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/command-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 | $0.00007 | $0.00425 |
| Opus 5 | $0.00003 | $0.00212 |
| Sonnet 5 | $0.00001 | $0.00085 |
| Haiku 4.5 | $0.00001 | $0.00042 |
Grade A, and why
command-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 yesterday.
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
Command Evaluate
Wraps cc:cc-commands skill.
Score slash command quality across 10 dimensions. Evaluate only — make NO changes. Delegates to cc:cc-commands skill.
When to Use
- Check current score without making changes
- Compare scores before and after refinement
Arguments
| Argument | Description | Default |
|---|---|---|
<nameOrPath> |
Command name or path to its .md file | (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 a command
/cc:command-evaluate ./commands/my-command.md
# Save results to the evaluation store
/cc:command-evaluate ./commands/my-command.md --save
Implementation
Pass $ARGUMENTS to the underlying skill for processing.
Delegates to cc:cc-commands skill:
Skill(skill="cc:cc-commands", args="evaluate $ARGUMENTS")
Direct CLI execution (all platforms):
superskill command 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.
- yesterday First seen · 57 lines · 7 tokens per session scan A 9551d9a2d12f
command-evaluate is a command published in the GitHub repository gobing-ai/superskill (5 stars, last pushed 5d ago), licensed Apache-2.0. It adds 7 tokens to every session and 425 once invoked, about $0.0000 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 commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.