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/gobing-ai/superskill/cc-agentsnpx skills add gobing-ai/superskill --skill cc-agentsgit clone --depth 1 https://github.com/gobing-ai/superskillWrote 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/gobing-ai/superskill/cc-agents)<a href="https://agentmods.dev/skills/gobing-ai/superskill/cc-agents"><img src="https://agentmods.dev/badge/skills/gobing-ai/superskill/cc-agents.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 | $0.00069 | $0.02993 |
| Opus 5 | $0.00034 | $0.01496 |
| Sonnet 5 | $0.00014 | $0.00599 |
| Haiku 4.5 | $0.00007 | $0.00299 |
Grade A, and why
cc-agents 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 5d 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.
How it starts
The opening of the file, as written. The whole thing — 286 lines — stays where its author put it; the contents beside it link to each section on GitHub.
cc-agents: Universal Subagent Creator
Create subagents that work across ALL platforms from a single source of truth.
When to Use
- Creating a new subagent -> use scaffold
- Checking agent structure -> use validate
- Scoring quality -> use evaluate
- Fixing quality issues -> use refine
- Planning longitudinal improvement -> use evolve
Quick Start
# Create a new agent
superskill agent scaffold my-agent --output ./agents
# Check structure
superskill agent validate agents/my-agent.md
# Score quality (two-call seam: envelope → Scorer → ingest)
superskill agent evaluate agents/my-agent.md --rubric <rubric.yaml> --json > envelope.json
# ... Scorer persona scores envelope.json → scores.json ...
superskill agent evaluate agents/my-agent.md --ingest scores.json --save
# Fix issues
superskill agent refine agents/my-agent.md --auto --save
# Evolve (two-call seam: envelope → Author → Skeptic → Judge → ingest)
superskill agent evolve agents/my-agent --propose-only --json > briefs.json
# ... Author persona rewrites → proposal.json; Skeptic refutes; Judge picks winner ...
superskill agent evolve agents/my-agent --ingest proposal.json --accept <id>
Workflows
- New agent: scaffold → validate → evaluate → refine
- Improve existing agent: evaluate → refine → evaluate (verify improvement)
- Longitudinal improvement planning: evaluate → refine → collect feedback → evolve
(
evolveis a separate longitudinal loop for proposal-driven maintenance and rollback)
Operations
This skill accepts 5 operations:
| Operation | Purpose | Script |
|---|---|---|
| scaffold | Create a new agent from template (pick tier, fill placeholders, then validate) | superskill agent scaffold |
| validate | Check agent structure; --target per platform, --strict for all rules; loop to 0 errors |
superskill agent validate |
| evaluate | Score agent quality (rubric-driven two-call seam) | superskill agent evaluate |
| refine | Fix issues and improve quality (--auto --save, then re-evaluate) |
superskill agent refine |
| evolve | Propose and apply longitudinal improvements (two-call seam with personas) | superskill agent evolve |
What ships with it
13 files 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.
- agents/openai.yaml 475 B
- metadata.openclaw 428 B
- references/agent-anatomy.md 9.4 KB
- references/architecture.md 2.4 KB
- references/colors.md 7.4 KB
- references/evaluation-framework.md 5.8 KB
- references/frontmatter-reference.md 3.8 KB
- references/hybrid-architecture.md 5.8 KB
- references/model-tiers.md 8.3 KB
- references/platform-compatibility.md 5.9 KB
- references/red-flags.md 5.5 KB
- references/troubleshooting.md 2.6 KB
- references/workflows.md 33 KB
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.
- 5d ago First seen · 286 lines · 69 tokens per session scan A a75cefc422e6
cc-agents is a skill published in the GitHub repository gobing-ai/superskill (5 stars, last pushed 2d ago), licensed Apache-2.0. It adds 69 tokens to every session and 2,993 once invoked, about $0.0003 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 2026-08-31.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…