Agent skill

Powerlifting

by Raidriar7170 in Raidriar7170/hermes-skilleval

Calculating powerlifting scores to determine the performance of lifters across different weight classes.

MITAuto-check passedData & Analytics

Install Powerlifting

skills CLI
$ npx skills add Raidriar7170/hermes-skilleval --skill powerlifting -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Raidriar7170/hermes-skilleval powerlifting --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Raidriar7170/hermes-skilleval.git skills-src && mkdir -p .claude/skills && cp -r skills-src/artifacts/v0.3/skillsbench-pilot/v0.3-stage2-input-package-candidate-20260701T010000Z/candidate-data/skill-sources/skillsbench__powerlifting .claude/skills/powerlifting && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
powerlifting
GitHub stars
125
Used in
1 other repo
Token cost
~3.7k tokens
SKILL.md length
613 words
Files
1
Skills in repo
8
Repo updated
First seen
Licence
MIT

At a glance

Calculating powerlifting scores to determine the performance of lifters across different weight classes.

  • Data & Analytics work in your project
  • SKILL.md covers Dynamic Objective Team Scoring…, IPF Good Lift Coefficient, Wilks coefficient and Glossbrenner
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Powerlifting is an agent skill from Raidriar7170/hermes-skilleval. Calculating powerlifting scores to determine the performance of lifters across different weight classes.

Its SKILL.md is about 3.7k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Data & Analytics. The repository describes itself as: Verification-gated skill routing and self-improvement harness for Hermes-style agent skills. The licence is MIT.

When your agent uses it

  • Data & Analytics work in your project

Example prompts

  • “/powerlifting”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit 8f6a21e. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are rust).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • powerliftpro.app
    • powerlifting.sport
    • en.wikipedia.org
    • worldpowerliftingcongress.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Powerlifting loads about 3.7k tokens when it runs. Until then it costs about 29 tokens; SKILL.md has 613 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~29
When it runs · the whole SKILL.md, loaded when a task matches
~3.7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from Raidriar7170/hermes-skilleval at commit 8f6a21e, republished under its MIT licence (© Raidriar7170). 613 words, ~3,715 tokens.

Download SKILL.mdSave it as .claude/skills/powerlifting/SKILL.md (or your agent's skills folder).
name
powerlifting
description
Calculating powerlifting scores to determine the performance of lifters across different weight classes.

Calculating Powerlifting Scores as a Professional Coach

Dynamic Objective Team Scoring (Dots)

In the world of powerlifting, comparing lifters across different body weights is essential to /determine relative strength and fairness in competition. This is where the DOTS score—short for “Dynamic Objective Team Scoring”—comes into play. It’s a widely-used formula that helps level the playing field by standardizing performances regardless of a lifter’s body weight. Whether you’re new to powerlifting or a seasoned competitor, understanding the DOTS score is crucial for evaluating progress and competing effectively. This section is from powerliftpro.

What is the DOTS Score?

The DOTS score is a mathematical formula used to normalize powerlifting totals based on a lifter’s body weight. It provides a single number that represents a lifter’s relative strength, allowing for fair comparisons across all weight classes.

The score takes into account:

  • The lifter's total: The combined weight lifted in the squat, bench press, and deadlift.
  • Body weight: The lifter’s weight on competition day.
The DOTS Formula

For real powerlifting nerds who want to calculate their DOTS longhand (remember to show all work! sorry, old math class joke), the DOTS formula is as follows:

DOTS Score=Total Weight Lifted (kg)×a+b(BW)+c(BW2)+d(BW3)+e(BW4)500

Here:

  • Total Weight Lifted (kg): Your combined total from the squat, bench press, and deadlift.
  • BW: Your body weight in kilograms.
  • a, b, c, d, e: Coefficients derived from statistical modeling to ensure accurate scaling across various body weights.

