Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/pyramidheadshark/claude-scaffold/windows-developernpx skills add pyramidheadshark/claude-scaffold --skill windows-developergit clone --depth 1 https://github.com/pyramidheadshark/claude-scaffoldWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/pyramidheadshark/claude-scaffold/windows-developer)<a href="https://agentmods.dev/skills/pyramidheadshark/claude-scaffold/windows-developer"><img src="https://agentmods.dev/badge/skills/pyramidheadshark/claude-scaffold/windows-developer.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.00648 |
| Opus 5 | $0.00000 | $0.00324 |
| Sonnet 5 | $0.00000 | $0.00130 |
| Haiku 4.5 | $0.00000 | $0.00065 |
Grade A, and why
windows-developer scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
subprocess.run(cmd, capture_output=True, text=True, encoding="utf-8") How it starts
The opening of the file, as written. The whole thing — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Windows Developer Guide
When to Load
Automatically loaded on Windows (platform_trigger: "win32").
Applies to: .py, .ps1, .bat, .cmd files and any Windows-specific workflow.
Python on Windows
Encoding (CRITICAL)
Windows defaults to cp1251 / cp1252 for file I/O. Always specify UTF-8 explicitly:
with open("file.txt", "r", encoding="utf-8") as f:
content = f.read()
Path("file.txt").read_text(encoding="utf-8")
Path("file.txt").write_text(content, encoding="utf-8")
import json
json.load(open("data.json", encoding="utf-8"))
At script entry point, reconfigure stdout:
import sys
sys.stdout.reconfigure(encoding="utf-8")
sys.stderr.reconfigure(encoding="utf-8")
For subprocess calls:
subprocess.run(cmd, capture_output=True, text=True, encoding="utf-8")
Python Command
Use python (not python3) on Windows. The python3 alias is not reliably available.
python -m pytest tests/
python -m pip install -e .
python scripts/run.py
Path Handling
Use pathlib.Path or os.path — never hardcode forward/backslashes:
from pathlib import Path
config = Path(__file__).parent / "config" / "settings.json"
Terminal Encoding
Git Bash (Preferred for Claude Code)
Git Bash handles UTF-8 well by default. Recommended as primary shell.
CMD / PowerShell
Set code page to UTF-8 before running scripts:
chcp 65001
PowerShell profile setup:
[Console]::OutputEncoding = [System.Text.Encoding]::UTF8
$OutputEncoding = [System.Text.Encoding]::UTF8
Environment Variable
Set globally for consistent behavior:
[Environment]::SetEnvironmentVariable("PYTHONIOENCODING", "utf-8", "User")
Common Windows Pitfalls
- File locking: Windows locks open files — close handles before rename/delete
- Max path length: Enable long paths via Group Policy or registry if paths exceed 260 chars
- Line endings: Configure git:
git config core.autocrlf true - Temp files: Use
tempfile.NamedTemporaryFile(delete=False)— Windows cannot open a temp file while it's held - Process cleanup: Use
taskkill /F /PIDinstead ofkill -9 - Permission errors on rmtree: Use
onerrorhandler for read-only files:
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 103 lines · 0 tokens per session scan A a4c03227b1df
windows-developer is a skill published in the GitHub repository pyramidheadshark/claude-scaffold (4 stars, last pushed 4mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 648 tokens. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
mle-workflow
Production ML engineering workflow — data contracts, reproducible training, evaluation gates, deployment, and monitoring. Use when building, reviewing, or hardening ML systems beyond notebooks.
data-scientist
!cat Claude-Production-Grade-Suite/.protocols/ux-protocol.md 2>/dev/null || true !cat Claude-Production-Grade-Suite/.protocols/input-validation.md 2>/dev/null || true !cat Claude-Production-Grade-Suite/.protocols/tool-efficiency.md 2>/dev/null || true !cat Claude-Production-Grade-Suite/.protocols/visual-identity.md…
ai-engineer
Builds production AI/ML systems — model training, fine-tuning, MLOps pipelines, model serving, evaluation frameworks, RAG optimization, and agent orchestration at scale. Use when the user asks to build, train, or deploy ML models, set up MLOps pipelines, optimize RAG systems, create inference endpoints, or design…
data-scientist
!cat skills/shared/protocols/ux-protocol.md 2>/dev/null || true !cat skills/shared/protocols/input-validation.md 2>/dev/null || true !cat skills/shared/protocols/tool-efficiency.md 2>/dev/null || true !cat .production-grade.yaml 2>/dev/null || echo "No config — using defaults".
ai-ml-engineering
AI/ML Engineering Review: Reviews AI/ML systems for production readiness — model serving, MLOps pipelines, LLM integration patterns, prompt engineering, evaluation frameworks, and responsible AI. Covers model deployment, feature stores, experiment tracking, monitoring/drift detection, and AI safety. Use when the user…
git-hooks-manager
Setup and manage git hooks for pre-commit, pre-push automation (lint, test, format).