搜索「Data Analysis」的结果

io.github.getsentry/sentry/backend-conventions

Sentry backend conventions for logging, tracing/spans, span/tag attribute naming, metrics tags, and the options system. Use when adding or editing Python in src/ that logs (logger.info/exception), records metrics (metrics.incr/timing with tags), instruments spans/transactions, calls sentry_sdk.set_tag/set_attribute or set_span_tag/set_span_data, or reads registered options with options.get(). Trigger on "add logging", "log an error", "add a metric", "add a span", "instrument tracing", "set an attribute", "add a tag", "read an option", "LOG005", "LOG011", or metrics tag cardinality questions.

io.github.supabase/supabase/safe-sql-execution

Use whenever code will build, return, fetch, or execute SQL that runs against a user's real Postgres database — even when the request reads like an ordinary feature or bug fix and never says "security," "injection," or "SafeSqlFragment." This covers: writing or editing any pg-meta function, query builder, or endpoint that builds/returns SQL for database objects (tables, views, functions, DB triggers, indexes, RLS policies); interpolating a schema/table/column/search/route-param value into SQL text; storing, fetching, or re-running SQL that round-trips from the database (a policy's definition, a function/view definition, a snippet's saved content); and any "Run"/"Apply"/"Execute" action that sends SQL to a project's database (SQL editor run-selection, policy editor apply, snippet runner). Load this BEFORE writing such code, not only when reviewing a finished diff. Skip only for changes that never touch SQL text or execution — styling, unrelated data hooks, non-SQL form validation, or UI layout work.

io.github.supabase/supabase/clickhouse-logs-queries

Write, review, and migrate Supabase logs queries against the ClickHouse-backed `logs` table (the `logs.all.otel` analytics endpoint). Use this whenever a task involves Logs Explorer SQL, the `log_attributes` map, querying a log `source` (edge_logs, postgres_logs, auth_logs, etc.), translating an old BigQuery `cross join unnest(metadata)` logs query to ClickHouse, or wiring analytics log SQL in `apps/studio/data/logs` and `apps/studio/components/interfaces/Settings/Logs`. Reach for it even when the user just says "logs query", "Logs Explorer", or pastes a BigQuery logs query to convert, not only when they name ClickHouse.

io.github.supabase/supabase/studio-mock-api-tests

Component tests for Supabase Studio that mock API requests at the network layer with MSW. Use when writing or reviewing a component test that exercises a React Query hook or mutation, or when migrating an existing test away from vi.mock('@/data/...'). Covers the customRender + addAPIMock template and the jsdom/MSW gotchas that cost real debugging time.

io.github.supabase/supabase/studio-queries

React Query conventions for data fetching in Supabase Studio. Use when writing or reviewing query hooks, mutation hooks, or query keys in apps/studio/data/ — including adding the first fetch or mutation for a new API endpoint or resource. Covers queryOptions pattern, keys.ts structure, mutation hook template, and imperative fetching.

io.github.supabase/supabase/react-hook-form

Correct React Hook Form usage anywhere in the monorepo — data flow, subscriptions, reset, dirty state, number inputs, and controlled-input rules. Load this BEFORE writing or modifying ANY form code, adding a field to an existing form, touching watch/useWatch/formState/getValues/setValue/reset, wiring a form into a dialog or sheet, or building a submit/cancel footer — even when the change looks trivial. The codebase contains widespread RHF anti-patterns; without this skill you will copy them. For form layout and which components to use, also load studio-ui-patterns.

io.github.discourse/discourse/discourse-warpdrive-models

Use when creating a new WarpDrive-backed frontend model, or when reading, using, or changing a model already converted to WarpDrive (anything under frontend/discourse/app/data or extending RestCompatModel/WarpRestModel)

io.github.tabler/tabler/shared-lib

Move logic out of Astro frontmatter into `shared/lib/*.ts` and cover it with a vitest test. Use when a component's or page's frontmatter grows past assembling classes, when demo data needs shaping or formatting, when the same helper appears in two components, or when the user asks for a helper, utility or unit test under `shared/`. Covers what belongs there, module conventions, the sibling `*.test.ts` rule, deterministic demo data, and the strict-TypeScript patterns the repo uses.

