Implement Elo rating system to rank items or players from pairwise comparison outcomes.
$ npx skills add asgard-ai-platform/skills --skill algo-rank-elo -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install asgard-ai-platform/skills algo-rank-elo --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/algo-rank-elo .claude/skills/algo-rank-elo && rm -rf skills-srcUse ~/.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/
Install the "algo-rank-elo" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-rank-elo into .claude/skills/algo-rank-elo/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-rank-elo", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/asgard-ai-platform/skills/tree/main/algo-rank-eloType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add asgard-ai-platform/skills --skill algo-rank-elo -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install asgard-ai-platform/skills algo-rank-elo --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/algo-rank-elo .agents/skills/algo-rank-elo && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "algo-rank-elo" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-rank-elo into .agents/skills/algo-rank-elo/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-rank-elo", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add asgard-ai-platform/skills --skill algo-rank-elo -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install asgard-ai-platform/skills algo-rank-elo --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/algo-rank-elo .cursor/skills/algo-rank-elo && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "algo-rank-elo" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-rank-elo into .cursor/skills/algo-rank-elo/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-rank-elo", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/asgard-ai-platform/skills.git --path algo-rank-elo--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add asgard-ai-platform/skills --skill algo-rank-elo -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install asgard-ai-platform/skills algo-rank-elo --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/algo-rank-elo .gemini/skills/algo-rank-elo && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "algo-rank-elo" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-rank-elo into .gemini/skills/algo-rank-elo/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-rank-elo", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install asgard-ai-platform/skills algo-rank-eloInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add asgard-ai-platform/skills --skill algo-rank-elo -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/algo-rank-elo .github/skills/algo-rank-elo && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "algo-rank-elo" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-rank-elo into .github/skills/algo-rank-elo/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-rank-elo", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add asgard-ai-platform/skills --skill algo-rank-elo -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install asgard-ai-platform/skills algo-rank-elo --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/algo-rank-elo .opencode/skills/algo-rank-elo && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "algo-rank-elo" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-rank-elo into .opencode/skills/algo-rank-elo/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-rank-elo", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
algo-rank-eloImplement Elo rating system to rank items or players from pairwise comparison outcomes.
Algo Rank Elo is an agent skill from asgard-ai-platform/skills. Implement Elo rating system to rank items or players from pairwise comparison outcomes. Use this skill when the user needs to rank items from head-to-head matchups, build a competitive rating system, or evaluate relative quality from comparison data — even if they say 'player rating', 'ranking from comparisons', or 'competitive scoring system'.
Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including scripts and reference files (for example `examples/sample_input.json`, `references/bradley-terry.md` and `references/variable-k.md`).
The repository describes itself as: 301 open-source coding agent skills across 22 domains — methodology, judgment & gotchas packaged as Claude Agent Skills for the Asgard AI Platform. The licence is MIT.
4 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 4e7f4f8. It shows what the files ask for, not the result of running them.
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.
Ships 1 file in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
pythonFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Algo Rank Elo loads about 1.1k tokens when it runs, and up to ~5.2k if it reads all its reference files. Until then it costs about 90 tokens; SKILL.md has 417 words of instructions outside code blocks.
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.
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); the scripts in this folder are not scanned.
The full file from asgard-ai-platform/skills at commit 4e7f4f8, republished under its MIT licence (© asgard-ai-platform). 417 words, ~1,053 tokens.
.claude/skills/algo-rank-elo/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.Elo assigns numerical ratings that update after each pairwise comparison. Winner gains points, loser loses points. The amount exchanged depends on expected vs actual outcome. Originally for chess, now used for sports, games, and A/B preference testing. Update runs in O(1) per match.
Trigger conditions:
When NOT to use:
IRON LAW: Elo Assumes Each Matchup Is Independent and Stationary
Rating changes are based on surprise: beating a higher-rated opponent
gains more points than beating a lower-rated one. K-factor controls
update speed: high K (32) = volatile, fast adaptation. Low K (16) =
stable, slow adaptation. Choose K based on how quickly skill changes.Initialize all participants at base rating (typically 1500). Collect match results: winner, loser (or draw). Gate: Valid match data, no self-matches.
Check: total rating points conserved (zero-sum). Rating distribution is reasonable (no extreme values from data errors). Gate: Ratings conserved, top-ranked items pass sanity check.
Return sorted ratings with confidence indicators.
{
"ratings": [{"id": "player_A", "rating": 1720, "matches": 50, "wins": 35, "losses": 15}],
"metadata": {"k_factor": 32, "initial_rating": 1500, "total_matches": 500}
}Input: Player A (1500) beats Player B (1500), K=32 Expected: E_A = 0.5, S_A = 1. R_A_new = 1500 + 32×(1-0.5) = 1516. R_B_new = 1484.
| Input | Expected | Why |
|---|---|---|
| 1500 beats 2000 | Large rating gain (~29 pts at K=32) | Huge upset, large surprise |
| 2000 beats 1500 | Small rating gain (~3 pts at K=32) | Expected outcome, minimal surprise |
| Draw between equals | No change | Expected outcome exactly matches actual |
| Script | Description | Usage |
|---|---|---|
scripts/elo.py | Update Elo ratings (single match or batch) with zero-sum verification | python scripts/elo.py --help |
Run python scripts/elo.py --verify to execute built-in sanity tests.
references/bradley-terry.mdreferences/variable-k.md© asgard-ai-platform, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 4 other files (scripts, references) in algo-rank-elo of asgard-ai-platform/skills.
