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 skills add itmisx/deepx-code --skill superpowersgit clone --depth 1 https://github.com/itmisx/deepx-codeWrote 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/itmisx/deepx-code/superpowers)<a href="https://agentmods.dev/skills/itmisx/deepx-code/superpowers"><img src="https://agentmods.dev/badge/skills/itmisx/deepx-code/superpowers/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/itmisx/deepx-code/superpowers"><img src="https://agentmods.dev/badge/skills/itmisx/deepx-code/superpowers.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00050 | $0.01567 |
| Opus 5.5 | $0.00020 | $0.00627 |
| Sonnet 5.5 | $0.00010 | $0.00313 |
| Haiku 4.5 | $0.00005 | $0.00157 |
Grade A, and why
superpowers 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 1mo 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
84% identical to using-superpowers — 17 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 — 127 lines — stays where its author put it; the contents beside it link to each section on GitHub.
deepx 适配(工具名映射) —— 下文沿用 Claude Code 的工具名,在 deepx 里请按此对应,不要调用不存在的
Skill/TodoWrite/EnterPlanMode:
Skill工具 → deepx 用LoadSkill(按 skill 名加载并遵循)TodoWrite→ deepx 用Todo(全量重发整张待办清单)EnterPlanMode→ deepx 无此工具:可并行拆任务用CreatePlan,或直接在回复里给出计划;/plan模式由用户进入,模型不能自己进Task/ 派发子 agent → deepx 用CreatePlan(拆并发子 agent 的 DAG);单会话内顺序执行则自己逐步做 凡正文出现上述名字,一律换成 deepx 的对应工具。
IF A SKILL APPLIES TO YOUR TASK, YOU DO NOT HAVE A CHOICE. YOU MUST USE IT.
This is not negotiable. This is not optional. You cannot rationalize your way out of this.
Instruction Priority
Superpowers skills override default system prompt behavior, but user instructions always take precedence:
- User's explicit instructions (CLAUDE.md, GEMINI.md, AGENTS.md, direct requests) — highest priority
- Superpowers skills — override default system behavior where they conflict
- Default system prompt — lowest priority
If CLAUDE.md, GEMINI.md, or AGENTS.md says "don't use TDD" and a skill says "always use TDD," follow the user's instructions. The user is in control.
How to Access Skills
In Claude Code: Use the Skill tool. When you invoke a skill, its content is loaded and presented to you—follow it directly. Never use the Read tool on skill files.
In Copilot CLI: Use the skill tool. Skills are auto-discovered from installed plugins. The skill tool works the same as Claude Code's Skill tool.
In Gemini CLI: Skills activate via the activate_skill tool. Gemini loads skill metadata at session start and activates the full content on demand.
In other environments: Check your platform's documentation for how skills are loaded.
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.
- 1mo ago First seen · 127 lines · 50 tokens per session scan A e3fe341be6d2
superpowers is a skill published in the GitHub repository itmisx/deepx-code (391 stars, last pushed 25d ago), licensed MIT. It adds 50 tokens to every session and 1,567 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 84% identical to using-superpowers, differing in 17 lines, and is treated as a copy.
Other skills, from other repositories
plugin-creator
Create and scaffold plugin directories for Codex with a required .codex-plugin/plugin.json, optional plugin folders/files, valid manifest defaults, and personal-marketplace entries by default. Use when Codex needs to create a new personal plugin, add optional plugin structure, generate or update marketplace entries…
update-v8-version
Update Codex's pinned v8 / rustyv8 versions, validate the release-candidate path, and investigate failed V8 canary or artifact builds. Use when asked to bump V8, update rustyv8 artifacts, prepare or validate a V8 release candidate, check v8-canary, or diagnose why a V8 version update no longer builds.
agent-state-bar
A design guide for an agent status bar: a short, automatically updated summary of progress, remaining tasks, tool counts, errors, and system state placed near the latest model input.
reward-design
A guide to defining the scores that an AI agent receives during trial-and-error training. It covers checks based on rules, human preferences, or another model, and distinguishes rewarding the final result from rewarding the steps taken.
agent-evolution
A guide to making an AI agent improve from its past runs. It covers how to judge results, extract reusable lessons, and decide whether to store them as knowledge, instructions, code, or model training.
knowledge-org
A guide to organising large bodies of knowledge so an AI system can navigate relationships, summaries, and layers instead of searching only flat text chunks. It covers tree indexes, knowledge graphs, file-like structures, incremental updates, and long-term memory layouts.