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/guanyang/open-agent-hub/executing-plansnpx skills add guanyang/open-agent-hub --skill executing-plansgit clone --depth 1 https://github.com/guanyang/open-agent-hubWrote 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/guanyang/open-agent-hub/executing-plans)<a href="https://agentmods.dev/skills/guanyang/open-agent-hub/executing-plans"><img src="https://agentmods.dev/badge/skills/guanyang/open-agent-hub/executing-plans.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 | $0.00022 | $0.00503 |
| Opus 5 | $0.00011 | $0.00251 |
| Sonnet 5 | $0.00004 | $0.00101 |
| Haiku 4.5 | $0.00002 | $0.00050 |
Grade A, and why
executing-plans scanned grade A with 0 findings 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 4d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to executing-plans — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Executing Plans
Overview
Load plan, review critically, execute all tasks, report when complete.
Announce at start: "I'm using the executing-plans skill to implement this plan."
Note: Tell your human partner that Superpowers works much better with access to subagents (Claude Code, Codex CLI, Codex App, Copilot CLI, and Gemini CLI all qualify; see the per-platform tool refs in ../using-superpowers/references/). If subagents are available, use superpowers:subagent-driven-development instead of this skill.
The Process
Step 1: Load and Review Plan
- Ensure an isolated workspace: use superpowers:using-git-worktrees to create one or verify the existing one
- Read plan file
- Review critically - identify any questions or concerns about the plan
- If concerns: Raise them with your human partner before starting
- If no concerns: Create todos for the plan items and proceed
Step 2: Execute Tasks
For each task:
- Mark as in_progress
- Follow each step exactly (plan has bite-sized steps)
- Run verifications as specified
- Mark as completed
Step 3: Complete Development
After all tasks complete and verified:
- Announce: "I'm using the finishing-a-development-branch skill to complete this work."
- REQUIRED SUB-SKILL: Use superpowers:finishing-a-development-branch
- Follow that skill to verify tests, present options, execute choice
When to Stop and Ask for Help
STOP executing immediately when:
- Hit a blocker (missing dependency, test fails, instruction unclear)
- Plan has critical gaps preventing starting
- You don't understand an instruction
- Verification fails repeatedly
Ask for clarification rather than guessing.
When to Revisit Earlier Steps
Return to Review (Step 1) when:
- Partner updates the plan based on your feedback
- Fundamental approach needs rethinking
Don't force through blockers - stop and ask.
Remember
- Review plan critically first
- Follow plan steps exactly
- Don't skip verifications
- Reference skills when plan says to
- Stop when blocked, don't guess
- Never start implementation on main/master branch without explicit user consent
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.
- 4d ago First seen · 65 lines · 22 tokens per session scan A c4c3d8b628c5
executing-plans is a skill published in the GitHub repository guanyang/open-agent-hub (960 stars, last pushed yesterday), licensed MIT. It adds 22 tokens to every session and 503 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to executing-plans, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
la-le-me
以中文、English 或中英双语记录、解释和复盘成人日常排便与肠道健康信息，包括 Bristol 便形文字分类、频次和时段趋势、食物线索、可持续饮食调整、季节菜谱、每日排便提醒，以及危险信号分流和经同意的附近就医机构查找。Use for Chinese, English, or bilingual requests about poop, stool, bowel movements, constipation or diarrhea tendencies, foods associated with a bowel change, gut-friendly meals, recurring bowel-health…
generating-project-rules
Make sure to use this skill WHENEVER the user mentions creating, generating, or updating project rules files (.cursorrules, AGENTS.md, CLAUDE.md, GEMINI.md, or equivalent) for any software project. This is essential when a user wants to integrate AI skills into their development workflow, or when they need…
newton
Reasoning and sparring partner mode for rigorous, calibrated engagement on any topic — pressure-testing ideas, researching with current authoritative sources, running reuse-before-rebuild checks on "how do I" tasks, and scoping work thoroughly before diving in. Use this skill ONLY when the user explicitly invokes…
python-pro
🚀 Professional AI Agent orchestrator for generating tailored rules and AGENTS.md with deterministic JIT skill discovery. Optimized for Context Savings and Agent Alignment.
found
🚀 Professional AI Agent orchestrator for generating tailored rules and AGENTS.md with deterministic JIT skill discovery. Optimized for Context Savings and Agent Alignment.
sample-skill
Capability summary.