Best of Prompt Engineering — April 2026

  1. 1
    Video
    Avatar of philipplacknerPhilipp Lackner·4w

    3 Theoretical Limits of AI - These Things Can't Be Fixed

    A critical look at three fundamental, unfixable limitations of current LLM-based AI: (1) the learning ceiling problem — LLMs can't exceed the collective intelligence of their training data, especially as AI-generated content pollutes future training sets; (2) hallucination as an architectural inevitability — the same mechanism that enables creativity also produces confident incorrect outputs, and these can't be separated; (3) the frame problem — LLMs operate strictly within the context given to them and lack the ability to reframe a problem the way an experienced developer would. The author argues the truth lies between AI replacing developers and AI being useless, and that developers who understand these limits and use AI skillfully will gain a real productivity edge.

  2. 2
    Article
    Avatar of udhamugjdzaay9lointosAngel Santiago·5w

    Stop Prompting: Use the Design-Log Method to Build Tools Predictably and Reliably

    The Design-Log Methodology addresses the 'context wall' problem in AI-assisted development by maintaining a version-controlled ./design-log/ folder with markdown documents capturing design decisions before any code is written. A practitioner shares how adopting this approach transformed their cybersecurity tool development workflow: instead of large prompts and back-and-forth corrections, they write a design log first, have the AI ask clarifying questions, freeze the design before implementation, and log any deviations. Four core rules guide the process: read before you write, design before implementation, immutable history, and Socratic questioning. The result is more reliable, auditable, and architecturally consistent AI-generated code, especially valuable when building security-sensitive tools.

  3. 3
    Article
    Avatar of bartwullemsThe Art of Simplicity·6w

    Awesome GitHub Copilot just got awesommer (if that’s a word)

    The Awesome GitHub Copilot repository, a community hub for custom instructions, prompts, agents, and chat modes, now has a dedicated website and Learning Hub. The site at awesome-copilot.github.com offers full-text search across 175+ agents, 208+ skills, 176+ instructions, and more, with category filters, modal previews, and one-click installs into VS Code. The Learning Hub explains core concepts like agents, skills, hooks, and plugins. The plugin system lets users bundle related agents and skills into installable packages, and Awesome GitHub Copilot is now a default plugin marketplace for GitHub Copilot CLI and VS Code.

  4. 4
    Article
    Avatar of thomasthorntonThomas Thornton·4w

    What Makes a Good GitHub Copilot Agent Skill?

    Designing effective GitHub Copilot agent skills requires more than writing good documentation. The key design choices include: crafting precise YAML description fields that mirror real user phrasing and include explicit negative scope (what the skill is NOT for); keeping the skill body lean using progressive disclosure with references/ directories for detail; explaining reasoning behind instructions rather than issuing blunt rules; testing trigger activation against messy real-world prompts; and bundling reusable assets for consistency. Skills should be treated like microservices — clear boundaries, predictable behavior when coexisting with other skills. The best skills are derived from workflows that already succeeded in practice, capturing reusable decision logic rather than one-off specifics.

  5. 5
    Article
    Avatar of minersThe Miners·5w

    Stop Putting Best Practices in Skills

    A data-driven investigation into why best practices should live in CLAUDE.md rather than Claude Code skills. The author ran 51 multi-turn evals across 4 configurations (Superpowers, plain skills, CLAUDE.md, CLAUDE.md+hint) and found that plain skills are only invoked 6% of the time in multi-turn sessions, while CLAUDE.md guidelines are always in context. The key insight: skills and CLAUDE.md are both just prompts — the difference is reliability of delivery. Superpowers works not because of skills but because its SessionStart hook front-loads instructions, achieving 66% invocation. The recommendation is clear: put coding standards, TDD rules, and debugging protocols in CLAUDE.md (100% presence, no activation gap), and reserve skills for on-demand procedural recipes like scaffolding or migrations.

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