Generate Cosine Similarity Matrix With Id Column Naming

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha 04e912cff54b · 14 Oct 2026Report an issue or request a re-scan

• PublisherECNU-ICALK GitHub organizationOwner of the repository this skill is read from
• Source folderECNU-ICALK/AutoSkill/SkillBank/ConvSkill/english_gpt3.5_8_GLM4.7/generate-cosine-similarity-matrix-with-id-column-namingThe exact folder we audited
• LicenceNot declaredAs the repository states it
• Commit pinned for this verdict04e912cff54bA new audit runs when the source changes
• Repository statusNot archivedLast push 157 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: Calculates pairwise cosine similarity for a DataFrame column, formats the result matrix with columns named 'compared_to_{id}', and merges it back to the original DataFrame.

---
id: "31e75fd2-0c04-4df9-bb92-a043bcd8921a"
name: "Generate Cosine Similarity Matrix with ID Column Naming"
description: "Calculates pairwise cosine similarity for a DataFrame column, formats the result matrix with columns named 'compared_to_{id}', and merges it back to the original DataFrame."
version: "0.1.0"
tags:
  - "python"
  - "pandas"
  - "cosine-similarity"
  - "nlp"
  - "dataframe-merge"
triggers:
  - "calculate cosine similarity for dataframe"
  - "create similarity matrix with inquiry ids"
  - "merge cosine similarity results with original df"
  - "format similarity columns with compared_to prefix"
---

# Generate Cosine Similarity Matrix with ID Column Naming

Calculates pairwise cosine similarity for a DataFrame column, formats the result matrix with columns named 'compared_to_{id}', and merges it back to the original DataFrame.

## Prompt

# Role & Objective
You are a Python data engineer. Your task is to generate a pairwise cosine similarity matrix from a specific column in a pandas DataFrame, format the output columns using IDs from the DataFrame, and merge the results back to the original data.

# Operational Rules & Constraints
1. **Input Data**: Work with a pandas DataFrame `df` containing an `inquiry_id` column and a text column specified by the variable `column_to_use`.
2. **Embedding Generation**: Use the `encoder.encode()` method on the list of values from `df[column_to_use]`. Ensure the column is accessed dynamically using the `column_to_use` variable (e.g., `df[column_to_use].tolist()`).
3. **Similarity Calculation**: Calculate the cosine similarity matrix using `cosine_similarity(embedding, embedding)`.
4. **DataFrame Construction**: Create a result DataFrame (`result_df`) where the columns represent the similarity scores.
5. **Column Naming**: Name the columns in `result_df` by combining the prefix 'compared_to_' with the corresponding values from the `inquiry_id` column in `df`.
6. **Merging**: Merge the original `df` and `result_df` on their indices using `pd.merge(df, result_df, left_index=True, right_index=True)`.

# Anti-Patterns
- Do not hardcode the column name for encoding; use the `column_to_use` variable.
- Do not use default integer indices for column names; use the `inquiry_id` values with the specified prefix.

## Triggers

- calculate cosine similarity for dataframe
- create similarity matrix with inquiry ids
- merge cosine similarity results with original df
- format similarity columns with compared_to prefix

Excerpt, first 2,498 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

git clone https://github.com/ECNU-ICALK/AutoSkill
git -C AutoSkill checkout 04e912cff54b

Works in

1. 2026-10-14
   ✓SAFE B 89 / 100

   first audit · source 04e912cff54b

Questions about Generate Cosine Similarity Matrix With Id Column Naming

• github.com/ECNU-ICALK/AutoSkill/tree/main/SkillBank/ConvSkill/en — the audited source at commit 04e912cff54b and every finding aboveObserved · 14 Oct 2026

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