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/sangrokjung/claude-forge/plangit clone --depth 1 https://github.com/sangrokjung/claude-forgeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/sangrokjung/claude-forge/plan)<a href="https://agentmods.dev/commands/sangrokjung/claude-forge/plan"><img src="https://agentmods.dev/badge/commands/sangrokjung/claude-forge/plan.svg" alt="Measured on agentmods" height="20"></a>What 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 5.1 | $0.00017 | $0.01187 |
| Opus 5 | $0.00009 | $0.00593 |
| Sonnet 5 | $0.00003 | $0.00237 |
| Haiku 4.5 | $0.00002 | $0.00119 |
Grade A, and why
plan 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 6d 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.
Copies of this mod
7 near-identical copies found in the catalogue:
How it starts
The opening of the file, as written. The whole thing — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Plan Command
This command invokes the planner agent to create a comprehensive implementation plan before writing any code.
What This Command Does
- Restate Requirements - Clarify what needs to be built
- Identify Risks - Surface potential issues and blockers
- Create Step Plan - Break down implementation into phases
- Wait for Confirmation - MUST receive user approval before proceeding
When to Use
Use /plan when:
- Starting a new feature
- Making significant architectural changes
- Working on complex refactoring
- Multiple files/components will be affected
- Requirements are unclear or ambiguous
How It Works
The planner agent will:
- Analyze the request and restate requirements in clear terms
- Break down into phases with specific, actionable steps
- Identify dependencies between components
- Assess risks and potential blockers
- Estimate complexity (High/Medium/Low)
- Present the plan and WAIT for your explicit confirmation
Example Usage
User: /plan I need to add real-time notifications when markets resolve
Agent (planner):
# Implementation Plan: Real-Time Market Resolution Notifications
## Requirements Restatement
- Send notifications to users when markets they're watching resolve
- Support multiple notification channels (in-app, email, webhook)
- Ensure notifications are delivered reliably
- Include market outcome and user's position result
## Implementation Phases
### Phase 1: Database Schema
- Add notifications table with columns: id, user_id, market_id, type, status, created_at
- Add user_notification_preferences table for channel preferences
- Create indexes on user_id and market_id for performance
### Phase 2: Notification Service
- Create notification service in lib/notifications.ts
- Implement notification queue using BullMQ/Redis
- Add retry logic for failed deliveries
- Create notification templates
### Phase 3: Integration Points
- Hook into market resolution logic (when status changes to "resolved")
- Query all users with positions in market
- Enqueue notifications for each user
### Phase 4: Frontend Components
- Create NotificationBell component in header
- Add NotificationList modal
- Implement real-time updates via Supabase subscriptions
- Add notification preferences page
## Dependencies
- Redis (for queue)
- Email service (SendGrid/Resend)
- Supabase real-time subscriptions
## Risks
- HIGH: Email deliverability (SPF/DKIM required)
- MEDIUM: Performance with 1000+ users per market
- MEDIUM: Notification spam if markets resolve frequently
- LOW: Real-time subscription overhead
## Estimated Complexity: MEDIUM
- Backend: 4-6 hours
- Frontend: 3-4 hours
- Testing: 2-3 hours
- Total: 9-13 hours
**WAITING FOR CONFIRMATION**: Proceed with this plan? (yes/no/modify)
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.
- 6d ago First seen · 143 lines · 17 tokens per session scan A c7d86b5fb4ae
plan is a command published in the GitHub repository sangrokjung/claude-forge (825 stars, last pushed 2d ago), licensed MIT. It adds 17 tokens to every session and 1,187 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 2026-08-30.
Other commands, from other repositories
CONTEXT
Slash commands for SPEC-First development workflow.
review
Review plans with multi-angle analysis (mandatory + extended + autonomous).
review
Perform a thorough code review of changes in this branch.
01_confirm
Extract plan from conversation, create file in draft/, auto-apply non-BLOCKING improvements, move to pending.
add-tool
Scaffold and implement a new tool for VisionAgent.
implement
You are in full autonomous implementation mode. Follow the VisionAgent agentic task approach from CLAUDE.md rigorously.