Assesses whether branch or PR changes are high-risk for externally hosted or embedded Streamlit usage and recommends whether external e2e coverage with `@pytest.mark.external_test` is needed. Use during code review, PR triage, or test planning when changes touch routing, auth, websocket/session behavior, embedding, assets, cross-origin behavior, SiS/Snowflake runtime, storage, or security headers.
npx skills add https://github.com/streamlit/streamlit --skill assessing-external-test-risk
Use this skill to decide whether a branch or PR should include external e2e coverage using @pytest.mark.external_test.
This helps protect deployments that commonly involve proxies, embedded iframe contexts, CSP constraints, and other browser security boundaries.
This skill is for risk assessment and recommendation. It does not auto-mark tests unless explicitly requested.
Use an any-hit policy:
Evaluate all categories. A single hit is enough to recommend external coverage.
server.baseUrlPath, catch-alls, request methods, URL resolution, redirects, or status codes._streamlit_user, _streamlit_xsrf, CSRF/XSRF handling, server.trustedUserHeaders, or session-to-identity binding.postMessage), iframe sizing or resize behavior, iframe sandbox or allow attributes, or permissions policy behavior in embedded contexts.server.customComponentBaseUrlPath), or proxying rules for static/component assets.crossOrigin usage, external-origin fetches or external networks behavior, or backend URL discovery via window.__streamlit.*.running_in_sis(), get_active_session(), Snowflake connection/session semantics, or SiS-specific environment flags.10. Client storage behavior
localStorage, or sessionStorage usage that may differ in embedded or third-party contexts.11. Security headers and browser policies
Use this exact structure:
## External test recommendation
- Recommend external_test: [Yes/No]
- Triggered categories: [List category numbers and names, or "None"]
- Evidence:
- `<path>`: [short reason from diff]
- `<path>`: [short reason from diff]
- Suggested external_test focus areas:
- [Concrete scenario to validate externally]
- [Concrete scenario to validate externally]
- Confidence: [High/Medium/Low]
- Assumptions and gaps: [Unknowns, missing context, or why confidence is reduced]
Diff includes:
lib/streamlit/web/server/starlette/starlette_routes.py route changesallow attributesExpected output:
Recommend external_test: YesDiff includes:
Expected output:
Recommend external_test: NoNoneToolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.
Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup
Use when implementing any feature or bugfix, before writing implementation code
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always
Expert guidance for systematic backtesting of trading strategies. Use when developing, testing, stress-testing, or validating quantitative trading strategies. Covers "beating ideas to death" methodology, parameter robustness testing, slippage modeling, bias prevention, and interpreting backtest results. Applicable when user asks about backtesting, strategy validation, robustness testing, avoiding overfitting, or systematic trading development.
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
Take streamlit/assessing-external-test-risk from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.