ClaudeBar is a macOS menu bar application that displays remaining quotas, credits, balances, and reset times for AI coding services. It is for developers who use services such as Claude, Codex, Gemini, Copilot, or Cursor and want to monitor their usage from one place.
Borrowing it
Nothing to install: this file belongs to tddworks/ClaudeBar. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/tddworks/ClaudeBar/main/.claude/skills/implement-feature/SKILL.mdgit clone --depth 1 https://github.com/tddworks/ClaudeBarWrote 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/skills/tddworks/claudebar/implement-feature)<a href="https://agentmods.dev/skills/tddworks/claudebar/implement-feature"><img src="https://agentmods.dev/badge/skills/tddworks/claudebar/implement-feature/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/tddworks/claudebar/implement-feature"><img src="https://agentmods.dev/badge/skills/tddworks/claudebar/implement-feature.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00111 | $0.04639 |
| Opus 5.5 | $0.00044 | $0.01856 |
| Sonnet 5.5 | $0.00022 | $0.00928 |
| Haiku 4.5 | $0.00011 | $0.00464 |
Grade A, and why
implement-feature scanned grade A with 1 finding 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 5d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
given(mockClient).fetch(any()).willReturn(validResponseData) How it starts
The opening of the file, as written. The whole thing — 374 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Implement Feature in ClaudeBar
Implement features using architecture-first design, TDD, rich domain models, and Swift 6.2 patterns.
Workflow Overview
┌─────────────────────────────────────────────────────────────┐
│ 1. ARCHITECTURE DESIGN (Required - User Approval Needed) │
├─────────────────────────────────────────────────────────────┤
│ • Read the design docs — they are the source of truth │
│ • Place the feature: owner, capability, extension point │
│ • Write the design change into the docs first │
│ • Analyze requirements │
│ • Create component diagram │
│ • Show data flow and interactions │
│ • Present to user for review │
│ • Wait for approval before proceeding │
└─────────────────────────────────────────────────────────────┘
│
▼ (User Approves)
┌─────────────────────────────────────────────────────────────┐
│ 2. TDD IMPLEMENTATION │
├─────────────────────────────────────────────────────────────┤
│ • Model tests → models (Quotas / Providers / Domain) │
│ • Module tests → generic rules and workers (DataSources) │
│ • Views, reading the domain directly │
└─────────────────────────────────────────────────────────────┘
Phase 0: Architecture Design (MANDATORY)
Before writing any code, design it in the docs and get user approval. The design docs are the source of truth (AGENTS.md → Design docs are the source of truth).
Step 0: Read the design docs, and place the feature in them
Read the design in order (ARCHITECTURE.md maps it):
USER_JOURNEYS.md (the person and the moment),
CANONICAL_MODEL.md (the tree, the words, each law
and its owner, owned vs offered), TARGET_ARCHITECTURE.md (the
pieces and their one job, the flows), then any docs/features/<context>/<x>/design.md it touches. Then answer:
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 5d ago Changed · +13 lines 81228f4d180c
- 9d ago Changed · +2 lines c1853187843a
- 16d ago Changed · +12 lines db430dd56792
- 22d ago Changed · +7 lines dfbea5a49edc
- 23d ago Changed · +32 lines 5c60a675bc82
- 24d ago Changed 63ddc51cca4b
- 26d ago Changed · +19 lines · +6 tokens per session b7ad9eaa3e99
- 1mo ago Changed 9ccf1ba11ccc
- 1mo ago First seen · 289 lines · 105 tokens per session scan A e7590481a222
implement-feature is a skill published in the GitHub repository tddworks/ClaudeBar (1,560 stars, last pushed today), licensed Apache-2.0. It adds 111 tokens to every session and 4,639 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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