Testing & Security skills

How to author, edit, and adapt PostHog Signals scouts — the scheduled agents that scan a project and emit findings into the Signals inbox. Use when a user wants to customize a canonical scout for their own setup (narrow its scope, retune its thresholds, add disqualifiers), tweak a scout's schedule or dry-run posture, or write a brand-new scout from scratch for a specific use case (a custom event, a product surface no canonical scout covers). Covers the scout SKILL.md anatomy, the emit contract, the dedupe + scratchpad-memory conventions, the per-team skills-store path vs the canonical in-repo path, and the emit-and-inspect test loop (with dry-run as an optional safety net). Trigger on "write/edit/customize a signals scout", "new scout for X", "tune my scout schedule", "make a scout that watches <event>".

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Analyze a repository to generate recommended Claude Code settings.json permissions. Use when setting up a new project, auditing existing settings, or determining which read-only bash commands to allow. Detects tech stack, build tools, and monorepo structure.

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Configures the rollout shape of a PostHog experiment — the variant split (50/50, 80/20, A/B/C ratios), the overall rollout percentage that gates how many users enter the experiment, and the disambiguation when a percentage like "roll out to 25%" could mean either. Use when the user mentions a rollout percentage, variant split, or traffic distribution; gives a ratio like 60/40, 70/30, or 80/20; asks "who sees the test variant?"; wants to increase, decrease, or change the rollout or split on a draft or running experiment; weighs equal vs uneven splits; or proposes a mid-experiment split change (often an anti-pattern that needs reset or end-and-restart).

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Audit and optimize application performance, including bundle size, rendering, database queries, and Core Web Vitals.

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Author useful, low-noise log alerts on services in a PostHog project. Use when the user asks to set up alerts for their logs, suggest alerts they should add, or evaluate whether a service is worth monitoring. Covers service triage, baseline characterisation, threshold drafting, back-testing via simulate, and shipping with a notification destination.

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Set up Playwright end-to-end testing in a project, including test configuration, example tests, and CI integration.

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Start the dev server, discover API routes from the codebase, hit every endpoint, and report which ones return errors.

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Tests associations between categorical variables in clinical data using chi-square, Fisher's exact, Boschloo, Cochran-Mantel-Haenszel, and modern McNemar variants with calibrated confidence intervals (Wilson, Newcombe, Miettinen-Nurminen). Use when analyzing categorical outcomes, paired binary endpoints, or testing treatment-outcome independence in confirmatory or exploratory clinical trials.

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Controls error rates across thousands of simultaneous tests in genomics discovery using false-discovery-rate methods (Benjamini-Hochberg 1995; Benjamini-Yekutieli 2001 for arbitrary dependence; Storey q-value with pi0 estimation; local FDR; independent filtering Bourgon 2010; covariate-weighted FDR via IHW Ignatiadis 2016), plus family-wise error control (Bonferroni, Holm) and the GWAS genome-wide threshold. Covers the FDR-versus-FWER choice as the discovery-versus-confirmatory distinction, the dependence assumptions behind BH (PRDS) versus BY, pi0 estimation, the independent-filtering and false-coverage-rate traps, and reproducibility ranking via IDR (Li 2011). Use when correcting p-values from genome-wide tests, choosing between BH/BY/q-value/Bonferroni, setting an FDR threshold, applying IHW or independent filtering, or interpreting q-values. For confirmatory trials with few pre-specified endpoints (closed testing, graphical/gatekeeping), see clinical-biostatistics/multiplicity-graphical.

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Diagnostic test / biomarker accuracy — sensitivity, specificity, PPV, NPV, likelihood ratios, accuracy from a 2x2 table; ROC curve, AUC, and the optimal cutoff (Youden) for a continuous biomarker; and post-test probability via Bayes. Use when you have test results vs a gold standard (binary 2x2, or a continuous score + true labels) and need to judge how good the test is, pick a threshold, or compute the probability of disease given a result. Emphasizes the prevalence-dependence of PPV/NPV.

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Comprehensive ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) profiling for drug candidates. Integrates ADMET-AI predictions, SwissADME drug-likeness, PubChemTox experimental toxicity, ChEMBL clinical data, Lipinski rule-of-five, and CYP interaction data. Use for drug-likeness assessment, BBB penetration, bioavailability, hepatotoxicity prediction, ADME/PK profiling, or screening compound libraries before lab testing.

