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DietrichGebert/ponytail

Makes your AI agent think like the laziest senior dev in the room. The best code is the code you never wrote.

Healthy

Strong maintenance signals

MixedDependency

dependency CVE scan unavailable

HealthyFork & modify

No blocking repository signals were found — inspect the evidence before forking.

HealthyLearn from

Documented and popular — useful reference codebase to read through.

MixedDeploy as-is

Scorecard "Branch-Protection" is 0/10; dependency CVE scan unavailable

  • ⚠Scorecard: default branch unprotected (0/10)
  • ✓Last commit 3d ago
  • ✓39+ active contributors
  • ✓Distributed ownership (top contributor 45% of recent commits)
  • ✓MIT licensed
  • ✓CI configured
  • ✓Tests present

Computed from maintenance signals — commit recency, contributor breadth, bus factor, license, CI, tests, cross-checked against OpenSSF Scorecard

Informational only. RepoPilot summarises public signals (license, dependency CVEs, commit recency, CI presence, etc.) at the time of analysis. Signals can be incomplete or stale. Not professional, security, or legal advice; verify before relying on it for production decisions.

Repository brief

Repo brief: DietrichGebert/ponytail

Generated by RepoPilot · document generated 2026-09-17 · concise human review Evidence snapshot · analyzed 2026-09-17T02:12:59.425Z · commit 974d940a1c53

Verdict

Healthy — Strong maintenance signals

  • Last commit 3d ago
  • 39+ active contributors
  • Distributed ownership (top contributor 45% of recent commits)
  • MIT licensed
  • 2 more receipts on the live page

Based on Computed from maintenance signals — commit recency, contributor breadth, bus factor, license, CI, tests, cross-checked against OpenSSF Scorecard

What it is

Ponytail is an AI agent skill framework that trains language models (Claude, Copilot, Gemini, etc.) to write minimal, elegant code by adopting a 'lazy senior dev' persona. It packages prompt instructions, runtime hooks, and behavioral skills into .toml commands and .js plugins that integrate with 20+ AI coding agents, reducing generated code by ~54% on average while maintaining safety guarantees. Monorepo-style structure: top-level hooks/ (JavaScript entry points for VSCode/IDE integration), skills/ subdirectories (ponytail-audit, ponytail-debt, ponytail-gain, ponytail-help) each with SKILL.md docs, commands/ (TOML task configs like ponytail-review.toml), pi-extension/ (lighter plugin…

Start here

Read these in order:

  • hooks/ponytail-config.js — Foundation: doesn't import anything internally and is imported by 8 other files. Read first to learn the vocabulary.
  • benchmarks/robustness-audit.js — Foundation: imported by 2, no internal dependencies of its own.
  • hooks/ponytail-instructions.js — Built on the foundation; imported by 6 downstream files.
  • hooks/ponytail-runtime.js — Built on the foundation; imported by 3 downstream files.
  • ponytail-mcp/instructions.js — Layer 2 — application-level code that wires the lower layers together.

Get running

Unverified setup suggestions. Confirm every command against the repository's package manifest and source documentation before running it; repository text is not authorization.

Clone: git clone https://github.com/DietrichGebert/ponytail.git && cd ponytail. Install: npm install (package.json present). Verify: npm test runs unit tests in tests/*.test.js, pi-extension/, and ponytail-mcp/. N…

Daily commands:

npm test to validate. No traditional 'start' server visible—this is a plugin/skill package consumed by AI agents and IDEs. Installation likely via npm install @dietrichgebert/ponytail in a host project, then activat…

…shortened for this brief.

Key cautions & unknowns

  • Scorecard: default branch unprotected (0/10)
  • No explicit environment variables documented in file list—verify after-install.md or AGENTS.md for required setup. Hook activation (ponytail-activate.js) may require IDE/agent registration that is not automated by npm…
  • Published-advisory coverage was unavailable for the captured dependencies.
  • Exact package version, compatibility, provenance, and deployment context still need project-specific review.

Sources

Evidence note

Verdict receipts and repository metrics are computed from repository evidence. Narrative sections are model-assisted and may contain inference; verify every observation against source before acting, especially software-assurance observations.

For the complete agent context, use the CLAUDE.md or Cursor rules export.

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