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/depsgit 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 | $0.00014 | $0.00441 |
| Opus 5 | $0.00007 | $0.00220 |
| Sonnet 5 | $0.00003 | $0.00088 |
| Haiku 4.5 | $0.00001 | $0.00044 |
Grade A, and why
rforge:deps 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
/rforge:deps - Dependency Graph
Build and visualize dependency relationships in your R package ecosystem.
What It Does
Uses the rforge_deps MCP tool to:
- Parse DESCRIPTION files
- Build dependency graph
- Identify topological order
- Find circular dependencies
- Calculate dependency depth
Usage
# Dependency graph for ecosystem
/rforge:deps
# Graph for specific package
/rforge:deps medfit
# Include reverse dependencies
/rforge:deps --reverse
Output
Returns dependency analysis with:
- Graph Structure: ASCII or Mermaid diagram
- Topological Order: Safe update sequence
- Depth Levels: How deep each dependency goes
- Circulars: Any circular dependency issues
Examples
Ecosystem Dependencies
🔗 DEPENDENCY GRAPH
Level 0 (Core):
└─ medfit
Level 1 (Implementations):
├─ probmed → medfit
├─ medsim → medfit
└─ sensitivity → medfit
Level 2 (Meta):
└─ mediationverse → medfit, probmed, medsim
Topological order: medfit → {probmed, medsim, sensitivity} → mediationverse
Reverse Dependencies
🔗 REVERSE DEPS: medfit
Direct dependents: 3
• probmed (suggests in 2 places)
• medsim (imports 1 function)
• sensitivity (depends)
Indirect dependents: 1
• mediationverse (via probmed, medsim)
Total ecosystem impact: 4 packages
Use When
- Planning version updates
- Understanding ecosystem structure
- Before making breaking changes
- Coordinating multi-package releases
Related Commands
/rforge:impact- See change impact/rforge:cascade- Plan coordinated updates/rforge:release- Plan CRAN submission order
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 · 87 lines · 14 tokens per session scan A 760ec3af23e7
rforge:deps is a command published in the GitHub repository Data-Wise/claude-plugins (7 stars, last pushed 6d ago), licensed MIT. It adds 14 tokens to every session and 441 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.
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.