GreenLv/skillferry
Portable agent workspace across Codex, Claude Code, and DeepSeek Harness
ARCHETYPE
Minimalist
Lean instructions, few tools, tiny context tax. Lets the model think.
Copy this rig
# review before running: this installs third-party code
$ npx degit GreenLv/skillferry/skills ./rig-skillferry/skills MCP servers are added to Claude Code at local scope; env vars are shown as YOUR_… placeholders — we never store values. Files are fetched with degit into a separate folder so you can review before merging.
$ npx degit GreenLv/skillferry/examples/starter-workspace/skills/release-checklist .claude/skills/release-checklist $ npx degit GreenLv/skillferry/examples/starter-workspace/skills/setup-skillferry .claude/skills/setup-skillferry
This rig commits no guardrails. Here is the community baseline instead — the deny/ask rules most often found across all 6,974 rigs:
{
"permissions": {
"deny": [
"Read(./.env)",
"Read(**/.env)",
"Read(~/.ssh/**)",
"Bash(rm -rf *)",
"Read(**/*.pem)",
"Bash(rm -rf /)",
"Bash(git push --force:*)",
"Bash(sudo *)",
"Read(~/.aws/**)",
"Bash(rm -rf /*)",
"Read(./.env.*)",
"Read(.env)",
"Bash(git push --force*)",
"Bash(rm -rf:*)",
"Read(**/.env.*)",
"Read(**/*.key)",
"Bash(sudo:*)",
"Bash(git reset --hard*)",
"Bash(git reset --hard:*)",
"Read(.env.*)"
],
"ask": [
"Bash(git push:*)",
"Bash(git push *)",
"Bash(git commit:*)",
"Bash(rm *)",
"Bash(rm:*)",
"Bash(npm publish:*)",
"Bash(wget *)",
"Bash(git rebase *)",
"Bash(gh pr merge *)",
"Bash(git commit *)"
]
}
} Skills (2)
Similar rigs
zzallirog/agent-atlas
A modular working-memory layout for LLM agents that work across many repositories.
Minimalist 904 tok ·
herbeus/graft
One shared directory of AI-agent context, linked into every repository you work on - found by git remote URL, not by hardcoded path
Minimalist 33 tok ·
seanchatmangpt/claude-code-config-lsp
LSP 3.18 server for Claude Code config files (settings.json, CLAUDE.md, hooks, agents, skills)
Orchestrator 6.4k tok ·
maximhq/bifrost
Fastest enterprise AI gateway (50x faster than LiteLLM) with adaptive load balancer, cluster mode, guardrails, 1000+ models support & <100 µs overhead at 5k RPS.
Skill Collector 16.5k tok ·