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Implement Gale-Shapley stable matching algorithm for two-sided matching problems.
MITAuto-check passed
Install Algo Hr Matching
$ npx skills add asgard-ai-platform/skills --skill algo-hr-matching -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install asgard-ai-platform/skills algo-hr-matching --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-hr-matching .claude/skills/algo-hr-matching && 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-hr-matching" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-hr-matching into .claude/skills/algo-hr-matching/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-hr-matching", 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-hr-matchingType 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-hr-matching -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install asgard-ai-platform/skills algo-hr-matching --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-hr-matching .agents/skills/algo-hr-matching && 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-hr-matching" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-hr-matching into .agents/skills/algo-hr-matching/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-hr-matching", 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-hr-matching -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install asgard-ai-platform/skills algo-hr-matching --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-hr-matching .cursor/skills/algo-hr-matching && 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-hr-matching" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-hr-matching into .cursor/skills/algo-hr-matching/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-hr-matching", 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-hr-matching--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-hr-matching -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install asgard-ai-platform/skills algo-hr-matching --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-hr-matching .gemini/skills/algo-hr-matching && 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-hr-matching" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-hr-matching into .gemini/skills/algo-hr-matching/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-hr-matching", 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-hr-matchingInstalls 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-hr-matching -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-hr-matching .github/skills/algo-hr-matching && 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-hr-matching" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-hr-matching into .github/skills/algo-hr-matching/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-hr-matching", 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-hr-matching -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-hr-matching --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-hr-matching .opencode/skills/algo-hr-matching && 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-hr-matching" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-hr-matching into .opencode/skills/algo-hr-matching/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-hr-matching", 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.
Facts
- Skill name
algo-hr-matching- GitHub stars
- 242
- Token cost
- ~1k tokens
- SKILL.md length
- 392 words
- Files
- 4 (incl. references)
- Skills in repo
- 207
- Repo updated
- First seen
- Licence
- MIT
At a glance
Implement Gale-Shapley stable matching algorithm for two-sided matching problems.
- Works in 4 steps: Input Validation → Core Algorithm → Verification → …
- The user needs to match candidates to positions
- SKILL.md covers Overview, When to Use, Algorithm and Output Format, plus 3 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Algo Hr Matching is an agent skill from asgard-ai-platform/skills. Implement Gale-Shapley stable matching algorithm for two-sided matching problems. Use this skill when the user needs to match candidates to positions, assign students to schools, or solve any two-sided preference matching — even if they say 'optimal job matching', 'stable assignment', or 'candidate-position pairing'.
Its SKILL.md is about 1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including reference files (for example `examples/sample_scenario.md`, `references/many-to-one.md` and `references/strategic-manipulation.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.
When your agent uses it
- The user needs to match candidates to positions
- Assign students to schools
- Solve any two-sided preference matching — even if they say optimal job matching
- Stable assignment
Example prompts
- “optimal job matching”
- “stable assignment”
- “candidate-position pairing”
- “/algo-hr-matching”
Workflow steps
4 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 4e7f4f8. 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 json).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
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
Algo Hr Matching loads about 1k tokens when it runs, and up to ~6.2k if it reads all its reference files. Until then it costs about 84 tokens; SKILL.md has 392 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.
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 asgard-ai-platform/skills at commit 4e7f4f8, republished under its MIT licence (© asgard-ai-platform). 392 words, ~1,020 tokens.
Download SKILL.mdSave it as
.claude/skills/algo-hr-matching/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.- name
- algo-hr-matching
- description
- Implement Gale-Shapley stable matching algorithm for two-sided matching problems. Use this skill when the user needs to match candidates to positions, assign students to schools, or solve any two-sided preference matching — even if they say 'optimal job matching', 'stable assignment', or 'candidate-position pairing'.
- metadata.category
- WP-42 HR 演算法
- metadata.tags
- hr, stable-matching, gale-shapley, assignment
Gale-Shapley Stable Matching
Overview
Gale-Shapley (deferred acceptance) finds a stable matching between two equally-sized sets where no unmatched pair prefers each other over their current match. Runs in O(n²) worst case. Proposer-optimal: the proposing side gets their best stable partner.
When to Use
Trigger conditions:
- Matching candidates to job positions based on mutual preferences
- Assigning students to schools or residents to hospitals
- Any two-sided matching where stability (no blocking pairs) is required
When NOT to use:
- For one-sided assignment (use Hungarian algorithm)
- When preferences are based on scores, not rankings (use optimization)
Algorithm
IRON LAW: The Proposing Side Gets Their BEST Stable Partner
Gale-Shapley is proposer-optimal and reviewer-pessimal. If employers
propose, they get their best stable match; candidates get their worst.
The CHOICE of who proposes determines which stable matching is found.Phase 1: Input Validation
Collect: preference rankings from both sides. Each participant ranks all members of the other side. Gate: Complete preference lists, equal-sized groups (or handle unequal with dummy entries).
Phase 2: Core Algorithm
- All proposers are "free" (unmatched)
- While any proposer is free and hasn't proposed to everyone:
- Free proposer proposes to their highest-ranked unproposed-to reviewer
- Reviewer accepts if unmatched, or replaces current match if new proposer is preferred
- Replaced proposer becomes free again
- Terminate when all proposers are matched
Phase 3: Verification
Check stability: for every unmatched pair (a,b), verify that at least one of them prefers their current match over the other. No blocking pairs = stable. Gate: Zero blocking pairs found.
