Borrowing it
Nothing to install: this file belongs to basher83/lunar-claude. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/basher83/lunar-claude/main/.claude/commands/design-validation/landscape-research-protocol.mdgit 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/landscape-research-protocol)<a href="https://agentmods.dev/commands/basher83/lunar-claude/landscape-research-protocol"><img src="https://agentmods.dev/badge/commands/basher83/lunar-claude/landscape-research-protocol.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 | $0.00000 | $0.01801 |
| Opus 5 | $0.00000 | $0.00901 |
| Sonnet 5 | $0.00000 | $0.00360 |
| Haiku 4.5 | $0.00000 | $0.00180 |
Grade A, and why
landscape-research-protocol 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 8d 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 — 268 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Execute systematic research to discover existing solutions before finalizing a design. This protocol ensures you've thoroughly explored the landscape and can justify any decision to build rather than integrate.
Use this command BEFORE designing a solution to avoid reinventing the wheel.
Variables
PROBLEM_DOMAIN: $1 (e.g., "markdown linting", "API gateway", "workflow orchestration")
KEY_REQUIREMENTS: $2 (file path to requirements doc OR comma-separated list)
OUTPUT_DIR: docs/reviews/design-validation/landscape-research/
FILE_NAME: <problem-domain-slug>.md
Instructions
- IMPORTANT: If no
PROBLEM_DOMAINis provided, stop and ask the user to provide it. - If
KEY_REQUIREMENTSis a file path, read it first. - Verify
OUTPUT_DIRexists, create it if it doesn't. - Save research output to
OUTPUT_DIR/FILE_NAME. - Be thorough - the goal is to AVOID building what already exists.
- Use web search tools to conduct actual research, not just hypotheticals.
Workflow
Phase 1: Open Source Discovery
Search Targets:
- GitHub, GitLab, Sourcehut repositories
- Package registries (npm, PyPI, Maven Central, crates.io, etc.)
- Awesome lists and curated collections
- Stack Overflow discussions and tool recommendations
For each relevant project found, document:
| Project | Stars/Adoption | Key Features | Gaps vs. Needs | License | Maturity |
|---|---|---|---|---|---|
| [name] | [metrics] | [strengths] | [missing] | [license] | [active/stale] |
Assess reusability:
- Components/libraries we could use directly
- Patterns or architectures to learn from
- Integration points available
Phase 2: Commercial/SaaS Solutions
Search Targets:
- Product Hunt, G2, Capterra
- Industry analyst reports (if available)
- Vendor comparisons and review sites
- LinkedIn for company/product discovery
For each relevant solution, document:
| Solution | Pricing | Feature Match | Integration | Viability |
|---|---|---|---|---|
| [name] | [cost] | [% needs met] | [API quality] | [stability] |
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.
- 8d ago First seen · 268 lines · 0 tokens per session scan A 708567de6a27
landscape-research-protocol is a command published in the GitHub repository basher83/lunar-claude (22 stars, last pushed yesterday), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,801 tokens. 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.