These coefficients are carefully designed to balance the advantage heavier lifters might have in absolute strength and the lighter lifters’ advantage in relative strength.

rust
use crate::poly4;
use opltypes::*;

pub fn dots_coefficient_men(bodyweightkg: f64) -> f64 {
    const A: f64 = -0.0000010930;
    const B: f64 = 0.0007391293;
    const C: f64 = -0.1918759221;
    const D: f64 = 24.0900756;
    const E: f64 = -307.75076;

    // Bodyweight bounds are defined; bodyweights out of range match the boundaries.
    let adjusted = bodyweightkg.clamp(40.0, 210.0);
    500.0 / poly4(A, B, C, D, E, adjusted)
}

pub fn dots_coefficient_women(bodyweightkg: f64) -> f64 {
    const A: f64 = -0.0000010706;
    const B: f64 = 0.0005158568;
    const C: f64 = -0.1126655495;
    const D: f64 = 13.6175032;
    const E: f64 = -57.96288;

    // Bodyweight bounds are defined; bodyweights out of range match the boundaries.
    let adjusted = bodyweightkg.clamp(40.0, 150.0);
    500.0 / poly4(A, B, C, D, E, adjusted)
}

/// Calculates Dots points.
///
/// Dots were introduced by the German IPF Affiliate BVDK after the IPF switched to
/// IPF Points, which do not allow comparing between sexes. The BVDK hosts team
/// competitions that allow lifters of all sexes to compete on a singular team.
///
/// Since Wilks points have been ostracized from the IPF, and IPF Points are
/// unsuitable, German lifters therefore came up with their own formula.
///
/// The author of the Dots formula is Tim Konertz <tim.konertz@outlook.com>.
///
/// Tim says that Dots is an acronym for "Dynamic Objective Team Scoring,"
/// but that they chose the acronym before figuring out the expansion.
pub fn dots(sex: Sex, bodyweight: WeightKg, total: WeightKg) -> Points {
    if bodyweight.is_zero() || total.is_zero() {
        return Points::from_i32(0);
    }
    let coefficient: f64 = match sex {
        Sex::M | Sex::Mx => dots_coefficient_men(f64::from(bodyweight)),
        Sex::F => dots_coefficient_women(f64::from(bodyweight)),
    };
    Points::from(coefficient * f64::from(total))
}

IPF Good Lift Coefficient

The IPF Good Lift Coefficient calculator computes the comparative weight coefficient between weight lifters based on the weight of the lifter (x), the type of lift and the gender of the lifter, all combined in the IPF GL Coefficient formula. Information about this is from IPF.

INSTRUCTIONS: Choose units and enter the following:

  • (x) Weigh of Person
  • (g) Gender of Person
  • (LT) lift type
    • Equipped Power Lift
    • Classic Power Lift
    • Equipped Bench Press
    • Classic Bench Press

IPFL GL Coefficient (IPC): The calculator returns the coefficient as a real number (decimal).

The Math / Science

The International Powerlifting Federation GL coefficient formula is:

IPC=100A−B⋅e−C⋅BWT

where:

  • IPC = IPF GL coefficient
  • BWT = body weight of the lifter
  • A, B, C = f(gender, lift type), see below
rust
use opltypes::*;

/// Hardcoded formula parameters: `(A, B, C)`.
type Parameters = (f64, f64, f64);

/// Gets formula parameters from what is effectively a lookup table.
fn parameters(sex: Sex, equipment: Equipment, event: Event) -> Parameters {
    // Since the formula was made for the IPF, it only covers Raw and Single-ply.
    // We do our best and just reuse those for Wraps and Multi-ply, respectively.
    let equipment = match equipment {
        Equipment::Raw | Equipment::Wraps | Equipment::Straps => Equipment::Raw,
        Equipment::Single | Equipment::Multi | Equipment::Unlimited => Equipment::Single,
    };

    // Points are only specified for Sex::M and Sex::F.
    let dichotomous_sex = match sex {
        Sex::M | Sex::Mx => Sex::M,
        Sex::F => Sex::F,
    };

    const SBD: Event = Event::sbd();
    const B: Event = Event::b();

    match (event, dichotomous_sex, equipment) {
        (SBD, Sex::M, Equipment::Raw) => (1199.72839, 1025.18162, 0.009210),
        (SBD, Sex::M, Equipment::Single) => (1236.25115, 1449.21864, 0.01644),
        (SBD, Sex::F, Equipment::Raw) => (610.32796, 1045.59282, 0.03048),
        (SBD, Sex::F, Equipment::Single) => (758.63878, 949.31382, 0.02435),