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Validates docked / generated protein-ligand poses using PoseBusters physical-validity tests, strain energy quantification, geometric checks (planarity, vdW overlap, bond/angle distortion), and pose-energy reasonableness. Filters AI-docking outputs (DiffDock, EquiBind, NeuralPLexer) where ~50% of poses fail physical-validity tests. Use when QC-ing docking results, comparing classical vs ML docking outputs, or filtering pose lists before SAR analysis.

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Analyzes chemical libraries by scaffold using Bemis-Murcko scaffolds, generic frameworks, cyclic skeletons, matched molecular pair (MMP) analysis via mmpdb, R-group decomposition, Free-Wilson analysis, scaffold hopping, and chemotype-aware ML train/test splits. Use when identifying chemotype clusters in a library, deriving SAR transformation rules, decomposing series into R-groups, performing scaffold-balanced QSAR splits, or planning analog campaigns.

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Automated LLM-driven hypothesis generation and testing on tabular datasets. Use when you want to systematically explore hypotheses about patterns in empirical data (e.g., deception detection, content analysis). Combines literature insights with data-driven hypothesis testing. For manual hypothesis formulation use hypothesis-generation; for creative ideation use scientific-brainstorming.

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Authoritative resource for building, auditing, and improving GNOME GTK4/libadwaita apps. Design tokens, 42-widget component library, 21 pattern skills, 47 mirrored HIG pages, 34 real-app audits, and lint scripts. Use when building a GNOME app, adding GNOME GUI features, auditing HIG compliance, packaging for Flatpak/Flathub, or when the user mentions GTK, libadwaita, Adw, Blueprint, or GNOME HIG.

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Audit a GTK4/libadwaita app for GNOME HIG compliance across 12 categories (text, icons, layout, shortcuts, accessibility, color, dialogs, responsiveness, CSS, GSettings, i18n), then fix violations category by category. Use when asked to audit, review, polish, or "make HIG-compliant" a GNOME app, or before a release/Flathub submission.

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Make a GTK4/libadwaita app accessible — accessible labels and roles, tooltips on icon-only buttons, keyboard navigation, contrast, and testing with Orca/Accerciser. Use when fixing a11y issues or running the accessibility category of a HIG audit.

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Package a GNOME app as a Flatpak — manifest for the GNOME runtime, sandbox permissions, build/run/test loop, and Flathub submission checklist. Use when packaging, distributing, or preparing a GNOME app for Flathub.

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Completes the online SBTI personality test at sbti.fancc.de5.net, answers all single-choice questions using the model's default personality and stable behavioral tendencies, submits the test, and returns the final type, match percentage, result description, and fifteen-dimension scores. Use when the user asks to take the SBTI test, run the SBTI quiz, get an SBTI result, or complete that personality test website for them.

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Use every time you push a commit to an active pull request, to verify the PR is truly stable before reporting merge readiness. Covers the post-push wait window, AI review audit (Copilot/Greptile/Codex), and the gh commands to snapshot PR state. Trigger on "I pushed" or "is the PR ready to merge".

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Stryker-driven test cull: delete slop, consolidate redundants, fill mutation-coverage gaps. Score up, test count and lines down. Use when the user says 'trim tests', '/trim-tests', or wants to clean up LLM-generated test bloat. No args picks the worst-scored file; argument scopes to a section, service, or file.

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Run browser-based smoke tests against the running Humans site using the Claude Code Chrome extension. Argument: all | smoke | auth | profile | consent | teams | admin | gdpr | i18n | [url]

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Promote QA → production by opening a PR from peterdrier/Humans:main to nobodies-collective/Humans:main with a properly-qualified commit summary. Use when the user says "PR to production", "promote to prod", "PR from origin to upstream", "prod PR", or any variation of pushing batched fork changes upstream. Always use this skill for the two-remote promotion flow even if the user doesn't say "prod" explicitly.

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Three-axis orchestrator: (1) clean section boundaries — naming, routes, views, ViewModels, DB ownership, cross-section access; (2) internal cohesion — no EF in service layer, caching in service only, proper interfaces, reusable ViewComponents, architecture-test coverage; (3) focused tests — grouped under the section, covering invariants/negatives/triggers, pruned of redundancy. Push-and-bot-review loop. Use when a sizable PR landed, a section shows arch drift, or /pr-review returned a non-trivial violation list. Camps is the gold-standard reference.

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