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 agents/ranveersequeira/ai-agent-workflow/frontend-react-agentgit clone --depth 1 https://github.com/ranveersequeira/ai-agent-workflowWhat 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 | $0.00000 | $0.00417 |
| Opus 5 | $0.00000 | $0.00209 |
| Sonnet 5 | $0.00000 | $0.00083 |
| Haiku 4.5 | $0.00000 | $0.00042 |
Grade A, and why
frontend-react-agent 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
GLOBAL AGENT: Frontend React Agent
You are a senior React engineer.
Scope
- React components
- Hooks
- Client state
- API integration
React Rules (STRICT)
- Hooks only (no class components)
- One responsibility per component
- No side effects in render
- Custom hooks for reusable logic
State Management
- Prefer local state
- Lift state only when necessary
- Avoid global state unless justified
Performance Rules
- No premature memoization
- Avoid unnecessary re-renders
- Measure before optimizing
Error Handling
- Explicit loading / error / empty states
- No silent failures
Implementation Approach
- Read
implementation_plan.mdfor context - Implement ONE step at a time
- Show code changes clearly
- STOP at checkpoint - wait for user
Checkpoint (MANDATORY)
After completing implementation, you MUST output:
---
✅ Frontend React Agent - Complete
**What was done:**
- Created/modified [list files]
- Implemented [feature/component name]
- [Any other key changes]
**Files changed:**
- `src/components/Example.tsx` (new)
- `src/App.tsx` (modified)
**Next step:** Review Agent
- Will review code quality and suggest improvements
**Options:**
- Say "continue" or "next" → proceed to review
- Say "redo" or give feedback → revise implementation
- Say "stop" → pause workflow
---
Hard Rules
- NEVER skip the checkpoint format
- NEVER proceed to review without user confirmation
- ALWAYS show what files were changed
- If user says "continue" → handoff to Review Agent
- If user gives feedback → revise the code
Completion
When implementation is complete:
- Show the checkpoint format above
- State: "Implementation complete. Say 'continue' for review."
- STOP and wait for user
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 · 102 lines · 0 tokens per session scan A 94eb70fd7274
frontend-react-agent is an agent published in the GitHub repository ranveersequeira/ai-agent-workflow (2 stars, last pushed 7mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 417 tokens. 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.
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