        (B, Sex::M, Equipment::Raw) => (320.98041, 281.40258, 0.01008),
        (B, Sex::M, Equipment::Single) => (381.22073, 733.79378, 0.02398),
        (B, Sex::F, Equipment::Raw) => (142.40398, 442.52671, 0.04724),
        (B, Sex::F, Equipment::Single) => (221.82209, 357.00377, 0.02937),

        _ => (0.0, 0.0, 0.0),
    }
}

/// Calculates IPF GOODLIFT Points.
pub fn goodlift(
    sex: Sex,
    equipment: Equipment,
    event: Event,
    bodyweight: WeightKg,
    total: WeightKg,
) -> Points {
    // Look up parameters.
    let (a, b, c) = parameters(sex, equipment, event);

    // Exit early for undefined cases.
    if a == 0.0 || bodyweight < WeightKg::from_i32(35) || total.is_zero() {
        return Points::from_i32(0);
    }

    // A - B * e^(-C * Bwt).
    let e_pow = (-c * f64::from(bodyweight)).exp();
    let denominator = a - (b * e_pow);

    // Prevent division by zero.
    if denominator == 0.0 {
        return Points::from_i32(0);
    }

    // Calculate GOODLIFT points.
    // We add the requirement that the value be non-negative.
    let points: f64 = f64::from(total) * (0.0_f64).max(100.0 / denominator);
    Points::from(points)
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn published_examples() {
        // Dmitry Inzarkin from 2019 IPF World Open Men's Championships.
        let weight = WeightKg::from_f32(92.04);
        let total = WeightKg::from_f32(1035.0);
        assert_eq!(
            goodlift(Sex::M, Equipment::Single, Event::sbd(), weight, total),
            Points::from(112.85)
        );

        // Susanna Torronen from 2019 World Open Classic Bench Press Championships.
        let weight = WeightKg::from_f32(70.50);
        let total = WeightKg::from_f32(122.5);
        assert_eq!(
            goodlift(Sex::F, Equipment::Raw, Event::b(), weight, total),
            Points::from(96.78)
        );
    }
}
Show full SKILL.md (230 more words)Show less

Wilks coefficient

The following equation is used to calculate the Wilks coefficient: $$ \text{Coef} = \frac{500}{a + bx + cx^2 + dx^3 + ex^4 + fx^5} $$ where $x$ is the body weightof the lifter in kilograms.

The total weight lifted (in kg) is multiplied by the coefficient to find the standard amount lifted, normalised across all body weights.

MenWomen
a-216.0475144594.31747775582
b16.2606339−27.23842536447
c-0.0023886450.82112226871
d-0.00113732−0.00930733913
e7.01863 × 10−64.731582 × 10−5
f−1.291 × 10−8−9.054 × 10−8
rust
use crate::poly5;
use opltypes::*;

pub fn wilks_coefficient_men(bodyweightkg: f64) -> f64 {
    // Wilks defines its polynomial backwards:
    // A + Bx + Cx^2 + ...
    const A: f64 = -216.0475144;
    const B: f64 = 16.2606339;
    const C: f64 = -0.002388645;
    const D: f64 = -0.00113732;
    const E: f64 = 7.01863E-06;
    const F: f64 = -1.291E-08;

    // Upper bound avoids asymptote.
    // Lower bound avoids children with huge coefficients.
    let adjusted = bodyweightkg.clamp(40.0, 201.9);

    500.0 / poly5(F, E, D, C, B, A, adjusted)
}

pub fn wilks_coefficient_women(bodyweightkg: f64) -> f64 {
    const A: f64 = 594.31747775582;
    const B: f64 = -27.23842536447;
    const C: f64 = 0.82112226871;
    const D: f64 = -0.00930733913;
    const E: f64 = 0.00004731582;
    const F: f64 = -0.00000009054;

    // Upper bound avoids asymptote.
    // Lower bound avoids children with huge coefficients.
    let adjusted = bodyweightkg.clamp(26.51, 154.53);

