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/data-wise/claude-plugins/smartgit clone --depth 1 https://github.com/Data-Wise/claude-pluginsWhat 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 | $0.00000 | $0.00443 |
| Opus 5 | $0.00000 | $0.00221 |
| Sonnet 5 | $0.00000 | $0.00089 |
| Haiku 4.5 | $0.00000 | $0.00044 |
Grade A, and why
smart 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
Smart Commands
Universal AI-powered commands for intelligent task routing.
/craft:do
Purpose: Universal command that routes tasks to the best workflow automatically.
Usage:
/craft:do "add user authentication"
/craft:do "optimize database queries"
/craft:do "create API documentation"
The AI analyzes your request and:
- Determines the best approach
- Selects appropriate tools/commands
- Executes the workflow
- Reports results
/craft:orchestrate
Purpose: Enhanced orchestrator v2.1 with mode-aware execution and subagent monitoring.
Usage:
/craft:orchestrate "implement auth" optimize # Fast parallel
/craft:orchestrate "prep release" release # Thorough audit
/craft:orchestrate status # Agent dashboard
/craft:orchestrate timeline # Execution timeline
/craft:orchestrate continue # Resume session
Features:
- Mode-aware execution (default/debug/optimize/release)
- Subagent delegation and monitoring
- Chat compression for long sessions
- ADHD-optimized status tracking
- Timeline view of execution
/craft:check
Purpose: Pre-flight checks before commits, PRs, or releases.
Usage:
/craft:check # Quick validation
/craft:check --for commit # Pre-commit checks
/craft:check --for pr # Pre-PR validation
/craft:check --for release # Full release audit
Checks:
- Code linting
- Test execution
- Documentation validation
- Build verification
- Dependency updates
/craft:help
Purpose: Context-aware help and suggestions for your project.
Usage:
/craft:help # General suggestions
/craft:help testing # Deep dive into testing
/craft:help documentation # Docs-specific help
Features:
- Analyzes your project structure
- Suggests relevant commands
- Provides context-specific guidance
- Links to documentation
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 · 77 lines · 0 tokens per session scan A 801a589a8276
smart is a command published in the GitHub repository Data-Wise/claude-plugins (7 stars, last pushed 5d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 443 tokens. 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.