Claude Scholar is a semi-automated research assistant for academic research and software development, supporting literature review, coding, experiments, reporting, writing, and project knowledge management. Computer science and AI researchers use it across the research workflow with several coding-agent platforms; the catalogue contains its skills, commands, agents, hooks, plugin, and instruction.
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.
git clone --depth 1 https://github.com/Galaxy-Dawn/claude-scholarWrote 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/galaxy-dawn/claude-scholar/plan)<a href="https://agentmods.dev/commands/galaxy-dawn/claude-scholar/plan"><img src="https://agentmods.dev/badge/commands/galaxy-dawn/claude-scholar/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 | $0.00025 | $0.00798 |
| Opus 5 | $0.00013 | $0.00399 |
| Sonnet 5 | $0.00005 | $0.00160 |
| Haiku 4.5 | $0.00003 | $0.00080 |
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 8d 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.
How it starts
The opening of the file, as written. The whole thing — 114 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Plan Command
This command creates 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
This command should:
- 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
Assistant:
# 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.py
- Implement notification queue using Celery/Redis
- Add retry logic for failed deliveries
- Create notification templates (Jinja2)
### 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: API/Frontend
- Create notification endpoints (GET /api/notifications, POST /api/notifications/read)
- WebSocket support for real-time updates
- Frontend notification bell component
- Notification preferences page
## Dependencies
- Redis (for Celery queue)
- Celery (task queue)
- Email service (SendGrid/Resend/SMTP)
- WebSocket server (channels/websocket)
## Risks
- HIGH: Email deliverability (SPF/DKIM required)
- MEDIUM: Performance with 1000+ users per market
- MEDIUM: Notification spam if markets resolve frequently
- LOW: WebSocket connection overhead
## Estimated Complexity: MEDIUM
- Backend: 4-6 hours
- API/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.
- 8d ago First seen · 114 lines · 25 tokens per session scan A c5751a6e642d
plan is a command published in the GitHub repository Galaxy-Dawn/claude-scholar (5,349 stars, last pushed 11d ago), licensed MIT. It adds 25 tokens to every session and 798 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
pr-review
Generate a PR review report aggregating quality scan, coverage, complexity, and breaking changes.
task
Start TASK phase — task decomposition.
fix
User-triggered workflow to automatically fix open issues.
brainstorm
Pre-implementation collaborative requirement exploration and design (conversational brainstorm).
baseline
Manage violation baselines for gradual adoption.
threat-model
Generate a STRIDE-based threat model with auto-populated entry points and data stores.