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-excel-match-analyzergit 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-excel-match-analyzer)<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.
<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>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 | $0.00084 | $0.00863 |
| Opus 5 | $0.00042 | $0.00432 |
| Sonnet 5 | $0.00017 | $0.00173 |
| Haiku 4.5 | $0.00008 | $0.00086 |
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.
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 — 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)
- high_and_often: Highest number × count of that number
- summation: Sum of all 6 dice
- highs_and_lows: Highest × Lowest × (Highest - Lowest)
- only_two_numbers: If exactly 2 distinct numbers → 30 (else N/A)
- all_the_numbers: If rolls are {1,2,3,4,5,6} → 40 (else N/A)
- 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 scorerscore_summation(rolls)- Category 2 scorerscore_highs_and_lows(rolls)- Category 3 scorerscore_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 turncompute_game_score(t1_rolls, t2_rolls)- Best combined score for a gameload_excel_data(filepath, sheet_name)- Load dice data from Excelcompute_all_game_scores(data)- Score all gamessplit_by_parity(game_scores)- Split into P1 (odd) and P2 (even)compare_paired_matches(p1, p2)- Count wins for each playercompute_win_difference(p1_wins, p2_wins)- P1 wins minus P2 winswrite_answer(result, filepath)- Write numeric result to file
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.
- today First seen · 76 lines · 84 tokens per session scan A 269e75c7aca9
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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