coding.factor_common_code

A Python code-review guide for finding repeated or nearly repeated code and suggesting shared functions. Shared functions are reusable pieces of code that avoid keeping the same logic in several places.

In plain words
What is it for?
Reviewing multiple Python files, identifying safe refactoring opportunities, and explaining why similar code can be combined.
Why use it?
It helps reduce duplication while keeping the program’s behavior unchanged, making future updates easier to manage.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/causify-ai/helpers/coding.factor_common_code
Any agent
npx skills add causify-ai/helpers --skill coding.factor_common_code
Clone the repo
git clone --depth 1 https://github.com/causify-ai/helpers

Made for: Claude Code, Codex.

Per session 13 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 448 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00013 $0.00448
Opus 5 $0.00006 $0.00224
Sonnet 5 $0.00003 $0.00090
Haiku 4.5 $0.00001 $0.00045

Measured 2d ago against content hash 88b696e8c649, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

coding.factor_common_code 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 2d 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.

.claude/skills/coding.factor_common_code/SKILL.md · 60 lines

What it actually says

Role

  • You are a senior Python engineer with strong experience in refactoring and codebase hygiene

Goal

  • I will provide references to one or more Python source files

  • Your task is to:

    • Read and analyze the code across these files
    • Identify meaningful duplicated or near-duplicated code blocks that can be safely refactored into shared functions
    • Report these changes
    • Ask the user
  • You must not change the behavior of the code

Objectives

  • Detect common logic that appears in multiple places (exact or structurally similar)
  • Propose reusable functions that improve maintainability and readability

Guidelines

  • Do not suggest functions that are trivial (e.g., fewer than 2–3 meaningful lines)
  • Avoid trivial abstractions and prefer extracting logic that:
    • Is likely to change in one place in the future
    • Encapsulates a clear responsibility
  • If similar blocks are not identical, explain briefly why they can still be unified
  • Do not rewrite the full implementation unless explicitly asked, but only focus on identifying and describing refactor opportunities

Output Format

  • If the user uses the option --dry_run then report the output as below instead of executing the refactorings

  • For each proposed refactoring, produce:

    • Proposed function interface
      • Function name
      • Parameters (with brief explanation if non-obvious)
      • Return value (if any)
    • Summary of the locations of duplicated code
      • Use the following format:
        file1.py: l1–l2, l3–l4, ...
        File2.py: l5–l8, ...
        
    • Create a vim quickfile cfile for the locations using the convention in .claude/skills/cfile.rules.md

Make Changes

  • Make the changes to remove repeated code

Conventions

  • Follow the rules in .claude/skills/coding.rules.md
Changes

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.

  1. 2d ago First seen · 60 lines · 13 tokens per session scan A 88b696e8c649

Subscribe to this mod's changes

coding.factor_common_code is a skill published in the GitHub repository causify-ai/helpers (145 stars, last pushed 2d ago), licensed Apache-2.0. It adds 13 tokens to every session and 448 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 2026-08-30.

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