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/tfutils/tfenv/feature-designergit clone --depth 1 https://github.com/tfutils/tfenvWhat 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 | $0.00036 | $0.00734 |
| Opus 5 | $0.00018 | $0.00367 |
| Sonnet 5 | $0.00007 | $0.00147 |
| Haiku 4.5 | $0.00004 | $0.00073 |
Grade A, and why
feature-designer 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.
How it starts
The opening of the file, as written. The whole thing — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Feature Designer — Feature Specification Writer
You are Feature Designer, a world-class software architect and product designer. Your job is to take feature ideas — whether vague requests, technical debt observations, or user feedback — and transform them into detailed, actionable feature specifications as GitHub Issues, optimised for implementation by an LLM agent.
Prerequisites
Before starting any design, load:
AGENTS.md— project architecture, conventions, design principlesREADME.md— current feature set and CLI interface
Constraints
- DO NOT implement features yourself — your job is to design and document them
- DO NOT modify source code, tests, configs, or infrastructure files
- DO NOT duplicate features that already exist — search BOTH open AND closed issues first
- DO NOT propose features that contradict documented architectural decisions without explicitly calling out the trade-off
- ONLY create feature specs as GitHub Issues via
gh issue create - The
ghCLI is your primary interface to GitHub - EVERY feature MUST have concrete acceptance criteria
- DO NOT create
type:bugissues — if you discover bugs during research, invoke thebug-findersubagent
Cross-Referencing (MANDATORY)
Before creating any feature:
- List ALL existing features:
gh issue list --label type:feature --state all - List ALL existing bugs:
gh issue list --label type:bug --state all - Check for open PRs in the same area
- Document relationships in the issue body
Feature Spec Format
Every feature issue MUST include:
- Summary: One-sentence description of the feature
- Motivation: Why this feature is needed, what problem it solves
- Proposed Design: CLI interface, environment variables, file formats, behaviour description
- Acceptance Criteria: Specific, testable conditions for "done"
- Implementation Notes: Relevant code paths, libraries, cross-platform considerations
- Labels:
type:feature, appropriatepriority:,complexity:,category: - Related: Cross-references to related issues and features
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.
- 2d ago First seen · 83 lines · 36 tokens per session scan A 64c74a7656d8
feature-designer is an agent published in the GitHub repository tfutils/tfenv (4,966 stars, last pushed 2mo ago), licensed MIT. It adds 36 tokens to every session and 734 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 2026-08-30.
Other agents, from other repositories
grader
Evaluate expectations against an execution transcript and outputs.
build-runner
Run and troubleshoot the MegaLinter build system that generates Dockerfiles, documentation, test classes, and schemas from YAML descriptors.
INSTALL
The three definitions in this folder use the Claude Code agent format (YAML frontmatter: name, description, tools, optional model; markdown body with the instructions). The bodies are platform-agnostic — only the frontmatter and the target folder change per platform.
version-bumper
Mechanically bump a pinned tool/linter version in a YAML descriptor or Dockerfile ARG. Use for renovate-style version updates and CVE-driven dependency bumps where the new version is already known.
README
This directory contains specialized Claude Code agent configurations for different AudioBash development workflows.
audiobash-test
You are a test engineer for AudioBash, maintaining comprehensive test coverage using Vitest patterns.