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 skills add basher83/lunar-claude --skill adr-methodologygit 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/skills/basher83/lunar-claude/adr-methodology)<a href="https://agentmods.dev/skills/basher83/lunar-claude/adr-methodology"><img src="https://agentmods.dev/badge/skills/basher83/lunar-claude/adr-methodology/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/basher83/lunar-claude/adr-methodology"><img src="https://agentmods.dev/badge/skills/basher83/lunar-claude/adr-methodology.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Memory Poisoning · line 66 Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.Fix: Protect agent memory and state from modification by untrusted content. Use read-only memory for critical instructions and validate all state changes.
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 | $0.00030 | $0.01101 |
| Opus 5.5 | $0.00012 | $0.00440 |
| Sonnet 5.5 | $0.00006 | $0.00220 |
| Haiku 4.5 | $0.00003 | $0.00110 |
Grade A, and why
adr-methodology 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 2mo 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 — 151 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ADR Methodology
Structured frameworks for documenting architectural decisions with human-in-the-loop AI assistance.
Core Principle
AI handles drafting, formatting, and enumeration. Humans provide project-specific context, stakeholder awareness, and final decision accountability.
AI assists with:
- Research and enumeration of options
- Consistent formatting
- Risk/trade-off summarization
- Matrix generation
Humans provide:
- Project-specific context and constraints
- Stakeholder empathy and political nuance
- Final decision accountability
Workflow Stages
Stage 1: Context to Criteria (/adr-assistant:new)
Gather decision context and generate assessment criteria.
- Ask for problem description, constraints, stakeholders, initial options
- Select appropriate framework (Salesforce Well-Architected or Technical Trade-off)
- Generate criteria grouped by framework pillars
- For each criterion: name, rationale for this decision, definition of "good"
- Write criteria to
.claude/adr-session.yaml - Prompt user to refine criteria before analysis
Stage 2: Options Matrix (/adr-assistant:analyze)
Evaluate options against criteria with risk ratings.
- Read criteria from
.claude/adr-session.yaml - For each option, rate against each criterion (Low/Medium/High risk)
- Include rationale for each rating
- Generate comparison matrix table
- Write analysis to state file
- Prompt user to refine ratings before generation
Stage 3: ADR Generation (/adr-assistant:generate)
Output final ADR document using MADR template.
- Read criteria and analysis from state file
- Ask user which option they're choosing and why
- Generate ADR with AI disclosure
- Auto-detect next ADR number from
docs/adr/ - Write ADR file
- Clear state file
Assessment Frameworks
Salesforce Well-Architected (Trusted/Easy/Adaptable)
Use for enterprise decisions with security, UX, and scale concerns.
Trusted: Data security, compliance, access control, audit/governance Easy: User experience, deployment complexity, integration effort, maintenance Adaptable: Scalability, future flexibility, cost trajectory, team skill alignment
What ships with it
3 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.
- 2mo ago First seen · 151 lines · 30 tokens per session scan A 1a392640fb1b
adr-methodology is a skill published in the GitHub repository basher83/lunar-claude (23 stars, last pushed today), licensed MIT. It adds 30 tokens to every session and 1,101 once invoked, about $0.0001 per session on Opus 5.5. 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.
adr-methodology compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| adr-methodologybasher83/lunar-claude | Skill | 23 | today | MIT | A | 30 | Claude Code |
| imagegenopenai/codex | Skill | 134k | today | Apache-2.0 | A | 113 | Codex |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 3d ago | MIT | A | 200 | Claude Code, Codex |
| playwright-climicrosoft/playwright | Skill | 100k | today | Apache-2.0 | A | 19 | Claude Code |
| browser-automationopenclaw/openclaw | Skill | 394k | today | MIT | A | 36 | Claude Code |
| systematic-debuggingobra/superpowers | Skill | 315k | 6d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 95 | Claude Code |