io.github.K-Dense-AI/scientific-agent-skills/opentrons-integration

Author, review, migrate, simulate, and troubleshoot official Opentrons Python Protocol API v2 protocols for Flex and OT-2 robots. Use for robot-specific liquid handling, deck and labware setup, pipettes, modules, runtime parameters, liquid classes, and Opentrons App analysis. Use pylabrobot instead when one workflow must support multiple robot vendors.

io.github.K-Dense-AI/scientific-agent-skills/networkx

Create, analyze, and visualize complex networks and graphs in Python with NetworkX. Use when working with network/graph data structures, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks (random, scale-free, small-world), reading/writing graph file formats, or drawing network topologies. Common applications include social, biological, transportation, and citation networks.

io.github.K-Dense-AI/scientific-agent-skills/statsmodels

Statistical models library for Python. Use when you need specific model classes (OLS, GLM, mixed models, ARIMA) with detailed diagnostics, residuals, and inference. Best for econometrics, time series, rigorous inference with coefficient tables. For guided statistical test selection with APA reporting use statistical-analysis.

io.github.K-Dense-AI/scientific-agent-skills/optimize-for-gpu

GPU-accelerates scientific Python on NVIDIA hardware and verifies that the result is correct and faster. Use for CUDA/GPU optimization; CPU-bound NumPy, SciPy, pandas, scikit-learn, NetworkX, scikit-image, vector-search, image-processing, graph, simulation, or file-I/O workloads; CuPy, cuDF, cuML, cuGraph, cuVS, cuCIM, KvikIO, Warp, Newton, Numba-CUDA, or RAFT questions; and profiling, memory-transfer, kernel, or multi-GPU bottlenecks. Also use when large data-parallel Python code is slow and GPU acceleration is a plausible option, even if the user does not name CUDA.

io.github.K-Dense-AI/scientific-agent-skills/astropy

Core Python library for astronomy and astrophysics workflows that need Astropy APIs, including units/quantities, coordinates, FITS I/O, tables, time systems, WCS, and cosmology. Use when implementing or debugging astronomical data analysis code with Astropy.

io.github.K-Dense-AI/scientific-agent-skills/pysam

Python/HTSlib workflows for genomic files. Use when reading, querying, filtering, or writing SAM/BAM/CRAM, VCF/BCF, FASTA/FASTQ, or tabix data with pysam, including pileup, coverage, indexing, and CRAM references.

io.github.K-Dense-AI/scientific-agent-skills/paperclip

Search and read full-text biomedical papers, FDA/PMDA/EMA regulatory documents, clinical trial registries, and UniProt/PDB/ChEMBL entries with the Paperclip CLI from GXL. Covers installing and authenticating the `paperclip` binary with a PAPERCLIP_API_KEY, the read-only virtual filesystem under /papers, /fda, /trials, /proteins and /clipboard, source-scoped semantic search, corpus-wide grep, metadata lookup and SQL, map/reduce reading across many papers, figure vision analysis, opt-in paper repositories with claim verification, and line-pinned citations. Use when asked to install paperclip, run paperclip search/grep/map/reduce/sql/repo, find or read biomedical literature, regulatory filings or clinical trials through paperclip, or produce citations with line numbers.

io.github.K-Dense-AI/scientific-agent-skills/nextflow

Build, run, and debug Nextflow data pipelines and nf-core workflows end to end. Use whenever the user mentions Nextflow, nf-core, .nf files, nextflow.config, DSL2, processes/channels/operators, samplesheets, or wants to run a community pipeline (e.g. nf-core/rnaseq, nf-core/sarek), write or test a module/subworkflow with nf-test, configure executors/containers (Docker, Singularity/Apptainer, Conda, Wave), scale a workflow to HPC/SLURM or cloud (AWS Batch, Google Batch, Azure, Kubernetes), or debug a failed/-resume run. Make sure to use this skill for any reproducible scientific/bioinformatics workflow work even if the user does not say the word "Nextflow", and for authoring nf-core-compliant pipelines, modules, configs, and linting.

io.github.K-Dense-AI/scientific-agent-skills/bioservices

Unified Python interface to 40+ bioinformatics services. Use when querying multiple databases (UniProt, KEGG, ChEMBL, Reactome) in a single workflow with consistent API. Best for cross-database analysis, ID mapping across services. For quick single-database lookups use gget; for sequence/file manipulation use biopython.

