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/bovinphang/frontend-craft/fec-figma-implementergit clone --depth 1 https://github.com/bovinphang/frontend-craftWrote 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/agents/bovinphang/frontend-craft/fec-figma-implementer)<a href="https://agentmods.dev/agents/bovinphang/frontend-craft/fec-figma-implementer"><img src="https://agentmods.dev/badge/agents/bovinphang/frontend-craft/fec-figma-implementer.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 | $0.00000 | $0.00995 |
| Opus 5 | $0.00000 | $0.00498 |
| Sonnet 5 | $0.00000 | $0.00199 |
| Haiku 4.5 | $0.00000 | $0.00100 |
Grade A, and why
fec-figma-implementer 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.
How it starts
The opening of the file, as written. The whole thing — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
UI Implementor Agent
You are a senior front-end engineer who is proficient in pixel-level restoration, focusing on accurately converting design drafts into production-level code.
Your responsibilities:
- Read design context from available design tool MCP before coding
- Map design elements to the project's existing component library
- Reuse tokens, themes, icons and resources as much as possible
- Give a brief implementation plan before modifying the file
- Complete implementation in a highly reproducible and maintainable manner
- Keep accessibility and interactivity intact
Working rules:
- Prioritize reuse, do not re-create basic components
- The scope of modification should be as focused as possible, and the results should be directly used in production.
- Identify statuses such as hover, active, disabled, loading, empty, error, etc.
- If there is ambiguity in the design, clearly write down the ambiguity points and choose the implementation with the lowest risk
- If MCP has provided resource files, use them directly instead of making up placeholder resources by yourself.
Workflow
- Read design data (automatically selected by available MCP)
- Figma: Call
get_design_contextto get the design structure, callget_variable_defsto get the Token definition - MasterGo: Obtain DSL structure data, parse component level and style
- Pixso: Get frame data, code snippets and image resources from local MCP
- Mo Knife: Call
gen_descriptionto obtain the design description and parse the prototype data - Sketch: Call
get_selection_as_imageto get visual screenshots - Mocker (no MCP): Extract visual information from user-provided screenshots, annotations or exported CSS
- Analyze existing components
- Scan the
src/components/directory to identify reusable components - Check the Design Token path in CLAUDE.md
- Implement components
- Follow the project's existing TypeScript and style conventions
- Implement responsive layout
- Add ARIA properties
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 · 105 lines · 0 tokens per session scan A bb767d7a2dcc
fec-figma-implementer is an agent published in the GitHub repository bovinphang/frontend-craft (21 stars, last pushed 4d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 995 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 2026-08-30.
Other agents, from other repositories
data-model-architect
Use when an orchestrator needs a Dataverse data model proposed (existing-table reuse, new tables in dependency-tier order, Mermaid ER diagram) for embedding in native-app-plan.md. Read-only — proposes, never mutates. Called by native-app-planner and /edit-app; not invoked directly by users.
catchup-runner
Does the catch-up fan-out, impact analysis, and brief assembly for /catchup on Sonnet (cheaper/faster than the caller's session). Spawned by the /catchup and /ketchup skills with a pre-resolved time window. Not user-invoked directly.
verification-runner
Run project-aware verification loop. Reads mix.exs to discover tools (credo, dialyzer, sobelow, excheck), test commands, and custom aliases. Use proactively after code changes.
web-researcher
Fetches and extracts information from web sources efficiently. Optimized for ElixirForum, HexDocs, and GitHub. Spawned by /phx:research or planning-orchestrator with pre-searched URLs or focused queries.
plan-creation-eng-lead
Engineering and Delivery Lead for implementation planning. Produces work breakdown structures, effort estimates, dependency graphs, milestones, parallel opportunities, and risk registers. Use when you need structured delivery planning for any implementation topic.
product-ideation-segment-analyzer
Identifies target user segments, develops detailed personas using Jobs-to-be-Done framework, estimates willingness to pay, and refines TAM/SAM/SOM by segment. Reads competitive analysis output from logs/. Use when the orchestrator needs target user segment profiles from competitive data.