Open the folder on GitHubat commit 4e7f4f8
Algo Rank Elo next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Algo Rank Elo this skillasgard-ai-platform/skills | 242 | — | ~1.1k | Automated safety check: Pass | MIT | |
| Implementing API Abuse Detection With Rate Limitingmukul975/Anthropic-Cybersecurity-Skills | 34k | — | ~3.4k | Automated safety check: Pass | Apache-2.0 | |
| Implementsickn33/agentic-awesome-skills | 47k | 5 repos | ~306 | Automated safety check: Pass | MIT | |
| Implementcodewhale-hq/Codewhale | 41k | — | ~190 | Automated safety check: Pass | MIT | |
| MCP Implementation Security Reviewgithub/awesome-copilot | 40k | — | ~5.2k | Automated safety check: Pass | MIT | |
| Implementing API Key Security Controlsmukul975/Anthropic-Cybersecurity-Skills | 34k | — | ~4k | Automated safety check: Pass | Apache-2.0 |
mukul975/Anthropic-Cybersecurity-Skills
Implements API abuse detection using token bucket, sliding window, and fixed window rate-limiting algorithms backed by Redis, including adaptive limits that tighten during detected attacks and relax…
sickn33/agentic-awesome-skills
Implement a piece of work based on a PRD or set of issues. An agent skill from sickn33/agentic-awesome-skills.
codewhale-hq/Codewhale
Carry an authorized, defined request or approved plan through scoped edits and proportionate verification.
github/awesome-copilot
Review the implementation source code of MCP (Model Context Protocol) servers, clients, and tool handlers against a security baseline — authentication, sessions, rate limiting, input-schema…
mukul975/Anthropic-Cybersecurity-Skills
Implements secure API key generation with sufficient entropy, server-side hashing (SHA-256/bcrypt) instead of plaintext storage, per-key scoping to endpoints/IPs/rate limits, zero-downtime rotation…
mukul975/Anthropic-Cybersecurity-Skills
Implements API threat protection using Google Apigee reverse-proxy policies, including JSON/XML threat protection, OAuth 2.0 enforcement, SpikeArrest rate limiting, regex-based threat detection, and…
asgard-ai-platform/skills
Implement BM25 ranking function for e-commerce product search relevance scoring.
asgard-ai-platform/skills
Calculate Cpk process capability index to assess whether a process meets specification requirements.
asgard-ai-platform/skills
Calculate price elasticity of demand to quantify how price changes affect sales volume.
asgard-ai-platform/skills
Apply Bayesian averaging to rank items by combining observed ratings with prior expectations.
asgard-ai-platform/skills
Calculate Wilson Score confidence intervals for ranking items by positive proportion with sample size correction.
asgard-ai-platform/skills
Calculate Altman Z-Score to predict corporate bankruptcy probability from financial ratios.
Implement Elo rating system to rank items or players from pairwise comparison outcomes. Algo Rank Elo is an agent skill from asgard-ai-platform/skills. Implement Elo rating system to rank items or players from pairwise comparison outcomes.
Algo Rank Elo fits situations like: the user needs to rank items from head-to-head matchups; build a competitive rating system; evaluate relative quality from comparison data — even if they say player rating; ranking from comparisons.
Run `npx skills add asgard-ai-platform/skills --skill algo-rank-elo -a claude-code`. Or copy the skill folder (algo-rank-elo in asgard-ai-platform/skills) into .claude/skills/algo-rank-elo in your project. Claude Code loads it when a task matches its description.
Run `npx skills add asgard-ai-platform/skills --skill algo-rank-elo -a codex`. Or copy the skill folder (algo-rank-elo in asgard-ai-platform/skills) into .agents/skills/algo-rank-elo in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add asgard-ai-platform/skills --skill algo-rank-elo -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/algo-rank-elo, .gemini/skills/algo-rank-elo, .github/skills/algo-rank-elo and .opencode/skills/algo-rank-elo in your project.
Going by SKILL.md and its folder, Algo Rank Elo needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
Algo Rank Elo is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 1.1k tokens (SKILL.md is roughly 4.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 4.2k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Algo Rank Elo: Implementing API Abuse Detection With Rate Limiting (mukul975/Anthropic-Cybersecurity-Skills, 34k stars), Implement (sickn33/agentic-awesome-skills, 47k stars), Implement (codewhale-hq/Codewhale, 41k stars) and MCP Implementation Security Review (github/awesome-copilot, 40k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
asgard-ai-platform (a GitHub organization) maintains it in asgard-ai-platform/skills, which has 242 GitHub stars. The repository holds 207 skills in this directory. The repository was last updated on June 6, 2026.
Source: asgard-ai-platform/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.
