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Found 1,900 Skills
Use when building durable AI agents or agentic workflows with Inngest and AgentKit, including model calls, tool calls, multi-agent networks, human approval, realtime progress, provider rate limits, crash-safe execution, and Agent Evals handoff. Covers AgentKit, `step.ai`, `step.run`, `step.waitForEvent`, native realtime, and when to use lower-level Inngest primitives instead of an in-memory agent loop. Use `inngest-agent-evals` with this skill when the user wants scoring, sessions, experiments, deferred scorers, or outcome-based evaluation for the agent.
Diagnoses and fixes skills in the dotnet/skills repository that lose to their own baseline, fail to activate, time out, or return "no credible improvement". Use when an evaluation verdict is a regression or underpowered, when a skill regressed after a change, when /evaluate reports no results, or when deciding whether a weak skill should be strengthened or retired. Do not use for scaffolding a brand-new skill (use create-skill) or a brand-new eval (use create-skill-test).
YC SAFE Agreement review and advisory skill for startup founders and lawyers. Use when user (1) uploads a SAFE agreement for review/comparison, (2) asks questions about how SAFEs work, or (3) requests to draft a standard YC SAFE. Triggers on keywords like SAFE, Simple Agreement for Future Equity, YC SAFE, valuation cap, discount, MFN, pro rata, convertible instrument.
Use when reporting progress in autonomous loop iterations. Triggers at the end of every autonomous loop iteration, when the autonomous-loop skill completes a BUILD phase, when progress reporting is needed for monitoring or exit evaluation, or when producing machine-parseable RALPH_STATUS blocks with exit signal protocol.
Check the consistency and authenticity risks of citations and references in NSFC proposal text (read-only): Verify the existence of bibkey, format issues such as BibTeX fields and DOI, and generate structured input for the host AI to evaluate item-by-item whether the text expression actually cites the literature; by default, only an audit report is output, and the proposal or .bib file is not directly modified (unless the user explicitly requests it).
Evaluate and plan Amazon marketplace expansion to international sites. Market sizing, regulatory requirements, logistics planning, and localization strategy for EU, UK, Japan, Australia, and other Amazon marketplaces.
Audit, prune, and improve agent guidance markdown files in repositories. Use when the user asks to check, audit, update, improve, or fix AGENTS.md, CLAUDE.md, or related guidance files. Adds missing commands and gotchas, removes stale entries, deduplicates, and keeps the file small and relevant. Scan for guidance files, evaluate quality against templates, output a quality report, then make targeted updates.
Multi-dimensional health assessment for .NET projects with letter grades (A-F) using Roslyn MCP tools. Evaluates 8 dimensions: build health, code quality, architecture, test coverage, dead code, API surface, security posture, and documentation. Produces a structured report card with actionable recommendations. Load this skill when: "health check", "how healthy is this", "project health", "code quality report", "grade this project", "assess codebase", "quality audit", "technical assessment", "codebase review", "report card".
Execute tasks through competitive multi-agent generation, multi-judge evaluation, and evidence-based synthesis
Financial Data Analysis Skill (based on `bl mcp` + Alibaba Cloud Bailian MCP Market `market-cmapi00073529`), covering financial instruments such as China A-shares, funds, and bonds. It supports stock screening, fund screening, fund manager screening, financial data query (net profit / revenue / ROE, etc.), macro and industry time-series data (GDP / CPI / production-sales-price), brokerage research report retrieval, and A-share listed company announcement retrieval. Be sure to activate when users ask about the following keywords: stock selection / stock screening, fund screening, fund manager screening, financial data / net profit / revenue / valuation, macroeconomy / GDP / CPI, industry production-sales-price, brokerage research report / industry research report, listed company announcement. Not applicable to: general programming issues, non-financial data, non-Chinese market instruments.
Metric-learning recognition (ml-recog) for fine-grained visual recognition. Learns embeddings for retrieval-based matching (e.g., retail product recognition) using triplet / contrastive losses. Use when training, evaluating, exporting, or running inference for a TAO metric-learning recognition model. Trigger phrases include "train metric learning", "ml-recog", "retrieval embeddings", "triplet loss recognition", "fine-grained matching".
Facilitates the first step of a proven ideal-customer (ICP) method: gathering raw, honest, specific observations about what a company and product actually are — before any judgment about strengths or weaknesses. Walks the user through twelve unsparing question categories (what customers praise, the complaint with no defense, what separates your most profitable customers, and more) — or processes a team's write-storm notes one observation at a time — and records the results in OBSERVATIONS.md (numbered O1, O2, …), vivid and unevaluated. For a company operating online, it first scans public reviews and press into an External Research section that seeds it. Load when the user wants to figure out their ideal customer, take an honest look at their company, run a strengths-and-weaknesses exercise from scratch, or says 'who is our Carol' or 'what are we actually good at.' Do NOT load to classify observations into strengths and weaknesses (the next step), or for personal self-reflection unrelated to a company.