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/smart-helpgit 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 | $0.00007 | $0.01300 |
| Opus 5 | $0.00003 | $0.00650 |
| Sonnet 5 | $0.00001 | $0.00260 |
| Haiku 4.5 | $0.00001 | $0.00130 |
Grade A, and why
smart-help 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.
How it starts
The opening of the file, as written. The whole thing — 173 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/craft:help - Context-Aware Help
Get intelligent help based on your project and question.
Usage
/craft:help # Show relevant commands for current project
/craft:help <topic> # Get help on specific topic
/craft:help "how do I..." # Answer questions about workflows
Context-Aware Suggestions
Based on Project Type
Python Project:
╭─ Suggested Commands for Python Project ─────────────╮
│ │
│ Development: │
│ /craft:code:lint ruff, flake8 checks │
│ /craft:test:run pytest runner │
│ /craft:test:coverage coverage report │
│ │
│ Quality: │
│ /craft:code:deps-audit pip-audit security │
│ /craft:code:ci-local pre-commit simulation │
│ │
│ Release: │
│ /craft:code:release PyPI workflow │
│ /craft:docs:changelog update CHANGELOG.md │
│ │
╰─────────────────────────────────────────────────────╯
R Package:
╭─ Suggested Commands for R Package ──────────────────╮
│ │
│ Development: │
│ /craft:test:run testthat runner │
│ /craft:code:lint lintr checks │
│ /craft:code:docs-check roxygen2 validation │
│ │
│ Quality: │
│ /craft:code:ci-local R CMD check simulation │
│ /craft:arch:analyze package structure │
│ │
│ Release: │
│ /craft:code:release CRAN submission prep │
│ /craft:site:build pkgdown site │
│ │
╰─────────────────────────────────────────────────────╯
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 · 173 lines · 7 tokens per session scan A 08ac4e2dba89
smart-help is a command published in the GitHub repository Data-Wise/claude-plugins (7 stars, last pushed 5d ago), licensed MIT. It adds 7 tokens to every session and 1,300 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.