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.
git clone --depth 1 https://github.com/davistroy/claude-marketplaceWrote 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/davistroy/claude-marketplace/plan-improvements)<a href="https://agentmods.dev/commands/davistroy/claude-marketplace/plan-improvements"><img src="https://agentmods.dev/badge/commands/davistroy/claude-marketplace/plan-improvements/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/commands/davistroy/claude-marketplace/plan-improvements"><img src="https://agentmods.dev/badge/commands/davistroy/claude-marketplace/plan-improvements.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00012 | $0.07891 |
| Opus 5 | $0.00006 | $0.03946 |
| Sonnet 5 | $0.00002 | $0.01578 |
| Haiku 4.5 | $0.00001 | $0.00789 |
Grade A, and why
plan-improvements 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 10d 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 — 484 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Plan Improvements Command
Perform a comprehensive analysis of the current codebase to identify improvement opportunities, then generate detailed recommendations and a phased implementation plan.
See also:
/create-planfor requirements-driven planning from BRD/PRD/TDD documents./ultra-planfor issue/bug lists requiring deep investigation.
Optional Arguments
| Argument | Alias | Description |
|---|---|---|
--recommendations-only |
--no-plan |
Stop after generating RECOMMENDATIONS.md — skip implementation plan generation. Enables a two-stage workflow where you review/edit recommendations before running /create-plan RECOMMENDATIONS.md to generate the plan. |
--max-phases <n> |
Maximum number of phases to generate (default: 8). If analysis yields more phases than this limit, group related items to stay within bounds or suggest splitting into multiple plan files. |
Argument detection: Check if the user's message includes --recommendations-only or --no-plan. If either is present, set the internal flag RECOMMENDATIONS_ONLY = true. Otherwise, RECOMMENDATIONS_ONLY = false. Check for --max-phases <n> and set MAX_PHASES accordingly (default: 8).
Input Validation
Before proceeding, verify:
- This is a code project with meaningful source files (not empty or purely documentation)
- The repository structure is readable and accessible
- There is sufficient existing code to analyze for improvements
For trivial projects or fresh repositories, ask the user if they want a full improvement plan or just architectural guidance.
Instructions
Phase 1: Deep Codebase Analysis
Thoroughly analyze the codebase with extended thinking enabled.
Root Cause and Interrelationship Analysis
Critical: Do not just catalog surface-level symptoms. For every issue identified during analysis, aggressively investigate to understand:
- Root cause — Why does this problem exist? Is it a design flaw, an oversight, a dependency issue, or accumulated drift? Understanding the root cause determines whether the fix is a config change or an architectural refactor.
- Impact and risk — What is the blast radius? Does this issue affect one file or cascade across modules? What happens if we fix it incorrectly?
- Interrelationships — After cataloging all findings, map the connections between them. Issues that share root causes or affect overlapping code paths must be addressed together. A recommendation that fixes problem A but exacerbates problem B is not a recommendation — it's a liability.
- Architectural fit — Every recommendation must produce changes that fit within the project's overall architecture and intent. Do not propose isolated patches that create technical debt or trigger a whack-a-mole fix cycle. Design integrated, cohesive solutions where a single well-designed change addresses multiple related concerns.
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.
- 10d ago First seen · 484 lines · 12 tokens per session scan A 30de0f853b1d
plan-improvements is a command published in the GitHub repository davistroy/claude-marketplace (5 stars, last pushed 3d ago), licensed MIT. It adds 12 tokens to every session and 7,891 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 2026-08-31.
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.