evo-excel-match-analyzer

evo-excel-match-analyzer is a skill for Claude Code, Codex from OpenLAIR/OpenSkill. It costs 84 tokens per session (863 once invoked), scanned A, original, Apache-2.0.

An Excel data analyzer for dice games. It calculates scores under six stated rules and compares paired games between two players.

In plain words
What is it for?
Use it to load dice rolls from an Excel file, score each game, compare odd and even games, and write the win difference to a file.
Why use it?
It removes repetitive score calculations and match comparisons from spreadsheet data. It also calculates the difference in wins.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to load dice rolls from an Excel file, score each game, compare odd and even games, and write the win difference to a file.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/openlair/openskill/evo-excel-match-analyzer
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 OpenLAIR/OpenSkill --skill evo-excel-match-analyzer
Clone the repo
git clone --depth 1 https://github.com/OpenLAIR/OpenSkill

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for evo-excel-match-analyzer

README.md
[![agentmods](https://agentmods.dev/badge/skills/openlair/openskill/evo-excel-match-analyzer/github.svg)](https://agentmods.dev/skills/openlair/openskill/evo-excel-match-analyzer)
Your own site
<a href="https://agentmods.dev/skills/openlair/openskill/evo-excel-match-analyzer"><img src="https://agentmods.dev/badge/skills/openlair/openskill/evo-excel-match-analyzer/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.

agentmods 80×15 button for evo-excel-match-analyzer

Your own site · 80×15
<a href="https://agentmods.dev/skills/openlair/openskill/evo-excel-match-analyzer"><img src="https://agentmods.dev/badge/skills/openlair/openskill/evo-excel-match-analyzer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 84 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 863 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 $0.00084 $0.00863
Opus 5 $0.00042 $0.00432
Sonnet 5 $0.00017 $0.00173
Haiku 4.5 $0.00008 $0.00086

Measured today against content hash 269e75c7aca9, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

evo-excel-match-analyzer 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 today.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/utils.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

tasks-evolved/financial-modeling-qa/environment/skills/evo-excel-match-analyzer/SKILL.md · 76 lines

How it starts

The opening of the file, as written. The whole thing — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Excel Match Analyzer

Analyzes dice game data from Excel files. Computes game scores using 6 scoring categories, then pairs odd-numbered games (Player 1) vs even-numbered games (Player 2) for match comparison.

Usage

import sys
sys.path.insert(0, '/app/environment/skills/evo-excel-match-analyzer/scripts')
from utils import (
    load_excel_data, compute_all_game_scores, split_by_parity,
    compare_paired_matches, compute_win_difference, write_answer
)

# Load data
data = load_excel_data('/root/data.xlsx', sheet_name='Data')

# Compute all game scores
game_scores = compute_all_game_scores(data)

# Split by parity (odd=P1, even=P2)
p1_scores, p2_scores = split_by_parity(game_scores)

# Compare paired matches (game 1 vs 2, game 3 vs 4, etc.)
p1_wins, p2_wins, ties = compare_paired_matches(p1_scores, p2_scores)

# Compute and write answer
diff = compute_win_difference(p1_wins, p2_wins)
write_answer(diff, '/root/answer.txt')

Scoring Rules (6 Categories)

  1. high_and_often: Highest number × count of that number
  2. summation: Sum of all 6 dice
  3. highs_and_lows: Highest × Lowest × (Highest - Lowest)
  4. only_two_numbers: If exactly 2 distinct numbers → 30 (else N/A)
  5. all_the_numbers: If rolls are {1,2,3,4,5,6} → 40 (else N/A)
  6. ordered_subset_of_four: If rolls contain run of 4 consecutive inc/dec → 50 (else N/A)

Game Scoring

Each game has 2 turns. Find highest combined score using 2 DIFFERENT categories (one per turn). Try all valid category pairs and pick the maximum.

Match Pairing

  • Player 1 plays odd-numbered games (1, 3, 5, ...)
  • Player 2 plays even-numbered games (2, 4, 6, ...)
  • Matches: game 1 vs game 2, game 3 vs game 4, etc.
  • Higher game score wins the match

Key Functions

  • score_high_and_often(rolls) - Category 1 scorer
  • score_summation(rolls) - Category 2 scorer
  • score_highs_and_lows(rolls) - Category 3 scorer
  • score_only_two_numbers(rolls) - Category 4 scorer (returns None if N/A)
  • score_all_the_numbers(rolls) - Category 5 scorer (returns None if N/A)
  • score_ordered_subset_of_four(rolls) - Category 6 scorer (returns None if N/A)
  • compute_turn_scores(rolls) - All applicable scores for a turn
  • compute_game_score(t1_rolls, t2_rolls) - Best combined score for a game
  • load_excel_data(filepath, sheet_name) - Load dice data from Excel
  • compute_all_game_scores(data) - Score all games
  • split_by_parity(game_scores) - Split into P1 (odd) and P2 (even)
  • compare_paired_matches(p1, p2) - Count wins for each player
  • compute_win_difference(p1_wins, p2_wins) - P1 wins minus P2 wins
  • write_answer(result, filepath) - Write numeric result to file

Read the full file on GitHub · 76 lines

Files

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.

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. today First seen · 76 lines · 84 tokens per session scan A 269e75c7aca9

Subscribe to this mod's changes

evo-excel-match-analyzer is a skill published in the GitHub repository OpenLAIR/OpenSkill (88 stars, last pushed yesterday), licensed Apache-2.0. It adds 84 tokens to every session and 863 once invoked, about $0.0004 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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