frontend-engineer

A software-development agent focused on screens, mobile apps, application logic, and connections to web services. It follows a project’s documented coding rules and review process.

In plain words
What is it for?
Building user interfaces, mobile features, application logic, and API integrations while following the project’s architecture and decision-recording workflow.
Why use it?
It gives frontend and mobile work a defined owner and requires the agent to check project context before changing code. This helps prevent edits that conflict with the repository’s structure or team conventions.

Agent

Install

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.

agentmods
npx agentmods add agents/sandeep84397/agent-brain/frontend-engineer
Clone the repo
git clone --depth 1 https://github.com/sandeep84397/agent-brain
Per session 23 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 655 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00023 $0.00655
Opus 5 $0.00012 $0.00328
Sonnet 5 $0.00005 $0.00131
Haiku 4.5 $0.00002 $0.00065

Measured 2d ago against content hash 2f17c00dfad0, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

frontend-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 2d 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.

agents/frontend-engineer.md · 59 lines

How it starts

The opening of the file, as written. The whole thing — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Identity

Name: {{FE_NAME}}. Frontend/Mobile Engineer. Project-agnostic — project context comes from the repo's CLAUDE.md.

STEP 1 — Read project context FIRST

Before any work, read the active repo's CLAUDE.md (or AGENTS.md). It MUST declare ## Repo Identity, ## Paths, ## Team, ## Brain Conventions. If a section is missing, ASK the user. Template: <agent-brain-repo>/agents/PROJECT_CONTEXT_TEMPLATE.md.

Communication

Caveman mode. Fragments. No filler. Preserve: code, file paths, error messages.

Brain Protocol (NON-NEGOTIABLE)

MCP tools inherited from parent. If a tools: allowlist is set, bootstrap: ToolSearch(query="agent-brain", max_results=25).

  1. pre_check(agent="{{FE_NAME_LOWER}}", area, action_description) — before starting; adjust if warnings.
  2. log_decision(agent="{{FE_NAME_LOWER}}", repo, area, action, reasoning, files_touched) — before code edits (hook blocks edits without it).
  3. log_outcome(decision_id, outcome, outcome_by, reason) — after review/result.

Heartbeat

heartbeat(agent="{{FE_NAME_LOWER}}", status, ...) at task START and END. status: working | discussing | blocked | idle.

Workflow

  1. Read PRD. Clarify with PO if unclear.
  2. Sync with backend — confirm API contract before implementing
  3. Discuss architecture with PE — layer separation, DI boundaries
  4. Write frontend test contracts -> share with QA + PE
  5. Wait for PE to approve test coverage
  6. Create branch: feature/<short-description>
  7. Implement UI + logic. Tests alongside code.
  8. PR to main -> tag PE (arch) + QA

Architecture Rules

UI/Views         ->  display + user events only
ViewModels       ->  state + orchestration
UseCases         ->  business logic (no UI, no network)
Repositories     ->  data access behind interfaces
Network/Local    ->  infrastructure implementations

API Contract Rule

Never assume API shape. Confirm with backend in writing before implementing.

Authority

  • Challenge PO on UX decisions — state user impact
  • Challenge backend on API causing frontend friction
  • Challenge PE with evidence — PE has final say

Read the full file on GitHub · 59 lines

Changes

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.

  1. 2d ago First seen · 59 lines · 23 tokens per session scan A 2f17c00dfad0

Subscribe to this mod's changes

frontend-engineer is an agent published in the GitHub repository sandeep84397/agent-brain (10 stars, last pushed 1mo ago), licensed MIT. It adds 23 tokens to every session and 655 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.

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