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 commands/gobing-ai/superskill/hook-evaluategit 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 | $0.00020 | $0.00460 |
| Opus 5 | $0.00010 | $0.00230 |
| Sonnet 5 | $0.00004 | $0.00092 |
| Haiku 4.5 | $0.00002 | $0.00046 |
Grade A, and why
hook-evaluate 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.
What it actually says
Hook Evaluate
Wraps cc:cc-hooks skill.
Score hook quality across 4 dimensions. Evaluates hooks.json directly — scans for dangerous command patterns, checks event coverage, matcher specificity, and portability. Evaluate only — make NO changes. Delegates to cc:cc-hooks skill.
When to Use
- Scan hooks for dangerous command patterns before deployment
- Check event coverage breadth
- Verify matcher specificity and timeout presence
Arguments
| Argument | Description | Default |
|---|---|---|
<nameOrPath> |
Hook name or path to hooks.json | (required) |
--json |
Output machine-readable JSON; with --rubric, emit a scoring work order |
false |
--target |
Target platform | claude |
--save |
Persist the evaluation to the evaluation store (enables evolve trend analysis) | false |
--rubric <file> |
Rubric path for envelope-out scoring | built-in |
--ingest <file> |
Agent-scored result JSON to validate and persist | - |
Examples
# Evaluate the hooks config
/cc:hook-evaluate ./hooks/hooks.json
# Save results for trend analysis
/cc:hook-evaluate ./hooks/hooks.json --save
Implementation
Pass $ARGUMENTS to the underlying skill for processing.
Delegates to cc:cc-hooks skill:
Skill(skill="cc:cc-hooks", args="evaluate $ARGUMENTS")
Direct CLI execution (all platforms):
superskill hook evaluate $ARGUMENTS
Platform Notes
- Claude Code: Invoke via
Skill()delegation - Other platforms: Run
superskillCLI directly via Bash tool
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 · 58 lines · 20 tokens per session scan A 0cb22f8acb67
hook-evaluate is a command published in the GitHub repository gobing-ai/superskill (5 stars, last pushed 6d ago), licensed Apache-2.0. It adds 20 tokens to every session and 460 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 2026-08-31.
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