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 commands/basher83/lunar-claude/codebase-healthgit clone --depth 1 https://github.com/basher83/lunar-claudeWrote 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/commands/basher83/lunar-claude/codebase-health)<a href="https://agentmods.dev/commands/basher83/lunar-claude/codebase-health"><img src="https://agentmods.dev/badge/commands/basher83/lunar-claude/codebase-health.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 | $0.00015 | $0.00690 |
| Opus 5 | $0.00008 | $0.00345 |
| Sonnet 5 | $0.00003 | $0.00138 |
| Haiku 4.5 | $0.00002 | $0.00069 |
Grade A, and why
sc-codebase-health 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 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.
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 — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyze the codebase at $ARGUMENTS (or current directory if not specified) for code quality issues.
Phase 1: Check Context
If a context packet exists !fd 'context-packet-.*\.md' .agent-history/, use it to focus the analysis.
If not, briefly ask: "What areas should I focus on? (or 'all' for comprehensive scan)"
Phase 2: Launch Analysis Agents
Launch ALL 6 agents simultaneously using Task`:
Task(subagent_type: "sc-refactor:sc-structural-reviewer", run_in_background: true,
prompt: "Analyze [target] for structural completeness. Focus on: [context areas]")
Task(subagent_type: "sc-refactor:sc-duplication-hunter", run_in_background: true,
prompt: "Analyze [target] for code duplication. Focus on: [context areas]")
Task(subagent_type: "sc-refactor:sc-abstraction-critic", run_in_background: true,
prompt: "Analyze [target] for unnecessary abstractions. Focus on: [context areas]")
Task(subagent_type: "sc-refactor:sc-naming-auditor", run_in_background: true,
prompt: "Analyze [target] for naming inconsistencies. Focus on: [context areas]")
Task(subagent_type: "sc-refactor:sc-dead-code-detector", run_in_background: true,
prompt: "Analyze [target] for dead/unreferenced code. Focus on: [context areas]")
Task(subagent_type: "sc-refactor:sc-test-organizer", run_in_background: true,
prompt: "Analyze [target] test organization. Focus on: [context areas]")
Phase 3: Collect and Synthesize
Wait for all agents to complete. Consolidate findings:
# Codebase Health Report
## Executive Summary
[3-5 sentences on overall health]
## Quick Wins
[High-confidence issues fixable in < 30 min]
## Recommended Refactors
[Larger changes worth doing]
## Tech Debt Backlog
[Valid but not urgent]
## Skip These
[Not worth the effort - explain why]
Cross-reference findings (e.g., duplication often relates to abstraction issues).
Phase 4: User Choice
Use AskUserQuestion:
| Option | Label | Description |
|---|---|---|
| fix-plan | Generate fix plan | Step-by-step instructions for Quick Wins and Recommended Refactors |
| auto-fix | Auto-fix quick wins | Make code changes for quick wins (max 10 iterations) |
| report-only | Just the report | Output report and stop |
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 First seen · 84 lines · 15 tokens per session scan A 59ccbe274aa8
sc-codebase-health is a command published in the GitHub repository basher83/lunar-claude (22 stars, last pushed yesterday), licensed MIT. It adds 15 tokens to every session and 690 once invoked, about $0.0001 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 commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.