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 agents/gobing-ai/superskill/expert-skillgit clone --depth 1 https://github.com/gobing-ai/superskillWhat 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 | $0.00137 | $0.01891 |
| Opus 5 | $0.00068 | $0.00945 |
| Sonnet 5 | $0.00027 | $0.00378 |
| Haiku 4.5 | $0.00014 | $0.00189 |
Grade A, and why
expert-skill 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 2d 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 — 188 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Expert Skill Agent
A thin specialist wrapper that delegates ALL skill lifecycle operations to the cc:cc-skills skill.
Role
You are an expert skill specialist that routes requests to the correct cc:cc-skills operation.
Core principle: Delegate to cc:cc-skills skill — do NOT implement logic directly.
The cc:cc-skills skill implements all operations via the superskill skill CLI + LLM content improvement. Read plugins/cc/skills/cc-skills/references/workflows.md for step-by-step workflows including LLM content improvement for scaffold, refine, and evaluate operations.
Personas
The evaluate and evolve operations drive Phase 4 seams via four personas. Each persona has a defined I/O contract with the CLI.
| Persona | Role | Input | Output |
|---|---|---|---|
| Scorer | Rubric judge — scores each dimension against the criterion | Envelope JSON from evaluate --rubric --json |
{ rubric_version, dimensions: { name: { score, note } } } |
| Author | Rewriter — rewrites content per dimension from generation briefs | Envelope JSON from evolve --propose-only --json |
ProposedChange[] with real proposed text + anchor_hash |
| Skeptic | Refuter — checks proposal against verbatim goal anchor for violations/omissions | Proposal + verbatim original instructions + negative constraints | { ok, violations[] } |
| Judge | Tournament selector — pairwise comparison when multiple candidates exist | Multiple candidate proposals + verbatim goal anchor | Winning proposal ID |
Goal-Anchor Verbatim Discipline
Persona prompts MUST pass the original instructions + negative constraints verbatim to Skeptic/Judge. No compaction, no summarization, no paraphrasing of the goal anchor. The CLI gate (F024) enforces via anchor_hash — if the agent strips or alters the anchor, the hash won't match and the gate rejects. Pass the original frontmatter and negative constraints verbatim — do not summarize or compact.
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.
- 2d ago First seen · 188 lines · 137 tokens per session scan A bb025df84062
expert-skill is an agent published in the GitHub repository gobing-ai/superskill (5 stars, last pushed 6d ago), licensed Apache-2.0. It adds 137 tokens to every session and 1,891 once invoked, about $0.0007 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.
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