Ponytail: Agent Skill Against Overengineered AI Code

X postMiles CranmerTool

Miles Cranmer highlights the open-source repository 'ponytail' as an effective prompt-based skill to prevent AI coding agents from generating bloated solutions. The project guides agents towards minimal code and native platform features instead of redundant dependencies.

Key points

  1. Miles Cranmer endorses ponytail as a highly effective prompt setup to curb agent-generated 'slop' and overengineering.
  2. The tool uses a decision ladder forcing the agent to check alternatives before writing code: skip if unnecessary (YAGNI), reuse existing codebase logic, use the standard library, or adopt native platform features (e.g., standard date inputs instead of full component libraries).
  3. Benchmarks run on headless Claude Code sessions (Haiku 4.5) on a real FastAPI and React project show an average 54% reduction in lines of code (up to 94% on tasks like pickers), 20% lower costs, and 27% faster completions with 100% safety maintained.
  4. The repository emphasizes that code is minimal rather than 'golfed', preserving validation, security boundaries, and accessibility.
  5. Cranmer notes the repository has expanded over time and recommends focusing on core files or complementary skills like 'architect', 'blast-radius', and 'create-verification-skill'.

Why it matters

AI coding assistants frequently over-build features by adding boilerplate wrappers and external dependencies where simple native APIs would suffice. Well-engineered operational rules and skills can dramatically reduce code bloat, token consumption, and runtime costs without sacrificing reliability.

Analysis

The project tackles a prominent failure mode of LLM agents: the bias toward excessive code generation. By imposing a strict ladder of laziness that prioritises existing code and native capabilities over novel abstractions, ponytail achieves measurable efficiency gains while maintaining essential safety checks across agentic workflows.

Original post

Miles Cranmer

@MilesCranmer · 4 September 2026

@gshaikovski This thing! t.co/EQkN3YxKSt Basically a really good prompt for slop-generating agents

428 likes8 replies1.5k bookmarks47.5k views

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More posts in the thread (6)
  1. Before ponytail skill / After ponytail skill It's crazy how much prompt engineering still helps t.co/alnSJhd4I0
  2. @gshaikovski Ironically the repo has gotten bloated but this is the key file to install: t.co/jLaS5Bg4jv
  3. @mikaackermann9 Yes
  4. @audrow Looks interesting, I'm checking it out. Is the analogous skill this one? t.co/BombCDPkqs
  5. @audrow oh it is like a whole directory of skills/routines?
  6. @audrow What are the best skills inside it? I'm trying to filter it down (I already have too many skills installed...). According to an agent comparing the list with my own usage patterns, it sounds like 'create-verification-skill', 'blast-radius', and 'architect' would be most useful.
Selected replies (5)
  • @mrnoncr @MilesCranmer That's so bad. No one even uses native date input. Bad example t.co/IxV7WcWdUJ
  • @xstibo @MilesCranmer can not recommend, just use a better model
  • @mikaackermann9 @MilesCranmer Would you recommend this to all software engineeres?
  • @gena115592 @MilesCranmer quite impressive, though I am a bit skeptical about using it. Does it negatively affect the raw reasoning of the model, or say, due to this skill, does the model perform sub-optimally?
  • @johanzargu @MilesCranmer Functionally, they aren’t the same codes, so Ponytail broke it🤷‍♂️

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Summary generated by AI (Gemini 3.8 Flash, 27 September 2026). It may contain errors – the original source is authoritative.

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