القائمة
Resume a paused experiment. Checkout the experiment branch, read results history, continue iterating.
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/ArogyaReddy/alirezarezvani-claude-skills --skill resumeيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
25 advanced engineering agent skills and plugins for Claude Code, Codex, Gemini CLI, Cursor, OpenClaw. Agent design, RAG, MCP servers, CI/CD, database design, observability, security auditing, release management, platform ops.
Security audit and vulnerability scanner for AI agent skills before installation. Use when: (1) evaluating a skill from an untrusted source, (2) auditing a skill directory or git repo URL for malicious code, (3) pre-install security gate for Claude Code plugins, OpenClaw skills, or Codex skills, (4) scanning Python scripts for dangerous patterns like os.system, eval, subprocess, network exfiltration, (5) detecting prompt injection in SKILL.md files, (6) checking dependency supply chain risks, (7) verifying file system access stays within skill boundaries. Triggers: "audit this skill", "is this skill safe", "scan skill for security", "check skill before install", "skill security check", "skill vulnerability scan".
10 product agent skills and plugins for Claude Code, Codex, Gemini CLI, Cursor, OpenClaw. PM toolkit (RICE), agile PO, product strategist (OKR), UX researcher, UI design system, competitive teardown, landing page generator, SaaS scaffolder, research summarizer. Python tools (stdlib-only).
استنادا إلى تصنيف SOC المهني
عرض SKILL.md
| name | resume |
| description | Resume a paused experiment. Checkout the experiment branch, read results history, continue iterating. |
| command | /ar:resume |
Resume a paused or context-limited experiment. Reads all history and continues where you left off.
/ar:resume # List experiments, let user pick
/ar:resume engineering/api-speed # Resume specific experiment
If no experiment specified:
python {skill_path}/scripts/setup_experiment.py --list
Show status for each (active/paused/done based on results.tsv age). Let user pick.
# Checkout the experiment branch
git checkout autoresearch/{domain}/{name}
# Read config
cat .autoresearch/{domain}/{name}/config.cfg
# Read strategy
cat .autoresearch/{domain}/{name}/program.md
# Read full results history
cat .autoresearch/{domain}/{name}/results.tsv
# Read recent git log for the branch
git log --oneline -20
Summarize for the user:
Resuming: engineering/api-speed
Target: src/api/search.py
Metric: p50_ms (lower is better)
Experiments: 23 total — 8 kept, 12 discarded, 3 crashed
Best: 185ms (-42% from baseline of 320ms)
Last experiment: "added response caching" → KEEP (185ms)
Recent patterns:
- Caching changes: 3 kept, 1 discarded (consistently helpful)
- Algorithm changes: 2 discarded, 1 crashed (high risk, low reward so far)
- I/O optimization: 2 kept (promising direction)
How would you like to continue?
1. Single iteration (/ar:run) — I'll make one change and evaluate
2. Start a loop (/ar:loop) — Autonomous with scheduled interval
3. Just show me the results — I'll review and decide
If the user picks loop, hand off to /ar:loop with the experiment pre-selected.
If single, hand off to /ar:run.