next — ai-billing toktrack, community, ai-billing, ide skills, ai-coding, ai-cost, ai-tools, anthropic, claude-code, codex-cli, Claude Code

v1.0.0
GitHub

About this Skill

Perfect for AI Agents needing real-time LLM token usage insights and ultra-fast cost tracking, particularly those working with Claude Code. Session start - check progress, suggest next task

# Core Topics

ai-billing ai-coding ai-cost ai-tools anthropic claude-code
mag123c mag123c
[55]
[3]
Updated: 3/10/2026

Killer-Skills Review

Decision support comes first. Repository text comes second.

Reviewed Landing Page Review Score: 9/11

Killer-Skills keeps this page indexable because it adds recommendation, limitations, and review signals beyond the upstream repository text.

Original recommendation layer Concrete use-case guidance Explicit limitations and caution Quality floor passed for review Locale and body language aligned
Review Score
9/11
Quality Score
56
Canonical Locale
en
Detected Body Locale
en

Perfect for AI Agents needing real-time LLM token usage insights and ultra-fast cost tracking, particularly those working with Claude Code. Session start - check progress, suggest next task

Core Value

Empowers agents to track LLM token usage in real-time, providing valuable insights into cost optimization and utilization, leveraging Git Log analysis and presenting data through a structured flow, utilizing protocols like Git and file formats such as Markdown.

Ideal Agent Persona

Perfect for AI Agents needing real-time LLM token usage insights and ultra-fast cost tracking, particularly those working with Claude Code.

↓ Capabilities Granted for next

Analyzing LLM token usage for cost optimization
Tracking task completion rates through Git Log analysis
Presenting real-time insights into AI model performance

! Prerequisites & Limits

  • Requires access to Git repository
  • Designed specifically for LLM token usage analysis
  • Compatibility limited to AI tools like Claude Code

Source Boundary

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

Curated Collection Review

Reviewed In Curated Collections

This section shows how Killer-Skills has already collected, reviewed, and maintained this skill inside first-party curated paths. For operators and crawlers alike, this is a stronger signal than treating the upstream README as the primary story.

Reviewed Collection

CLI Workflow Tools to Install First

Reviewed 2026-04-17

Reviewed on 2026-04-17 against CLI-native workflow fit, installation clarity, operator handoff, and terminal-first execution value. This page is now positioned as an install-first terminal workflow entry point instead of a generic AI tools list.

We prioritize this page because terminal-intent users usually need a shortlist they can install and validate without leaving the shell for another round of vague tooling research.

12 entries Killer-Skills editorial review within the recovery-focused authority queue.
Reviewed Collection

OpenAI Workflow Tools for Prompt, Eval, and Agent Teams

Reviewed 2026-04-17

Reviewed on 2026-04-17 for setup clarity, eval usefulness, runtime visibility, and maintainer reliability. We kept the tools that help OpenAI teams move from experiments to repeatable production routines.

OpenAI visitors usually arrive with a concrete job: improve prompts, add evals, debug runtime behavior, or make agent operations easier to hand off. This page narrows the shortlist around those jobs.

12 entries Killer-Skills editorial review with monthly collection checks.
Reviewed Collection

Developer Workflow Tools to Install First

Reviewed 2026-04-17

Reviewed on 2026-04-17 against coding workflow fit, installation clarity, review and testing value, and release guardrails. This page is now positioned as an install-first developer workflow entry point instead of a vague tooling roundup.

We prioritize this page because developer-intent users usually need one toolchain they can install, validate, and prove inside a real coding loop before they standardize it across the team.

12 entries Killer-Skills editorial review within the recovery-focused authority queue.
After The Review

Decide The Next Action Before You Keep Reading Repository Material

Killer-Skills should not stop at opening repository instructions. It should help you decide whether to install this skill, when to cross-check against trusted collections, and when to move into workflow rollout.

Install Path

Start With Installation And Validation

If this skill is worth continuing with, the next step is to confirm the install command, CLI write path, and environment validation.
Trusted Recheck

Cross-Check Against Trusted Picks

If you are still comparing multiple skills or vendors, go back to the trusted collection before amplifying repository noise.
Workflow

Move To Workflow Collections For Team Rollout

When the goal shifts from a single skill to team handoff, approvals, and repeatable execution, move into workflow collections.
Labs Demo

Browser Sandbox Environment

⚡️ Ready to unleash?

Experience this Agent in a zero-setup browser environment powered by WebContainers. No installation required.

Boot Container Sandbox

FAQ & Installation Steps

These questions and steps mirror the structured data on this page for better search understanding.

? Frequently Asked Questions

What is next?

Perfect for AI Agents needing real-time LLM token usage insights and ultra-fast cost tracking, particularly those working with Claude Code. Session start - check progress, suggest next task

How do I install next?

Run the command: npx killer-skills add mag123c/toktrack/next. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for next?

Key use cases include: Analyzing LLM token usage for cost optimization, Tracking task completion rates through Git Log analysis, Presenting real-time insights into AI model performance.

Which IDEs are compatible with next?

This skill is compatible with Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use the Killer-Skills CLI for universal one-command installation.

Are there any limitations for next?

Requires access to Git repository. Designed specifically for LLM token usage analysis. Compatibility limited to AI tools like Claude Code.

↓ How To Install

  1. 1. Open your terminal

    Open the terminal or command line in your project directory.

  2. 2. Run the install command

    Run: npx killer-skills add mag123c/toktrack/next. The CLI will automatically detect your IDE or AI agent and configure the skill.

  3. 3. Start using the skill

    The skill is now active. Your AI agent can use next immediately in the current project.

Upstream Repository Material

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

Upstream Source

next

Install next, an AI agent skill for AI agent workflows and automation. Works with Claude Code, Cursor, and Windsurf with one-command setup.

SKILL.md
Readonly
Upstream Repository Material
The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.
Supporting Evidence

Next

Flow

Read Planning → Git Log → Analyze → Present → Suggest /clarify

Execution

  1. Read Planning

    bash
    1# Read ONLY the latest planning file (by date prefix YYYYMMDD-)
2# e.g., 20260205-improvements.md > 20260128-cli-parsers.md
3# Check checkbox status: [ ] incomplete, [x] complete

  2. Git Log

    bash
    1git log --oneline -5
2git status --short

  3. Analyze

    • Identify current phase
    • Count completed/total tasks
    • Identify next priority task

  4. Present (table format)

    PhaseStatusProgress
    Phase 0✅5/5
    Phase 1🔄3/4

  5. Suggest

    • Summarize next task
    • Suggest running /clarify

Output Format

markdown
1## Current Status
2- Phase: {current_phase}
3- Progress: {completed}/{total} tasks
4
5## Next Task
6**{task_id}: {task_name}**
7{brief_description}
8
9## Action
10Run `/clarify` to start: {task_summary}

Rules

  • Read only the latest dated planning file (highest YYYYMMDD- prefix)
  • If no planning files → infer from git log + code state
  • Keep output concise (5-10 lines)
  • Always suggest /clarify connection

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