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/impactgit 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 | $0.00011 | $0.00501 |
| Opus 5 | $0.00005 | $0.00251 |
| Sonnet 5 | $0.00002 | $0.00100 |
| Haiku 4.5 | $0.00001 | $0.00050 |
Grade A, and why
rforge:impact 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
/rforge:impact - Change Impact Analysis
Analyze the ripple effects of changes across your R package ecosystem.
What It Does
Uses the rforge_impact MCP tool to:
- Identify affected packages
- Estimate cascade workload
- Find breaking changes
- Calculate update priority
- Provide mitigation strategies
Usage
# Impact of recent changes
/rforge:impact
# Impact of specific change
/rforge:impact "Rename extract_mediation to extract_med"
# Impact for function change
/rforge:impact --function extract_mediation
Output
Returns impact assessment with:
- Severity: LOW/MEDIUM/HIGH/CRITICAL
- Affected Packages: List with details
- Cascade Workload: Estimated time
- Breaking Changes: Functions/APIs affected
- Mitigation: Suggested strategies
Examples
Medium Impact
📊 IMPACT ANALYSIS
Severity: MEDIUM
Affected: 2 packages (probmed, medsim)
probmed:
• 1 function call to update
• Tests: 12 tests reference old API
• Est. work: 2 hours
medsim:
• 1 vignette example
• Est. work: 1 hour
Total cascade: 3 hours
Recommended: Update & release in sequence
High Impact (Breaking Change)
⚠️ IMPACT ANALYSIS
Severity: HIGH (Breaking change)
Affected: 3 packages + indirect
Direct Impact:
• probmed: 5 functions, 47 test cases
• medsim: 2 examples, 1 vignette
• sensitivity: 1 function call
Indirect Impact:
• mediationverse: May need meta-update
Total cascade: 8-12 hours
Recommended: Deprecation path + major version bump
Use When
- Before making API changes
- Planning breaking changes
- Estimating release workload
- Coordinating ecosystem updates
Related Commands
/rforge:deps- See dependency structure/rforge:cascade- Plan coordinated updates/rforge:doc-check- Check documentation impact
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 · 95 lines · 11 tokens per session scan A d061066af462
rforge:impact is a command published in the GitHub repository Data-Wise/claude-plugins (7 stars, last pushed 5d ago), licensed MIT. It adds 11 tokens to every session and 501 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.