Borrowing it
Nothing to install: this file belongs to zhouguoqing/QianYuan.AIAgenticFramework. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/zhouguoqing/QianYuan.AIAgenticFramework/main/.agents/skills/using-superpowers/SKILL.mdgit clone --depth 1 https://github.com/zhouguoqing/QianYuan.AIAgenticFrameworkWrote 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/zhouguoqing/qianyuan.aiagenticframework/using-superpowers)<a href="https://agentmods.dev/skills/zhouguoqing/qianyuan.aiagenticframework/using-superpowers"><img src="https://agentmods.dev/badge/skills/zhouguoqing/qianyuan.aiagenticframework/using-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/zhouguoqing/qianyuan.aiagenticframework/using-superpowers"><img src="https://agentmods.dev/badge/skills/zhouguoqing/qianyuan.aiagenticframework/using-superpowers.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00026 | $0.00443 |
| Opus 5 | $0.00013 | $0.00221 |
| Sonnet 5 | $0.00005 | $0.00089 |
| Haiku 4.5 | $0.00003 | $0.00044 |
Grade A, and why
using-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 9d 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.
What it actually says
Using Superpowers
The Core Rule
If there is even a 1% chance a skill applies to the current task, invoke it.
Skills are cheap to load. Failing to use an applicable skill is expensive.
How to Find Skills
At the start of every new task:
- Review available skills (check
~/.openclaw/extensions/superpowers/skills/) - Ask: "Does any skill match what I'm about to do?"
- If yes → load and follow the skill exactly
- If no → proceed with default behavior
In OpenClaw's persistent agent context, check skills at the start of every new task, not just new conversations. A long-running session may handle many different task types.
Decision Flowchart
New task received
↓
Does any skill apply? (even 1% chance → YES)
↓
YES → Announce: "Using [skill-name] to [purpose]"
Load skill file
Follow instructions exactly
↓
Complete or hand off cleanly
NO → Proceed with default agent behavior
Priority Hierarchy
- User instructions — always highest priority
- Skills — override default behavior when applicable
- Default behavior — fallback when no skill applies
Announcing Skill Usage
When you invoke a skill, say so:
"Using
brainstormingto explore approaches before writing code." "Usingsystematic-debuggingto diagnose this error."
This keeps the human informed and lets them redirect if needed.
OpenClaw-Specific Notes
- In persistent sessions, skills should be checked at the start of each new task, not just session start
- Skills around
task-handoffandagent-self-recoverymatter more in OpenClaw than in session-based tools — don't skip them - If a task will take more than ~20 minutes, check
long-running-task-managementfirst
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.
- 9d ago First seen · 61 lines · 26 tokens per session scan A 9ee65e60f9c2
using-superpowers is a skill published in the GitHub repository zhouguoqing/QianYuan.AIAgenticFramework (36 stars, last pushed 23d ago), licensed Apache-2.0. It adds 26 tokens to every session and 443 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
conversational-ux
Design voice and conversational interfaces — dialog flows, error recovery, and persona. Use when the interface speaks and listens rather than being tapped. For graphical input collection, use form-design.
platform-conventions
Design to iOS and Android conventions — what each OS mandates, where they diverge, and when to unify. Use when shipping native apps. For breakpoint adaptation use responsive-design; for matching competitor patterns use jakobs-law (interaction-design).
research-repository
Build a repository that makes findings findable, reusable, and cumulative across teams. Use when the same research keeps getting redone. For synthesising one study, use affinity-diagram.
design-negotiation
Advocate for design quality, scope, and timeline with partners and leadership using evidence and shared goals. Use in the conversation itself. For the commercial vocabulary behind it, use business-design (ux-strategy).
form-design
Design a form end to end — field order, grouping, validation, and completion. Use when the artifact is a form. For product-wide error strategy use error-handling-ux; for first-run signup use onboarding-design.
peak-end-rule
Apply the Peak-End Rule — a flow is remembered by its most intense moment and its last. Use when designing completion, celebration, or cancellation moments. For sustaining engagement mid-flow, use zeigarnik-effect.