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 OpenLAIR/OpenSkill --skill evo-dataframe-diff-reportgit clone --depth 1 https://github.com/OpenLAIR/OpenSkillWrote 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/openlair/openskill/evo-dataframe-diff-report)<a href="https://agentmods.dev/skills/openlair/openskill/evo-dataframe-diff-report"><img src="https://agentmods.dev/badge/skills/openlair/openskill/evo-dataframe-diff-report/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/openlair/openskill/evo-dataframe-diff-report"><img src="https://agentmods.dev/badge/skills/openlair/openskill/evo-dataframe-diff-report.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00046 | $0.00462 |
| Opus 5 | $0.00023 | $0.00231 |
| Sonnet 5 | $0.00009 | $0.00092 |
| Haiku 4.5 | $0.00005 | $0.00046 |
Grade A, and why
evo-dataframe-diff-report 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 yesterday.
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
evo-dataframe-diff-report
Compares old vs new DataFrames to find deletions and modifications, exports as JSON.
Key Functions
find_deleted_employees(df_old, df_new, id_column='ID')
Finds IDs in old but not in new. Returns sorted list of ID strings.
find_modified_employees(df_old, df_new, id_column='ID', numeric_columns=None, float_tolerance=1e-4)
Finds field-level changes for employees in both DataFrames. Returns list of dicts: {"id": "EMP00003", "field": "Salary", "old_value": 50020, "new_value": 55010} Sorted by (id, field).
build_diff_report(deleted, modified)
Builds the final report dict with 'deleted_employees' and 'modified_employees'.
export_diff_to_json(report, output_path, indent=2)
Writes report to JSON file using NumpyTypeEncoder for safe serialization.
Value Type Rules
- Salary, Years: output as integers
- Score: output as float (e.g., 4.3)
- Text fields (First, Last, Dept, Position, Location): output as strings
- NaN values: output as null
Usage
import sys
sys.path.insert(0, '/app/environment/skills/evo-dataframe-diff-report/scripts')
from utils import find_deleted_employees, find_modified_employees, build_diff_report, export_diff_to_json
deleted = find_deleted_employees(df_old, df_new, id_column='ID')
modified = find_modified_employees(df_old, df_new, id_column='ID',
numeric_columns=['Salary', 'Years', 'Score'])
report = build_diff_report(deleted, modified)
export_diff_to_json(report, '/root/diff_report.json')
Dependencies
- Requires normalized DataFrames from evo-pdf-excel-extraction
- Both DataFrames must share the same column names and ID format
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- yesterday First seen · 49 lines · 46 tokens per session scan A 461a43620e0c
evo-dataframe-diff-report is a skill published in the GitHub repository OpenLAIR/OpenSkill (90 stars, last pushed 2d ago), licensed Apache-2.0. It adds 46 tokens to every session and 462 once invoked, about $0.0002 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 2026-09-11.
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