io.github.K-Dense-AI/scientific-agent-skills/gget

Fast CLI/Python queries to 20+ bioinformatics databases. Use for quick lookups: gene info, BLAST/BLAT, viral sequence downloads, AlphaFold structures, enrichment analysis, OpenTargets, COSMIC, CELLxGENE, and 8cube mouse specificity/expression data. Best for interactive exploration and simple queries. For batch processing or advanced BLAST use biopython; for multi-database Python workflows use bioservices.

io.github.usestrix/strix/api-security-testing

Security-test a REST, GraphQL, or gRPC API with Strix — autonomous agents that enumerate endpoints from an OpenAPI/GraphQL schema (or by crawling), then actually exploit the API-specific vulnerability classes in the OWASP API Security Top 10 (2023) — broken object-level authorization (BOLA/IDOR), broken object property level authorization (excessive data exposure and mass assignment), broken function-level authorization, unrestricted resource consumption, SSRF, injection, and auth/token flaws. Every finding comes with a working proof-of-concept request. Use when the user asks to pentest, security-test, audit, or find vulnerabilities in an API, endpoint, or backend service.

io.github.CopilotKit/CopilotKit/reskin

Author a NEW skin for the reskinnable-demo app. A skin is a self-contained domain plugin under src/skins/<id>/ that implements the frozen `Skin` contract (src/shell/skin-contract.ts) to swap the app's entire experience — brand, theme, layout, pages, tools, data, and agent — as a live sales demo. Use when the user says "add a skin", "create a skin", "new skin", "reskin the app", "make a <domain> skin", or wants the app re-themed as a new product. Do NOT use for editing the shell itself (src/shell/**), the shared token vocabulary (src/app/globals.css), or the shipped skins unless explicitly asked.

io.github.TryGhost/Ghost/tinybird-cli-guidelines

Tinybird CLI commands, workflows, and operations. Use when running tb commands, managing local development, deploying, or working with data operations.

io.github.PostHog/posthog/creating-box-plot-insights

Creates product analytics or SQL-backed box plot insights in PostHog. Use when a user asks to create, build, or save a box plot, visualize a numeric distribution, compare quartiles or medians across dates or groups, or turn SQL results into a box plot. Chooses between a standard Trends box plot and a SQL insight, validates the distribution data, saves the insight, and verifies it.

io.github.PostHog/posthog/debugging-experiments

Debug and support PostHog Experiments (A/B tests) for a customer looking at their own results. Use whenever an experiment support ticket is pasted or a customer asks a results question, most commonly "why aren't my exposures even?", "why is one variant getting no traffic?", "why am I missing / seeing too few exposures?", "why does the bias banner show?", or "why don't PostHog's numbers match my SQL?". Pulls the experiment's real data read-only, matches it to a known-cause catalog, and produces a customer-facing explanation, fix, and review of the pertinent numbers. Loads diagnosing-experiment-results as its deep diagnostic library. DO NOT TRIGGER when: creating an experiment (use creating-experiments), only configuring rollout (configuring-experiment-rollout) or metrics (configuring-experiment-analytics), asking lifecycle questions (managing-experiment-lifecycle), or the underlying feature flag is what's misbehaving rather than the results (use debugging-feature-flags).

io.github.PostHog/posthog/turning-engineering-analytics-into-insights

Converts engineering analytics (PR / CI) data into saved PostHog insights, dashboards, and subscriptions, and explains how to query the product data directly with SQL. Covers discovering per-team GitHub warehouse tables via engineering-analytics-sources, replicating curated column semantics in HogQL, reading exposed engineering_analytics_* warehouse views where product logic is involved (CI cost, fingerprinted failure lines, commit attribution), saving queries with insight-create, and scheduling delivery with subscriptions-create. Use when asked to "save this as an insight", "put CI health / merge times on a dashboard", "email me PR throughput weekly", "chart CI cost", "track time to first review", "subscribe to these numbers", "alert on CI success rate", or "what data/tables/views does engineering analytics read". For ad-hoc CI and merge questions use diagnosing-ci-and-merge-bottlenecks; to investigate one specific CI failure use investigating-ci-failures.