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Found 2,126 Skills
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.
Make sure to use this skill whenever the user mentions anything related to job searching on Akademikernes Jobbank, jobbank.dk, or looking for academic or highly educated positions in Denmark — even if they don't mention jobbank.dk explicitly. Also invoke this skill for questions about Danish job listings, graduate trainee positions, Ph.d. jobs, or finding work in specific industries or regions in Denmark. Trigger phrases include: jobbank, akademikernes jobbank, jobs denmark, academic jobs denmark, find job denmark, highly educated jobs, graduate job denmark, trainee position denmark, ph.d. position denmark, postdoc denmark, studiejob, fuldtidsjob, deltidsjob, vikariat, freelance job, praktikplads, job søgning, jobsøgning, søg job, ledige stillinger, nye jobs, it jobs denmark, engineering jobs denmark, marketing jobs denmark, finance jobs denmark, healthcare jobs denmark, remote job denmark, fjernarbejde, job københavn, job aarhus, job odense, nyuddannede job, job til nyuddannede, international job denmark, jobbank søgning, find stilling, data scientist job, software developer job, projektleder stilling, konsulent job, data analyse job.
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.
Applies Neil Rackham's SPIN methodology (Situation/Problem/Implication/Need-payoff questions) to major B2B sales. Use for complex multi-call sales cycles, enterprise deals where the customer must justify the decision to others, when objections are mounting, when calls end in vague continuations instead of advances, when traditional closing techniques are backfiring on large deals, or when designing discovery-call structure. Triggers include 'my deal isn't closing', 'too many objections', 'B2B sales coaching', 'discovery call structure', 'stuck in the middle of the sale'. Not for transactional sub-$50 sales, pure consumer impulse, or PLG self-serve products.
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.
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.
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.
Use when writing or reviewing n8n expressions (`{{...}}` syntax), `$json` / `$node` references, Luxon date code, or expression errors. Triggers on `{{}}`, `$json`, `$node`, `$input`, `DateTime`, `Luxon`, "expression error", "evaluating", "format date", "transform field", or any node-parameter assignment.
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.
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.
Convert evaluation traces and production logs into SFT examples and preference pairs. Use when graded traces or failure examples exist and need to become training data, when applying rejection sampling to model outputs, or when building DPO pairs from passing and failing runs.