algo-hr-matching

algo-hr-matching is a skill for Claude Code, Codex from asgard-ai-platform/skills. It costs 65 tokens per session (899 once invoked), scanned A, original, MIT.

An algorithm for matching people or organizations on two sides according to ranked preferences. It produces a stable assignment, meaning no unmatched pair would both prefer each other over their assigned partners.

In plain words
What is it for?
Matching job candidates with positions, students with schools, or residents with hospitals when both sides provide preference rankings.
Why use it?
It prevents pairs from having a reason to abandon their assignments for one another. The result also makes clear that the side making proposals receives the best outcome available among stable assignments.

Skill for Claude CodeCodex

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

Good fit Matching job candidates with positions, students with schools, or residents with hospitals when both sides provide preference rankings.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/asgard-ai-platform/skills/algo-hr-matching
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 asgard-ai-platform/skills --skill algo-hr-matching
Clone the repo
git clone --depth 1 https://github.com/asgard-ai-platform/skills

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 algo-hr-matching

README.md
[![agentmods](https://agentmods.dev/badge/skills/asgard-ai-platform/skills/algo-hr-matching/github.svg)](https://agentmods.dev/skills/asgard-ai-platform/skills/algo-hr-matching)
Your own site
<a href="https://agentmods.dev/skills/asgard-ai-platform/skills/algo-hr-matching"><img src="https://agentmods.dev/badge/skills/asgard-ai-platform/skills/algo-hr-matching/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 algo-hr-matching

Your own site · 80×15
<a href="https://agentmods.dev/skills/asgard-ai-platform/skills/algo-hr-matching"><img src="https://agentmods.dev/badge/skills/asgard-ai-platform/skills/algo-hr-matching.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 65 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 899 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. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 $0.00065 $0.00899
Opus 5 $0.00032 $0.00449
Sonnet 5 $0.00013 $0.00180
Haiku 4.5 $0.00006 $0.00090

Measured 13d ago against content hash a37bad94eca5, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

algo-hr-matching 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 13d ago.

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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

algo-hr-matching/SKILL.md · 88 lines

How it starts

The opening of the file, as written. The whole thing — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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

  1. All proposers are "free" (unmatched)
  2. 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
  3. 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"}
}

Read the full file on GitHub · 88 lines

Files

What ships with it

3 files 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. 13d ago First seen · 88 lines · 65 tokens per session scan A a37bad94eca5

Subscribe to this mod's changes

algo-hr-matching is a skill published in the GitHub repository asgard-ai-platform/skills (228 stars, last pushed 3mo ago), licensed MIT. It adds 65 tokens to every session and 899 once invoked, about $0.0003 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 2026-08-30.

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