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/causify-ai/helpers/coding.factor_common_codenpx skills add causify-ai/helpers --skill coding.factor_common_codegit clone --depth 1 https://github.com/causify-ai/helpersWhat 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 | $0.00013 | $0.00448 |
| Opus 5 | $0.00006 | $0.00224 |
| Sonnet 5 | $0.00003 | $0.00090 |
| Haiku 4.5 | $0.00001 | $0.00045 |
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.
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_runthen 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, ...
- Use the following format:
- Create a vim quickfile cfile for the locations using the convention in
.claude/skills/cfile.rules.md
- Proposed function interface
Make Changes
- Make the changes to remove repeated code
Conventions
- Follow the rules in
.claude/skills/coding.rules.md
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.
- 2d ago First seen · 60 lines · 13 tokens per session scan A 88b696e8c649
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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