Use when long-lived agents, project memories, system prompts, or playbooks are repeatedly summarized, rewritten, or optimized and risk losing domain detail, provenance, or execution learnings.
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Discovers all projects in a .NET solution, classifies each for Oracle-to-PostgreSQL migration eligibility, and produces a persistent master migration plan. Use at the start of an Oracle-to-PostgreSQL migration of a multi-project .NET solution.
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安装前可以查看交给 AI 代理的指令内容。
Attribution: Sourced from github/awesome-copilot by GitHub Community, licensed MIT. Modified for this collection.
Analyze a .NET solution, classify every project for Oracle→PostgreSQL migration eligibility, and write a structured plan that downstream agents and skills can parse.
Progress:
- [ ] Step 1: Discover projects in the solution
- [ ] Step 2: Classify each project
- [ ] Step 3: Confirm with user
- [ ] Step 4: Write the plan file
Step 1: Discover projects
Find the Solution File (it has a .sln or .slnx extension) in the workspace root (ask the user if multiple exist). Parse it to extract all .csproj project references. For each project, note the name, path, and type (class library, web API, console, test, etc.).
Step 2: Classify each project
Scan every non-test project for Oracle indicators:
Oracle.ManagedDataAccess, Oracle.EntityFrameworkCore (check .csproj and packages.config)appsettings.json, web.config, app.configOracleConnection, OracleCommand, OracleDataReader.github/oracle-to-postgres-migration/DDL/Oracle/ (if present)Assign one classification per project:
| Classification | Meaning |
|---|---|
| MIGRATE | Has Oracle interactions requiring conversion |
| SKIP | No Oracle indicators (UI-only, shared utility, etc.) |
| ALREADY_MIGRATED | A -postgres or .Postgres duplicate exists and appears processed |
| TEST_PROJECT | Test project; handled by the testing workflow |
Step 3: Confirm with user
Present the classified list. Let the user adjust classifications or migration ordering before finalizing.
Step 4: Write the plan file
Save to: .github/oracle-to-postgres-migration/Reports/Master Migration Plan.md
Use this exact template — downstream consumers depend on the structure:
# Master Migration Plan
**Solution:** {solution file name}
**Solution Root:** {REPOSITORY_ROOT}
**Created:** {timestamp}
**Last Updated:** {timestamp}
## Solution Summary
| Metric | Count |
|--------|-------|
| Total projects in solution | {n} |
| Projects requiring migration | {n} |
| Projects already migrated | {n} |
| Projects skipped (no Oracle usage) | {n} |
| Test projects (handled separately) | {n} |
## Project Inventory
| # | Project Name | Path | Classification | Notes |
|---|---|---|---|---|
| 1 | {name} | {relative path} | MIGRATE | {notes} |
| 2 | {name} | {relative path} | SKIP | No Oracle dependencies |
## Migration Order
1. **{ProjectName}** — {rationale, e.g., "Core data access library; other projects depend on it."}
2. **{ProjectName}** — {rationale}
Order projects so that shared/foundational libraries are migrated before their dependents.
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Use when long-lived agents, project memories, system prompts, or playbooks are repeatedly summarized, rewritten, or optimized and risk losing domain detail, provenance, or execution learnings.
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Use this skill when the user explicitly asks to map, document, or onboard into an existing codebase. Trigger for prompts like "map this codebase", "document this architecture", "onboard me to this repo", or "create codebase docs". Do not trigger for routine feature implementation, bug fixes, or narrow code edits unless the user asks for repository-level discovery.
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Adds educational comments to a code file so it becomes a learning resource, asking for the file (with a list of close matches) if none is given. Use when preparing code for learners, onboarding, or teaching.
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Write, debug, and optimize Adobe Illustrator automation scripts using ExtendScript (JavaScript/JSX). Use when creating or modifying scripts that manipulate documents, layers, paths, text frames, colors, symbols, artboards, or any Illustrator DOM objects. Covers the complete JavaScript object model, coordinate system, measurement units, export workflows, and scripting best practices.
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Patterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when: - Building AI agents that call external tools (APIs, databases, file systems) - Implementing policy-based access controls for agent tool usage - Adding semantic intent classification to detect dangerous prompts - Creating trust scoring systems for multi-agent workflows - Building audit trails for agent actions and decisions - Enforcing rate limits, content filters, or tool restrictions on agents - Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)
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Check any AI agent codebase against the OWASP Agentic Security Initiative (ASI) Top 10 risks. Use this skill when: - Evaluating an agent system's security posture before production deployment - Running a compliance check against OWASP ASI 2026 standards - Mapping existing security controls to the 10 agentic risks - Generating a compliance report for security review or audit - Comparing agent framework security features against the standard - Any request like "is my agent OWASP compliant?", "check ASI compliance", or "agentic security audit"
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