    500.0 / poly5(F, E, D, C, B, A, adjusted)
}

/// Calculates Wilks points.
pub fn wilks(sex: Sex, bodyweight: WeightKg, total: WeightKg) -> Points {
    if bodyweight.is_zero() || total.is_zero() {
        return Points::from_i32(0);
    }
    let coefficient: f64 = match sex {
        Sex::M | Sex::Mx => wilks_coefficient_men(f64::from(bodyweight)),
        Sex::F => wilks_coefficient_women(f64::from(bodyweight)),
    };
    Points::from(coefficient * f64::from(total))
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn coefficients() {
        // Coefficients taken verbatim from the old Python implementation.
        assert_eq!(wilks_coefficient_men(100.0), 0.6085890719066511);
        assert_eq!(wilks_coefficient_women(100.0), 0.8325833167368228);
    }

    #[test]
    fn points() {
        // Point values taken (rounded) from the old Python implementation.
        assert_eq!(
            wilks(Sex::M, WeightKg::from_i32(100), WeightKg::from_i32(1000)),
            Points::from(608.58907)
        );
        assert_eq!(
            wilks(Sex::F, WeightKg::from_i32(60), WeightKg::from_i32(500)),
            Points::from(557.4434)
        );
    }
}

Glossbrenner

When Powerlifting began having 'masters', it quickly became apparent that some sort of age allowance or 'handicap' was needed by this group to sort them out, one from another. How much better must a 40-yr-old be, than a 50-yr-old, to be considered a better lifter? How much better than a 55-yr-old, a 60-yr-old, etc? So, a set of age multipliers, or 'handicap numbers', was issued for Master Lifters year-by-year, from 40 through 80. Since then, however, the number of Master Lifters in the 50+ and 60+ group has become so large that a revision (based on extensive study and years of backup data) was needed. These new numbers are based on comparison of the totals of actual lifters over the past seven years, and a chart for their usage is on the final page of this section.

The formula is: (LT) times (GBC) = (PN)

LT - Lifter's Total GBC – Glossbrenner Bodyweight Coefficient * PN - Product Number

rust
use opltypes::*;

use crate::schwartzmalone::{malone_coefficient, schwartz_coefficient};
use crate::wilks::{wilks_coefficient_men, wilks_coefficient_women};

fn glossbrenner_coefficient_men(bodyweightkg: f64) -> f64 {
    // Glossbrenner is defined piecewise.
    if bodyweightkg < 153.05 {
        (schwartz_coefficient(bodyweightkg) + wilks_coefficient_men(bodyweightkg)) / 2.0
    } else {
        // Linear coefficients found by fitting to a table.
        const A: f64 = -0.000821668402557;
        const B: f64 = 0.676940740094416;
        (schwartz_coefficient(bodyweightkg) + A * bodyweightkg + B) / 2.0
    }
}

fn glossbrenner_coefficient_women(bodyweightkg: f64) -> f64 {
    // Glossbrenner is defined piecewise.
    if bodyweightkg < 106.3 {
        (malone_coefficient(bodyweightkg) + wilks_coefficient_women(bodyweightkg)) / 2.0
    } else {
        // Linear coefficients found by fitting to a table.
        const A: f64 = -0.000313738002024;
        const B: f64 = 0.852664892884785;
        (malone_coefficient(bodyweightkg) + A * bodyweightkg + B) / 2.0
    }
}

/// Calculates Glossbrenner points.
///
/// Glossbrenner is the average of two older systems, Schwartz-Malone and Wilks,
/// with a piecewise linear section.
///
/// This points system is most often used by GPC affiliates.
pub fn glossbrenner(sex: Sex, bodyweight: WeightKg, total: WeightKg) -> Points {
    if bodyweight.is_zero() || total.is_zero() {
        return Points::from_i32(0);
    }
    let coefficient: f64 = match sex {
        Sex::M | Sex::Mx => glossbrenner_coefficient_men(f64::from(bodyweight)),
        Sex::F => glossbrenner_coefficient_women(f64::from(bodyweight)),
    };
    Points::from(coefficient * f64::from(total))
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn coefficients() {
        // Coefficients taken verbatim from the old Python implementation.
        assert_eq!(glossbrenner_coefficient_men(100.0), 0.5812707859533183);
        assert_eq!(glossbrenner_coefficient_women(100.0), 0.7152488066040259);
    }

