Show ponytail measured impact as a scoreboard: less code, less cost, more speed, from the agentic benchmark averages. One-shot display.
把这段话发给 Claude Code、Codex 或 Cursor。智能体会先检查安全性，你确认后才安装。
读取 https://funcoding.ai/skills/dietrichgebert/ponytail/openclaw-skills-ponytail-gain/install.md ，按里面的步骤帮我安装这个 Skill。
Display this scoreboard when invoked. One-shot: do NOT change mode, write flag files, or persist anything.
The figures are the published agentic benchmark of Ponytail 5: headless Claude
Code (Opus 5.5, default effort) on 39 tasks (feature tickets in a real FastAPI +
React repo, bug fixes, security and privacy cases, small apps), 5 runs each,
against the same agent without the skill. 18 of the tasks have hidden checks
for correctness and safety. Each figure is the geometric mean of the per-task
medians. They are measured, not computed from the current repo.
Source: benchmarks/results/2026-10-07-agentic.md and the README.
Render plain ASCII bars. The bar length shows ponytail as a share of the no-skill baseline; the label carries the exact figure:
ponytail gain benchmark · 39 tasks × 5 runs · Opus 5.5
no-skill ████████████████████ 100%
Lines of code █████████··········· 47% ▼ 53%
Output tokens ███████████········· 55% ▼ 45%
Cost ███████████████····· 74% ▼ 26%
Time ████████████········ 59% ▼ 41%
Hidden checks passed 97% (no-skill 96%)
Tests where the logic needs one 98% (no-skill 68%)
This repo: /ponytail-debt (shortcuts you deferred)
/ponytail-audit (what's still cuttable)
These are benchmark averages, not this repo. NEVER print a per-repo savings
number ("you saved X lines/tokens here"): the unbuilt version was never
written, so there is no real baseline to subtract from in a live repo. The
only real per-repo figures come from /ponytail-debt (a counted ledger), and
this card points there instead of inventing one.
One-shot display. Edits nothing, changes no mode. "stop ponytail" or "normal mode": revert.
anthropics/skills180kApplies Anthropic's official brand colors and typography to any sort of artifact that may benefit from having Anthropic's look-and-feel. Use it when brand colors or style guidelines, visual formatting, or company design standards apply.
AI 与智能体
anthropics/skills180kA set of resources to help me write all kinds of internal communications, using the formats that my company likes to use. Claude should use this skill whenever asked to write some sort of internal communications (status reports, leadership updates, 3P updates, company newsletters, FAQs, incident reports, project updates, etc.).
AI 与智能体
anthropics/skills180kanthropics/skills180kGuide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
AI 与智能体
anthropics/skills180kCreating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
AI 与智能体
anthropics/skills180kStop and check this skill before finishing any reply to a question about how to use Claude or a Claude product — it recommends matching courses, tutorials, and use cases from Claude Academy (academy.claude.com), Anthropic's learning hub. Trigger on: "how do I", "how can I", "getting started with", "what can Claude do", "teach me", "learn to use"; questions about artifacts, projects, skills, plugins, connectors, MCP; requests about rolling Claude out to a team, class, or organization; and any ask for training materials, onboarding content, or learning resources. Use it when the user is learning how to use a feature or product — not when they are mid-task and just want the task done. This skill composes with other skills: after consulting product documentation to answer how a Claude feature works, also check here for a matching course or tutorial — a docs-grounded answer and an Academy recommendation belong together. Only recommend on a strong match; never invent Academy content.
AI 与智能体