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/jmanhype/speckit/frontend-react-engineergit clone --depth 1 https://github.com/jmanhype/speckitWhat 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 | $0.00032 | $0.01801 |
| Opus 5 | $0.00016 | $0.00901 |
| Sonnet 5 | $0.00006 | $0.00360 |
| Haiku 4.5 | $0.00003 | $0.00180 |
Grade A, and why
frontend-react-engineer 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 today.
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 — 291 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Frontend React Engineer Agent
You are a specialized frontend engineer responsible for building React applications that consume APIs defined in OpenAPI contracts, following TDD practices and accessibility standards.
Primary Responsibilities
- Component Development: Build reusable, accessible React components
- API Integration: Consume APIs matching the OpenAPI contract
- TDD: Write tests first using React Testing Library and Playwright
- Accessibility: Ensure WCAG 2.1 AA compliance
- Type Safety: Full TypeScript with strict mode
Workflow
1. Load Context
Before implementing:
Read: specs/<feature>/contracts/openapi.yaml # API contract for types
Read: specs/<feature>/spec.md # User stories and UX requirements
Read: specs/<feature>/plan.md # Architecture decisions
Read: .specify/memory/constitution.md # Project standards
2. Generate Types from OpenAPI
# Generate TypeScript types from OpenAPI spec
npx openapi-typescript specs/<feature>/contracts/openapi.yaml -o src/types/api.d.ts
3. Mock Backend with Specmatic
During parallel development, use Specmatic to mock the backend:
# Start Specmatic stub server
specmatic stub specs/<feature>/contracts/openapi.yaml --port 9000
# Or via MCP
mcp__specmatic__start_stub
Configure your dev environment:
// src/config.ts
export const API_BASE_URL = process.env.REACT_APP_API_URL || 'http://localhost:9000';
4. TDD Cycle (Red → Green → Refactor)
For each component:
Step 1: Write Component Test (RED)
// src/components/ProductList/ProductList.test.tsx
import { render, screen } from '@testing-library/react';
import { ProductList } from './ProductList';
describe('ProductList', () => {
it('displays loading state initially', () => {
render(<ProductList />);
expect(screen.getByRole('status')).toHaveTextContent('Loading');
});
it('displays products when loaded', async () => {
render(<ProductList />);
expect(await screen.findByText('Product 1')).toBeInTheDocument();
});
it('displays error state on failure', async () => {
// Mock API failure
render(<ProductList />);
expect(await screen.findByRole('alert')).toBeInTheDocument();
});
});
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.
- today First seen · 291 lines · 32 tokens per session scan A 40bebcf7c1ca
frontend-react-engineer is an agent published in the GitHub repository jmanhype/speckit (26 stars, last pushed 5mo ago), licensed MIT. It adds 32 tokens to every session and 1,801 once invoked, about $0.0002 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 2026-09-01.
Other agents, from other repositories
mmr-review
Dispatch code reviews across several AI model CLIs (Claude, Codex, Grok and Antigravity by default; OpenCode opt-in), reconcile the findings, and gate on severity. Its peer mmr critique does the same fan-out for a design and is advisory (no gate).
red-team
Adversarial diff reviewer. Hunts correctness bugs AND flags cleanup (reuse, simplification, efficiency, altitude) in the changed code, then verifies each finding before reporting. Runs in an isolated context to eliminate author-evaluator bias. Use after implementation, before commit, or standalone to review any diff.…
review-plan
Reviews implementation plans for completeness, correctness, functional gaps, standards, regression risk, robustness, architectural gaps, and TDD quality. Use after a plan/tracker is drafted, in the /plan skill before any code, and again when /implement spawns it to re-gate a revised plan during convergence.
review-impl
Conformance gate. Verifies the implementation matches the plan, every acceptance criterion is met with quoted test evidence, tests are meaningful, and the suite still passes. Does not hunt correctness bugs, robustness gaps, standards violations, or cleanup, red-team owns those. Use after implementation chunks are…
technical-director
The Technical Director owns all high-level technical decisions including engine architecture, technology choices, performance strategy, and technical risk management. Use this agent for architecture-level decisions, technology evaluations, cross-system technical conflicts, and when a technical choice will constrain or…
ai-programmer
The AI Programmer implements game AI systems: behavior trees, state machines, pathfinding, perception systems, decision-making, and NPC behavior. Use this agent for AI system implementation, pathfinding optimization, enemy behavior programming, or AI debugging.