Test design, vulnerability assessment, and quality assurance (13K skills)
Master Bash Automated Testing System (Bats) for comprehensive shell script testing. Use when writing tests for shell scripts, CI/CD pipelines, or requiring test-driven development of shell utilities.
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How to find and fix performance problems in Shift's desktop app and its Rust/WebAssembly engine. Use when something is slow, choppy, janky, or laggy (scrubbing, dragging, editing, undo, opening fonts), when an engine, store or WASM boundary change could cost time or memory on large fonts, when profiling or measuring, when adding or reviewing perf tests, and before claiming a change made something faster.
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Canonical rules for writing tests in the Shift codebase. Use whenever you add, rewrite, or review a `.test.ts` or `.spec.ts` file — or any time you're about to mock, stub, or spy your way around a testing problem. Covers TestEditor-based tests and desktop E2E tests driven through EditorDriver.
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Canonical rules for writing, rewriting, or reviewing Shift desktop Playwright E2E tests and visual goldens under `apps/desktop/e2e/`. Use whenever you add or change a `.spec.ts`, a fixture, `EditorDriver`, a screenshot baseline, or Playwright project membership, and whenever you investigate a flaky or failing E2E test. Covers waits, oracles, goldens, fixtures, projects, and flake verification.
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Adversarially fact-check DOCS.md files against the actual source code, verifying every concrete claim rather than trusting structure checks. Use when the user asks to audit docs, verify documentation accuracy, check whether docs are still true, or on a scheduled documentation review. This is the semantic layer the mechanical checkers cannot cover.
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Technical due diligence for M&A, investment, or acquisition. Reads a target company's codebase and generates a comprehensive tech DD report with architecture assessment, tech debt quantification, scalability analysis, security posture, team capability inference, build system quality, test coverage, deployment maturity, and open source license risks. Outputs tech-dd-report.md formatted like a real investment memo with risk ratings, remediation costs, and go/no-go recommendation.
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Use to run real static analysis over a diff or repo before shipping — Semgrep, CodeQL, secret scanning, dependency CVEs — and triage the findings into what must be fixed now versus noise. Trigger before a release or PR merge, during a security review, or when the user asks to scan the code for vulnerabilities.
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Answers business questions about the user's own spreadsheet or data export (CSV, TSV, XLSX from a CRM, Shopify, Stripe, QuickBooks, ad platforms, HR or payroll systems) correctly and auditably. Profiles every file first (grain, duplicate keys, embedded Grand Total rows, text-stored numbers, mixed currencies, UTC timestamps, Excel serial dates, hidden rows), states the metric definition used, computes every number in SQL or pandas instead of mental math, checks joins for fan-out, reconciles to a known total, and returns the query, filters, row counts, and rows behind the answer. Use this whenever the user says "analyze this spreadsheet", "how many...", "what's the total in this export", "which customers...", "pivot this", "revenue by month", "top 10", "what's our churn/MRR/AOV", or asks any question about their data, CSV, or Excel file, even a quick one. Also use when they ask to change values in a data file.
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Audits business spreadsheet models (.xlsx, .xlsm, or Google Sheets exported to .xlsx) for structural integrity errors and explains each fix -- budgets, forecasts, pricing models, commission calculators, FP&A packs, ops trackers, not only banking models. A bundled openpyxl script finds hardcoded numbers inside formulas, typed values pasted over formulas, inconsistent formulas across a row or column,
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Analyze test coverage gaps and generate tests to improve coverage. Use when improving test coverage, finding untested code, or writing missing tests.
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Plans security penetration tests for web applications. Analyzes codebase, API routes, auth implementation, and infrastructure config to generate comprehensive pentest plans. For authorized testing only.
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Simulate a panel of your real prospects and buyers to pressure-test sales and marketing before it goes out — cold emails, pitch decks, landing pages, pricing pages, demo scripts, proposals. Each simulated prospect reacts in character, raises the objection they'd actually raise, and tells you whether they'd reply, book, or ghost. Use to de-risk outbound and messaging without burning real leads.
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Uses Managed Agents' 14.5-hour runtime to audit an entire codebase overnight. Security, performance, accessibility, dependency issues. You wake up to a full report.
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Uses Agent SDK to deploy 3 parallel agents for client onboarding -- workflow auditor, tech stack mapper, and strategy drafter. Real consulting workflow that produces a complete client assessment.
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Audit a codebase for LLM calls that are really classifications in disguise, then produce a costed swap plan for a System One model. Use when asked to cut AI inference cost or latency, when scoping a performance engagement for a client, when reviewing an agent loop that feels slow, or when asked "where could we use Jev here". Produces a ranked table of candidates with measured latency and dollar deltas.
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Take any claim and design + execute a complete research protocol to test it. Apply scientific method automatically: design study, gather data, run analysis, provide verdict with confidence level.
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Argues the strongest honest case against a decision, plan, belief, hire, pricing move, strategy, or piece of work the user is leaning toward. Steelmans the other side, ranks at most three objections by consequence, runs a short pre-mortem, credits what the user got right, and ends with what would have to be true for their choice to work. Calibrated and collegial, never combative; says plainly when the position is strong. Use whenever the user says "play devil's advocate", "argue the other side", "poke holes in this", "what am I missing", "am I making a mistake", "talk me out of it", "stress-test this idea", "tell me why this is a bad idea", or asks for pushback on something they have mostly decided. Supports gentle, standard, and hard intensity. Not for interrogating requirements before a build (grill-the-brief) or staging a multi-persona debate (debate-simulator).
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Audit npm dependencies for security vulnerabilities, outdated packages, and unused dependencies. Use when checking for security issues, updating packages, or cleaning up dependencies.
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Point Cowork at a folder of medical bills and insurance EOBs -- matches every bill to its explanation of benefits, catches duplicate charges, balance-billing, and bills that ignore your insurance, then drafts the dispute letters and phone scripts. Most medical bills contain errors; this finds them.
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Cowork-style sweep of a folder of receipts, statements, and expense exports -- categorizes every transaction, matches receipts to statement lines, flags policy violations and anomalies, and outputs a clean expense report plus a findings memo.
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Use to actually verify a web change in a real browser instead of assuming it works — click through a flow, catch console errors, check a form submits, confirm a fix on the page. Trigger when a UI change needs proof, a bug only reproduces in the browser, or the user asks to test, check, or screenshot the running app.
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Writes, explains, debugs, and optimizes BI calculations - Power BI / Fabric DAX measures and calculated columns, Tableau calculated fields (FIXED/INCLUDE/EXCLUDE LOD expressions, table calculations), and Looker LookML measures, dimensions, and derived tables. Use it whenever the request involves DAX, CALCULATE, a Power BI measure, a Tableau calc or LOD, LookML, or a business metric such as YoY, YTD, rolling 12 months, running total, percent of total, distinct customers, ranking, new vs returning customers, closing balances, or basket analysis. Use it especially when someone says a measure "shows the wrong total", "is blank", "ignores my slicer/filter", "repeats the same value on every row", or "is slow" - those are filter context, context transition, LOD order-of-operations, or fanout bugs this skill is built to diagnose. Produces the formula, the context it evaluates in, and a hand-checkable test with expected numbers.
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Assesses how ready a business is for AI adoption across six dimensions. Evaluates data maturity, tech stack, team skills, process documentation, budget, and culture. Generates a comprehensive ai-readiness-report.md with scores, gap analysis, and recommended starting points. Aligned with OneWave AI's audit methodology.
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Audit websites for accessibility issues and WCAG compliance. Use when checking accessibility, fixing a11y issues, or ensuring WCAG compliance.
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