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 skills/smart-ai-memory/attune-ai/refactor-plannpx skills add Smart-AI-Memory/attune-ai --skill refactor-plangit clone --depth 1 https://github.com/Smart-AI-Memory/attune-aiWrote 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/smart-ai-memory/attune-ai/refactor-plan)<a href="https://agentmods.dev/skills/smart-ai-memory/attune-ai/refactor-plan"><img src="https://agentmods.dev/badge/skills/smart-ai-memory/attune-ai/refactor-plan.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 | $0.00041 | $0.00604 |
| Opus 5 | $0.00020 | $0.00302 |
| Sonnet 5 | $0.00008 | $0.00121 |
| Haiku 4.5 | $0.00004 | $0.00060 |
Grade A, and why
refactor-plan 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 6d 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 — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Refactor Planning
IMPORTANT: Start your response with a context preamble.
Call help_lookup(topic="refactor-plan", mode="preamble") and
display the returned preamble text as a blockquote. Then
tell the user they can say "tell me more" for a step-by-step
guide, or answer the scoping questions below to proceed.
If the MCP call fails, fall back to:
Refactor Plan — Analyzes code structure and produces a prioritized refactoring roadmap.
Scoping
Before running, ask:
- Target: "Which file or directory needs refactoring analysis?"
- Focus: "Full analysis or specific concern?"
- Full:
refactor_plan(all areas) - Simplify:
simplify_code(reduce complexity only)
- Full:
- Depth: "Quick scan or detailed roadmap?"
Execution
Based on scope:
- Full analysis:
refactor_plan(path="<target>") - Simplify only:
simplify_code(path="<target>")
Or via CLI:
attune workflow run refactor-plan --path <target>
MCP Tools
| Tool | What It Does |
|---|---|
refactor_plan |
Tech debt analysis and refactoring roadmap |
simplify_code |
Reduce complexity in specific files |
refactor_plan
Full refactoring analysis for a path.
refactor_plan(path="<target>")
simplify_code
Targeted complexity reduction for a single file or module. Flattens nested conditionals, inlines trivial helpers, removes dead code.
simplify_code(path="<target file>")
Analysis Areas
- Code Smells: Long methods, god classes, feature envy
- Duplication: Copy-paste detection, DRY violations
- Complexity: High cyclomatic complexity, deep nesting
- Coupling: Tight dependencies, circular imports
- Naming: Unclear or inconsistent naming
Output
Prefer the rich panel. If the tool response includes panel_html,
pass it to mcp__visualize__show_widget — the universal report panel
(title, score, findings/category sections; from
attune.workflows.report_panel). It shows an explicit "did not
complete" state on failure, never a false "clean". Fall back to the
markdown below when the widget surface is unavailable.
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.
- 6d ago First seen · 91 lines · 41 tokens per session scan A 793a28a0663e
refactor-plan is a skill published in the GitHub repository Smart-AI-Memory/attune-ai (10 stars, last pushed yesterday), licensed Apache-2.0. It adds 41 tokens to every session and 604 once invoked, about $0.0002 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.
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