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@@ -145,6 +145,7 @@ See [CONTRIBUTING.md](../CONTRIBUTING.md#adding-skills) for guidelines on how to
| [csharp-nunit](../skills/csharp-nunit/SKILL.md)<br />`gh skills install github/awesome-copilot csharp-nunit` | Get best practices for NUnit unit testing, including data-driven tests | None |
| [csharp-tunit](../skills/csharp-tunit/SKILL.md)<br />`gh skills install github/awesome-copilot csharp-tunit` | Get best practices for TUnit unit testing, including data-driven tests | None |
| [csharp-xunit](../skills/csharp-xunit/SKILL.md)<br />`gh skills install github/awesome-copilot csharp-xunit` | Get best practices for XUnit unit testing, including data-driven tests | None |
| [daily-focus-board](../skills/daily-focus-board/SKILL.md)<br />`gh skills install github/awesome-copilot daily-focus-board` | Spin up a personal, motivating daily focus board that renders in a browser canvas and that the user drives by talking to their AI partner. Tasks track status (to-do → in progress → done) with timestamped progress notes and roll up into a "today's momentum" feed; numeric-goal tasks (pages, pomodoros, reps) render as progress-bar counters. Executive-function / neurodivergent-friendly by design: Focus mode, kind "not today" carryover (no overdue-shaming), a brain-dump box, reduced-motion, and gentle deadline countdowns. Add, reorder, and relabel tasks live, assign Eisenhower priority (Do first / Schedule / Delegate / Later), open with an above/below-the-line check-in and a daily mantra, and save an end-of-day recap. Use when someone wants to plan their day, stay focused, kick off a work session, or track progress. Progress persists in the browser (localStorage). | `assets/board.template.html`<br />`examples`<br />`references/customize.md`<br />`references/neurodivergent-design.md`<br />`references/tutorial.md`<br />`scripts/serve-board.ps1` |
| [daily-prep](../skills/daily-prep/SKILL.md)<br />`gh skills install github/awesome-copilot daily-prep` | Prepare for tomorrow's meetings and tasks. Pulls calendar from Outlook via WorkIQ, cross-references open tasks and workspace context, classifies meetings, detects conflicts and day-fit issues, finds learning and deep-work slots, and generates a structured HTML prep file with productivity recommendations. | None |
| [data-breach-blast-radius](../skills/data-breach-blast-radius/SKILL.md)<br />`gh skills install github/awesome-copilot data-breach-blast-radius` | Pre-breach impact analysis: inventories sensitive data (PII, PHI, PCI-DSS, credentials), traces data flows, scores exposure vectors, and produces a regulatory blast radius report with fine ranges sourced verbatim from GDPR Art. 83, CCPA § 1798.155(a), and HIPAA 45 CFR § 160.404. Cost benchmarks from IBM Cost of a Data Breach Report (annually updated). All citations in references/SOURCES.md for verification. Use when asked: "assess breach impact", "what data could be exposed", "calculate blast radius", "data exposure analysis", "how bad would a breach be", "quantify data risk", "sensitive data inventory", "data flow security audit", "pre-breach assessment", "worst-case breach scenario", "breach readiness", "data risk report", "/data-breach-blast-radius". For any stack handling user data, health records, or financial information. Output labels law-sourced figures (exact) vs heuristic estimates (planning only). Does not replace legal counsel. | `references/SOURCES.md`<br />`references/blast-radius-calculator.md`<br />`references/data-classification.md`<br />`references/hardening-playbook.md`<br />`references/regulatory-impact.md`<br />`references/report-format.md` |
| [datanalysis-credit-risk](../skills/datanalysis-credit-risk/SKILL.md)<br />`gh skills install github/awesome-copilot datanalysis-credit-risk` | Credit risk data cleaning and variable screening pipeline for pre-loan modeling. Use when working with raw credit data that needs quality assessment, missing value analysis, or variable selection before modeling. it covers data loading and formatting, abnormal period filtering, missing rate calculation, high-missing variable removal,low-IV variable filtering, high-PSI variable removal, Null Importance denoising, high-correlation variable removal, and cleaning report generation. Applicable scenarios arecredit risk data cleaning, variable screening, pre-loan modeling preprocessing. | `references/analysis.py`<br />`references/func.py`<br />`scripts/example.py` |