testing.fix_mock_tests

testing.fix_mock_tests is a skill for Claude Code from causify-ai/helpers. It costs 6 tokens per session (919 once invoked), scanned A, original, Apache-2.0.

A refactoring guide for replacing mocks in Python unit tests with pure functions or injected dependencies. Mocking replaces real dependencies with test doubles; dependency injection passes those dependencies into the code instead.

In plain words
What is it for?
Use it to rewrite unit tests that rely on mocks and adjust the tested code when needed to accept dependencies or use pure functions.
Why use it?
It helps tests check real behavior without intercepting calls, while encouraging code whose dependencies are easier to control. This can reveal designs that are difficult to test directly.

Skill for Claude Code

Written for Claude Code: installed under .claude/. Also seen: model in frontmatter; reads .claude/ paths.

Good fit Use it to rewrite unit tests that rely on mocks and adjust the tested code when needed to accept dependencies or use pure functions.

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Install with agentmods
npx agentmods add skills/causify-ai/helpers/testing.fix_mock_tests
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.

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

Made for: Claude Code.

Wrote this? Show the measurements

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README.md
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Per session 6 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 919 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 3 findings, 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.
  • medium Data Exfiltration · line 32
    Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.
    Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
  • medium Data Exfiltration · line 42
    Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.
    Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
How audits are shown
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 5.1 $0.00006 $0.00919
Opus 5.5 $0.00002 $0.00368
Sonnet 5.5 $0.00001 $0.00184
Haiku 4.5 $0.00001 $0.00092

Measured 1mo ago against content hash 9c095897d3db, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

testing.fix_mock_tests scanned grade A with 1 finding 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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

return requests.get(f"https://api.com/users/{user_id}").json()
.claude/skills/testing.fix_mock_tests/SKILL.md · 125 lines

How it starts

The opening of the file, as written. The whole thing — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Goal

  • When the user passes a test file, apply the following transformations to remove mocking from unit testing code without changing behavior
  • Mocking often signals that your design could be more modular
    • Avoiding mocking in Python unit tests means designing code so dependencies are easy to control naturally, rather than being intercepted artificially

Workflow

  • Avoid mocking using the strategies below

Prefer Pure Functions

  • Functions that depend only on inputs and return outputs are trivial to test
    • Example:
      def add(a: int, b: int) -> int:
          return a + b
      
  • No mocking needed because there are no external dependencies

Design for Dependency Injection (DI)

  • Instead of hard coding dependencies inside functions or classes, pass them in
  • Bad (hardcodes the dependency, forcing a mock to test it)
    from typing import Any
    import requests
    
    def get_user(user_id: int) -> Any:
      return requests.get(f"https://api.com/users/{user_id}").json()
    
  • Good (injects the dependency, so a fake can be substituted in tests)
    from typing import Any, Protocol
    
    class HttpClient(Protocol):
        def get(self, url: str) -> Any: ...
    
    def get_user(user_id: int, http_client: HttpClient) -> Any:
      return http_client.get(f"https://api.com/users/{user_id}").json()
    
  • Bad (creates its own dependency inside __init__)
    class Database: pass
    
    class UserService:
      def __init__(self) -> None:
        self.db: Database = Database()
    
  • Good (accepts the dependency, so a fake can be passed in)
    class Database: pass
    
    class UserService:
      def __init__(self, db: Database) -> None:
        self.db: Database = db
    

Create Lightweight Fakes Instead of Mocks

  • A fake is a real implementation with simplified behavior
    • More maintainable and less brittle than mocks
    • Example:
      from typing import Any
      
      class FakeDatabase:
          def __init__(self) -> None:
              self.data: dict[str, Any] = {}
      
          def save(self, key: str, value: Any) -> None:
              self.data[key] = value
      
          def get(self, key: str) -> Any:
              return self.data.get(key)
      

Read the full file on GitHub · 125 lines

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. 1mo ago Changed · +6 lines 9c095897d3db
  2. 1mo ago First seen · 119 lines · 6 tokens per session scan A 070e7518068b

Subscribe to this mod's changes

testing.fix_mock_tests is a skill published in the GitHub repository causify-ai/helpers (150 stars, last pushed yesterday), licensed Apache-2.0. It adds 6 tokens to every session and 919 once invoked, about $0.0000 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.