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Productivity

model-usage▌

steipete/clawdis · updated Apr 8, 2026

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$npx skills add https://github.com/steipete/clawdis --skill model-usage
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summary

Per-model cost summaries from CodexBar CLI logs for Codex or Claude providers.

  • ›Supports two summary modes: \"current\" (most recent daily model with highest cost) and \"all\" (full model breakdown across all logged days)
  • ›Accepts input via live CodexBar CLI invocation, JSON file, or stdin; outputs as plain text or formatted JSON
  • ›Requires CodexBar CLI installed locally (macOS only via Homebrew; Linux support pending)
  • ›Falls back to last entry in modelsUsed when model breakdowns are u
skill.md

Model usage

Overview

Get per-model usage cost from CodexBar's local cost logs. Supports "current model" (most recent daily entry) or "all models" summaries for Codex or Claude.

TODO: add Linux CLI support guidance once CodexBar CLI install path is documented for Linux.

Quick start

  1. Fetch cost JSON via CodexBar CLI or pass a JSON file.
  2. Use the bundled script to summarize by model.
python {baseDir}/scripts/model_usage.py --provider codex --mode current
python {baseDir}/scripts/model_usage.py --provider codex --mode all
python {baseDir}/scripts/model_usage.py --provider claude --mode all --format json --pretty

Current model logic

  • Uses the most recent daily row with modelBreakdowns.
  • Picks the model with the highest cost in that row.
  • Falls back to the last entry in modelsUsed when breakdowns are missing.
  • Override with --model <name> when you need a specific model.

Inputs

  • Default: runs codexbar cost --format json --provider <codex|claude>.
  • File or stdin:
codexbar cost --provider codex --format json > /tmp/cost.json
python {baseDir}/scripts/model_usage.py --input /tmp/cost.json --mode all
cat /tmp/cost.json | python {baseDir}/scripts/model_usage.py --input - --mode current

Output

  • Text (default) or JSON (--format json --pretty).
  • Values are cost-only per model; tokens are not split by model in CodexBar output.

References

  • Read references/codexbar-cli.md for CLI flags and cost JSON fields.
how to use model-usage

How to use model-usage on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • ›Cursor installed and configured on your development machine
  • ›Node.js version 16.0+ with npm package manager (verify with node --version)
  • ›Active project directory or workspace where you want to add model-usage
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/steipete/clawdis --skill model-usage

The skills CLI fetches model-usage from GitHub repository steipete/clawdis and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/model-usage

Reload or restart Cursor to activate model-usage. Access the skill through slash commands (e.g., /model-usage) or your agent's skill management interface.

⚠

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

Additional Resources

›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills?

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases▌

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

✓

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

✓

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

✓

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

✓

Save 3-5 hours/week on communication overhead

Implementation Guide▌

Prerequisites

  • ›Claude Desktop or compatible AI client
  • ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • ›Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • ⚠Not validating competitive research—verify facts before sharing
  • ⚠Accepting user stories without involving engineering team
  • ⚠Over-relying on frameworks without qualitative judgment
  • ⚠Not customizing outputs to company culture and communication style
  • ⚠Skipping stakeholder validation of generated requirements

Best Practices▌

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • −Don't publish competitive analysis without fact-checking
  • −Don't finalize user stories without engineering review
  • −Don't make prioritization decisions solely on AI scoring
  • −Don't skip customer validation of generated requirements
  • −Don't ignore company-specific context and culture

💡 Pro Tips

  • ★Provide context: company goals, constraints, customer feedback
  • ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • ★Use skill for 70% generation + 30% customization to company needs

When to Use This▌

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path▌

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.7★★★★★45 reviews
  • ★★★★★Shikha Mishra· Dec 16, 2024

    I recommend model-usage for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • ★★★★★Ganesh Mohane· Dec 12, 2024

    We added model-usage from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • ★★★★★Ren Martinez· Dec 8, 2024

    Keeps context tight: model-usage is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • ★★★★★Omar Perez· Dec 4, 2024

    Solid pick for teams standardizing on skills: model-usage is focused, and the summary matches what you get after install.

  • ★★★★★Meera Liu· Nov 27, 2024

    model-usage is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • ★★★★★Charlotte Srinivasan· Nov 23, 2024

    model-usage reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • ★★★★★Ren Martin· Nov 23, 2024

    Registry listing for model-usage matched our evaluation — installs cleanly and behaves as described in the markdown.

  • ★★★★★Sakshi Patil· Nov 3, 2024

    model-usage fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • ★★★★★Chaitanya Patil· Oct 22, 2024

    Registry listing for model-usage matched our evaluation — installs cleanly and behaves as described in the markdown.

  • ★★★★★Omar Ndlovu· Oct 18, 2024

    Useful defaults in model-usage — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

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