Borrowing it
Nothing to install: this file belongs to Benkapner/claude-code-basecamp. 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/Benkapner/claude-code-basecamp/main/.claude/commands/refactor-safe.mdgit clone --depth 1 https://github.com/Benkapner/claude-code-basecampWrote 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/benkapner/claude-code-basecamp/refactor-safe)<a href="https://agentmods.dev/commands/benkapner/claude-code-basecamp/refactor-safe"><img src="https://agentmods.dev/badge/commands/benkapner/claude-code-basecamp/refactor-safe/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/benkapner/claude-code-basecamp/refactor-safe"><img src="https://agentmods.dev/badge/commands/benkapner/claude-code-basecamp/refactor-safe.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 | $0.00029 | $0.00708 |
| Opus 5.5 | $0.00012 | $0.00283 |
| Sonnet 5.5 | $0.00006 | $0.00142 |
| Haiku 4.5 | $0.00003 | $0.00071 |
Grade A, and why
refactor-safe 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 1mo 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 — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Safe Refactor
At this point, you've reviewed the code, tested it, and documented it. If it still has flagged structural problems (like a 65-line handler doing validation, inference, logging, and response formatting in one block), this is the command to fix it.
Refactoring with AI is risky because Claude tends to "improve" things you didn't ask it to touch. This command has an explicit constraint: the public API must not change. It focuses on internal structure only.
Instructions
1. Identify Scope
- Read the target file(s) specified in $ARGUMENTS
- Identify the public API: exported functions, class interfaces, function signatures, return types
- Document the public API before making any changes
2. Identify Refactoring Opportunities
Look for these internal problems:
- Long functions (50+ lines doing multiple things)
- Duplicated logic (same pattern in multiple places)
- Deep nesting (3+ levels of if/for/try)
- Poor naming (single-letter variables, misleading names)
- Mixed concerns (validation + business logic + formatting in one function)
- Dead code (unused imports, unreachable branches)
3. Apply Refactoring
For each change:
- Extract helper functions for distinct responsibilities
- Simplify nested conditionals (early returns, guard clauses)
- Rename variables/functions for clarity
- Remove dead code
- Consolidate duplicated logic
4. Verify
After refactoring:
- Confirm the public API is unchanged — same function signatures, same return types, same behavior
- Run tests if they exist (
uv run python -m pytest) - If no tests exist, warn the user
5. Present Changes
Show what was refactored:
Safe Refactor Summary
=====================
File: scripts/summarize_people.py
PUBLIC API: Unchanged
- main(argv) — same signature, same behavior
- compute_person_stats(issues) — same signature, same return type
INTERNAL CHANGES:
- Extracted _parse_date() from compute_person_stats (was inline, 8 lines)
- Extracted _count_collaborators() from compute_person_stats (was inline, 12 lines)
- Renamed 'ps' → 'person_summary' in main loop
- Removed unused import: 'json'
Tests: 70 passed, 0 failed
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.
- 1mo ago First seen · 80 lines · 29 tokens per session scan A b7794daee76e
refactor-safe is a command published in the GitHub repository Benkapner/claude-code-basecamp (18 stars, last pushed 27d ago), licensed MIT. It adds 29 tokens to every session and 708 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 2026-08-30.
Other commands, from other repositories
spec-forge
Use when generating software specifications — full chain ([Idea]→[Decompose]→[PRD]→SRS→Tech Design→Review) or individual documents.
propagate
Use after editing an upstream doc (PRD/SRS/tech-design/feature-spec) to propagate changes downstream and keep the entire doc chain consistent.
analyze
Use when analyzing a document collection to map themes, find conflicts, gaps, and redundancies — generates landscape analysis report.
review
Use when reviewing spec-forge generated documents for quality, completeness, and consistency — auto-fixes issues if found.
audit
Use when auditing existing project docs for quality, completeness, and code alignment — generates findings report with fix recommendations.
srs
Use when writing the requirements a team will be held to — an SRS, requirements spec, or a requirements document for an implementation team or vendor (IEEE 830, with traceability and acceptance criteria).