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 skills add causify-ai/helpers --skill coding.factor_common_codegit clone --depth 1 https://github.com/causify-ai/helpersWrote 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/causify-ai/helpers/coding.factor_common_code)<a href="https://agentmods.dev/skills/causify-ai/helpers/coding.factor_common_code"><img src="https://agentmods.dev/badge/skills/causify-ai/helpers/coding.factor_common_code/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/skills/causify-ai/helpers/coding.factor_common_code"><img src="https://agentmods.dev/badge/skills/causify-ai/helpers/coding.factor_common_code.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 3 Skill selects an external model or provider that may use a different account or billing plan than the operator expects. Undisclosed model switches can cause unexpected cost or quota consumption.Fix: Remove the model/provider override or disclose it prominently and require explicit operator approval before invoking an external coding CLI or billed model.
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 | $0.00498 |
| Opus 5.5 | $0.00005 | $0.00199 |
| Sonnet 5.5 | $0.00002 | $0.00100 |
| Haiku 4.5 | $0.00001 | $0.00050 |
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 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 — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
Workflow
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 quickfix 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
Verification
- Confirm proposed functions are non-trivial and each encapsulates a clear responsibility
- Confirm the code behavior is unchanged after refactoring
- Confirm the cfile locations point to the correct duplicated blocks
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 Changed · +7 lines · -1 tokens per session f57c884b9fb2
- 1mo 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 (150 stars, last pushed yesterday), licensed Apache-2.0. It adds 12 tokens to every session and 498 once invoked, about $0.0000 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.
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