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Found 8 Skills
Use when querying, transforming, or editing structured data (JSON, YAML, TOML, XML, CSV). Prefer these tools over grep/sed/awk on structured formats.
Vendor-neutral skill to track security exception expirations and generate remediation reminders.
Guide for using Nushell for structured data pipelines and scripting. Use when writing shell scripts, processing structured data, or working with cross-platform automation.
Use when writing or running Nushell commands, scripts, or pipelines - via the Nushell MCP server (mcp__nushell__evaluate), via Bash (nu -c), or in .nu script files. Also use when working with structured data (JSON, YAML, TOML, CSV, Parquet, SQLite), doing ad-hoc data analysis or exploration, or when the user's shell is Nushell.
Structured data research: search sources, extract structured data, archive raw sources, maintain canonical tracker pages, deduplicate. Parameterized via YAML recipes for investor updates, donations, company updates, or any email-to-structured-data pipeline.
Pull Bigdata.com (RavenPack) financial and news data through the official `bigdata-client` SDK and its public `/v1/*` REST endpoints when the Bigdata MCP server returns only pre-synthesized tearsheets but you need the machine-readable substrate underneath. MCP search returns prose chunks (text + relevance only — no per-chunk sentiment, no entity spans); its tearsheets give only aggregate values, not computable time series or per-field JSON. This skill bundles a verified, cost-guarded toolkit over the official REST API: annotated chunk search, entity/ISIN resolution, analyst estimates, calendar/surprise/ ratings/targets, financial statements, TTM metrics & ratios, prices, dividends, revenue segments, a daily entity-sentiment series, co-mention graph, screener, and batch search. Use it whenever the user mentions Bigdata.com, RavenPack, a `bd_v2_` key, the bigdata MCP, rp_entity_id, chunk/query_unit cost, or wants structured financials, fundamentals, prices, sentiment, or annotated news.
Parser Expert integration. Manage data, records, and automate workflows. Use when the user wants to interact with Parser Expert data.
Extract actionable Linear tickets from ambiguous input — Slack conversations, call transcripts, screenshots, meeting notes, or any unstructured material. Proposes tickets in a scratchpad file for user review, then creates them in Linear on approval. Use when the user wants to turn conversations, transcripts, screenshots, or notes into Linear tickets. Also use when user says "create tickets from this", "send to linear", "make issues from this call/chat", or provides raw material and asks for tickets.