    #[test]
    fn points() {
        // Point values taken (rounded) from the old Python implementation.
        assert_eq!(
            glossbrenner(Sex::M, WeightKg::from_i32(100), WeightKg::from_i32(1000)),
            Points::from(581.27)
        );
        assert_eq!(
            glossbrenner(Sex::F, WeightKg::from_i32(60), WeightKg::from_i32(500)),
            Points::from(492.53032)
        );

        // Zero bodyweight should be treated as "unknown bodyweight".
        assert_eq!(
            glossbrenner(Sex::M, WeightKg::from_i32(0), WeightKg::from_i32(500)),
            Points::from_i32(0)
        );
    }
}

© Raidriar7170, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in artifacts/v0.3/skillsbench-pilot/v0.3-stage2-input-package-candidate-20260701T010000Z/candidate-data/skill-sources/skillsbench__powerlifting of Raidriar7170/hermes-skilleval.

Open the folder on GitHubat commit 8f6a21e

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in Raidriar7170/hermes-skilleval, which our catalogue first saw on October 7, 2026.

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Questions about Powerlifting

What does Powerlifting do?

Calculating powerlifting scores to determine the performance of lifters across different weight classes. Powerlifting is an agent skill from Raidriar7170/hermes-skilleval. Calculating powerlifting scores to determine the performance of lifters across different weight classes.

When should I use Powerlifting?

Powerlifting fits situations like: data & Analytics work in your project.

How do I install Powerlifting in Claude Code?

Run `npx skills add Raidriar7170/hermes-skilleval --skill powerlifting -a claude-code`. Or copy the skill folder (artifacts/v0.3/skillsbench-pilot/v0.3-stage2-input-package-candidate-20260701T010000Z/candidate-data/skill-sources/skillsbench__powerlifting in Raidriar7170/hermes-skilleval) into .claude/skills/powerlifting in your project. Claude Code loads it when a task matches its description.

How do I install Powerlifting in Codex?

Run `npx skills add Raidriar7170/hermes-skilleval --skill powerlifting -a codex`. Or copy the skill folder (artifacts/v0.3/skillsbench-pilot/v0.3-stage2-input-package-candidate-20260701T010000Z/candidate-data/skill-sources/skillsbench__powerlifting in Raidriar7170/hermes-skilleval) into .agents/skills/powerlifting in your project. Codex loads it when a task matches its description.

Can I use Powerlifting in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Raidriar7170/hermes-skilleval --skill powerlifting -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/powerlifting, .gemini/skills/powerlifting, .github/skills/powerlifting and .opencode/skills/powerlifting in your project.

What does Powerlifting need to run?

SKILL.md names no scripts, command-line tools or credentials: Powerlifting is instructions for the agent only. Our summary lists: Python 3.

Does Powerlifting access the network?

SKILL.md names 4 domains. As links in the text: powerliftpro.app, powerlifting.sport, en.wikipedia.org and worldpowerliftingcongress.com. This is read from the text; nothing was executed.

Is Powerlifting safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Powerlifting use?

Powerlifting is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Powerlifting use?

About 3.7k tokens (SKILL.md is roughly 15k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Powerlifting?

Skills that share tags, products or a category with Powerlifting: Exploratory Data Analysis (spacering-net/codeg, 3.8k stars), Matplotlib (zLanqing/codex-claude-academic-skills, 4.6k stars), Scikit Learn (zLanqing/codex-claude-academic-skills, 4.6k stars) and Chart Visualization (bytedance/deer-flow, 83k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Powerlifting?

Raidriar7170 (a GitHub user) maintains it in Raidriar7170/hermes-skilleval, which has 125 GitHub stars. The repository holds 8 skills in this directory. The repository was last updated on September 26, 2026.

Source: Raidriar7170/hermes-skilleval on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.