)

Structured discovery process for starting new work. Aligns on goals, success criteria, and key design decisions through a two-phase interview: goal clarification then systematic resolution of design questions with options, tradeoffs and recommendations. Use when kicking off a new project or feature.

Use when results are valid but surprising, negative, unstable, or ambiguous — to decide debug/rerun/ablate/revise/park. Not for engineering failures like NaN/OOM (use experiment-debugger). Not for confound or claim-drift audit before locking results into the paper (use research-results-auditor).

Review ML/AI result tables, LaTeX table files, captions, provenance, and paper table style. Use for benchmark, ablation, metric, model-spec, and compute tables.

Pre-submission checklist for LaTeX academic papers. Use when the user wants to submit a paper, check submission readiness, prepare camera-ready, switch to final mode, or verify a paper is ready for a conference deadline.

Audit a skill repository or installed skill collection for global consistency, lifecycle coverage, routing quality, documentation drift, memory writeback coverage, stale future-skill references, broken helper paths, and validation readiness. Use this skill whenever the user asks for a global consistency audit, skill taxonomy review, lifecycle audit, cross-skill routing audit, README or AGENTS inventory consistency check, or maintenance pass over a collection of agent skills.

Run artifact-driven sidecar agent tasks through one-shot Codex CLI sessions. Use when a main agent should delegate bounded scans, drafts, audits, pre-reviews, or mechanical repo tasks to a fast isolated sidecar model such as gpt-5.3-codex-spark while keeping final decisions with the main agent.