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/generategit 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/generate)<a href="https://agentmods.dev/commands/basher83/lunar-claude/generate"><img src="https://agentmods.dev/badge/commands/basher83/lunar-claude/generate.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 5.1 | $0.00006 | $0.00772 |
| Opus 5 | $0.00003 | $0.00386 |
| Sonnet 5 | $0.00001 | $0.00154 |
| Haiku 4.5 | $0.00001 | $0.00077 |
Grade A, and why
generate 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 — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Generate the final Architecture Decision Record document.
Use the adr-methodology skill for ADR templates.
Prerequisites Check
Read .claude/adr-session.yaml to retrieve session state.
If file doesn't exist or status is not analyzed:
- Inform user that options analysis must be completed first
- Instruct them to run
/adr-assistant:analyzeto complete the analysis - Stop processing
Phase 1: Decision Capture
Display the options matrix summary from state.
Ask user:
- Which option are you choosing?
- Why? (Reference specific criteria and trade-offs)
- Who are the decision-makers? (Names for the ADR header)
Phase 2: Consequences Analysis
Based on the chosen option and its ratings, generate:
What becomes easier:
- Benefits from Low-risk ratings
- Advantages over rejected options
What becomes harder:
- Trade-offs from Medium/High ratings
- Capabilities lost by rejecting alternatives
Present for user review and refinement.
Phase 3: ADR Number Detection
Scan docs/adr/ directory for existing ADR files.
If directory exists:
- Find highest numbered ADR (pattern:
NNNN-*.md) - Next number = highest + 1
If directory doesn't exist:
- Create
docs/adr/directory - Start at 0001
Phase 4: Generate ADR
Use MADR template format. Generate complete ADR including:
- Header: Number, title, status (Accepted), date, decision-makers
- AI Disclosure: Note that Claude assisted; humans reviewed and decided
- Context: Summarize problem and constraints from session
- Decision Drivers: Key criteria that influenced the decision
- Considered Options: Each option with pros/cons from analysis
- Decision: Chosen option with rationale
- Consequences: What becomes easier/harder
- Appendix: Full assessment matrix from analysis
Phase 5: Output Path
Determine output path:
If $1 provided:
- Use
$1as the output path
If no argument:
- Generate path:
docs/adr/NNNN-[slugified-title].md - Slugify: lowercase, hyphens for spaces, remove special characters
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 · 143 lines · 6 tokens per session scan A 00ec45e2a303
generate is a command published in the GitHub repository basher83/lunar-claude (22 stars, last pushed today), licensed MIT. It adds 6 tokens to every session and 772 once invoked, about $0.0000 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 2026-09-03.
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.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
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.