Phase 4: Output
Return matching with stability confirmation.
Output Format
{
"matching": [{"proposer": "Candidate_A", "reviewer": "Company_X", "proposer_rank": 1, "reviewer_rank": 2}],
"metadata": {"pairs": 10, "rounds": 23, "blocking_pairs": 0, "proposer_side": "candidates"}
}Examples
Sample I/O
Input: 3 candidates, 3 companies, each with full preference rankings Expected: Stable matching with zero blocking pairs. Candidate-proposing gives candidate-optimal result.
Show full SKILL.md (147 more words)Show less
Edge Cases
| Input | Expected | Why |
|---|---|---|
| All prefer same #1 | Still terminates, stable | Rejected proposers move to next choice |
| Identical preferences | Unique stable matching | Only one possibility |
| Unequal sides | Some unmatched on larger side | Add dummy entries or use many-to-one variant |
Gotchas
- Proposer advantage: If candidates propose, they get better matches than if companies propose. This is a design choice with equity implications.
- Incomplete preferences: If participants don't rank everyone, unmatched results are possible. Handle with acceptable-partner thresholds.
- Many-to-one: Hospital-resident matching uses the many-to-one variant (each hospital has multiple slots). Use the Roth-Peranson extension.
- Strategic manipulation: The reviewing side CAN benefit from misreporting preferences (truncating lists). The proposing side cannot — truthful reporting is dominant strategy for proposers.
- Preference elicitation: Getting honest, complete rankings is hard in practice. People satisfice rather than fully rank all options.
References
- For many-to-one matching (hospital-resident), see
references/many-to-one.md - For strategic behavior analysis, see
references/strategic-manipulation.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
Files
SKILL.md and 3 other files (references) in algo-hr-matching of asgard-ai-platform/skills.
- SKILL.md
- examples/sample_scenario.md
- references/many-to-one.md
- references/strategic-manipulation.md
Open the folder on GitHubat commit 4e7f4f8
Compare with similar skills
Algo Hr Matching 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 Hr Matching this skillasgard-ai-platform/skills | 242 | — | ~1k | Automated safety check: Pass | MIT | |
| Manim Video Productionbrowser-use/video-use | 29k | 6 repos | ~3k | Automated safety check: Pass | MIT | |
| Logseq Review Workflow Evallogseq/logseq | 45k | — | ~1k | Automated safety check: Pass | AGPL-3.0 | |
| Baoyu URL To Markdownsdyckjq-lab/llm-wiki-skill | 2.5k | 2 repos | ~3.2k | Automated safety check: Pass | None | |
| DeepTutor CLIHKUDS/DeepTutor | 41k | — | ~2.8k | Automated safety check: Pass | Apache-2.0 | |
| Obsidian CLIAtmosphere/atmosphere | 3.8k | 13 repos | ~795 | Automated safety check: Pass | Apache-2.0 |
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Questions about Algo Hr Matching
What does Algo Hr Matching do?
Implement Gale-Shapley stable matching algorithm for two-sided matching problems. Algo Hr Matching is an agent skill from asgard-ai-platform/skills. Implement Gale-Shapley stable matching algorithm for two-sided matching problems.
When should I use Algo Hr Matching?
Algo Hr Matching fits situations like: the user needs to match candidates to positions; assign students to schools; solve any two-sided preference matching — even if they say optimal job matching; stable assignment.
How do I install Algo Hr Matching in Claude Code?
Run `npx skills add asgard-ai-platform/skills --skill algo-hr-matching -a claude-code`. Or copy the skill folder (algo-hr-matching in asgard-ai-platform/skills) into .claude/skills/algo-hr-matching in your project. Claude Code loads it when a task matches its description.
How do I install Algo Hr Matching in Codex?
Run `npx skills add asgard-ai-platform/skills --skill algo-hr-matching -a codex`. Or copy the skill folder (algo-hr-matching in asgard-ai-platform/skills) into .agents/skills/algo-hr-matching in your project. Codex loads it when a task matches its description.
Can I use Algo Hr Matching 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 asgard-ai-platform/skills --skill algo-hr-matching -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-hr-matching, .gemini/skills/algo-hr-matching, .github/skills/algo-hr-matching and .opencode/skills/algo-hr-matching in your project.
What does Algo Hr Matching need to run?
SKILL.md names no scripts, command-line tools or credentials: Algo Hr Matching is instructions for the agent only.
Does Algo Hr Matching access the network?
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.
Is Algo Hr Matching 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 Algo Hr Matching use?
Algo Hr Matching 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 Algo Hr Matching use?
About 1k tokens (SKILL.md is roughly 4.1k 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 5.1k tokens, read only when the agent opens those files.
What are the alternatives to Algo Hr Matching?
Skills that share tags, products or a category with Algo Hr Matching: Manim Video Production (browser-use/video-use, 29k stars), Logseq Review Workflow Eval (logseq/logseq, 45k stars), Baoyu URL To Markdown (sdyckjq-lab/llm-wiki-skill, 2.5k stars) and DeepTutor CLI (HKUDS/DeepTutor, 41k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Algo Hr Matching?
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.