constraint-elimination

constraint-elimination is a skill for Claude Code from sina-heidariaan/cold-run. It costs 109 tokens per session (2,616 once invoked), scanned A, original, no licence file.

A decision-checking method for comparing named software architectures or data stores against stated requirements. It separates facts from guesses before ruling out an option.

In plain words
What is it for?
Use it to compare two or more architectures or data stores, identify the deciding requirement, and record the threshold that would make a rejected option viable again.
Why use it?
It prevents guesses from deciding an architecture choice and shows exactly which requirement caused a candidate to be rejected.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the cold-run plugin — 24 skills, 3 commands, 1 MCP server shipped together

Good fit Use it to compare two or more architectures or data stores, identify the deciding requirement, and record the threshold that would make a rejected option viable again.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/sina-heidariaan/cold-run/constraint-elimination
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 sina-heidariaan/cold-run --skill constraint-elimination
Clone the repo
git clone --depth 1 https://github.com/sina-heidariaan/cold-run

Made for: Claude Code.

Or install cold-run, the plugin that ships this one along with the rest of its 24 skills, 3 commands, 1 MCP server.

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 constraint-elimination

README.md
[![agentmods](https://agentmods.dev/badge/skills/sina-heidariaan/cold-run/constraint-elimination/github.svg)](https://agentmods.dev/skills/sina-heidariaan/cold-run/constraint-elimination)
Your own site
<a href="https://agentmods.dev/skills/sina-heidariaan/cold-run/constraint-elimination"><img src="https://agentmods.dev/badge/skills/sina-heidariaan/cold-run/constraint-elimination/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 constraint-elimination

Your own site · 80×15
<a href="https://agentmods.dev/skills/sina-heidariaan/cold-run/constraint-elimination"><img src="https://agentmods.dev/badge/skills/sina-heidariaan/cold-run/constraint-elimination.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 109 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,616 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.
Origin unknown 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 $0.00109 $0.02616
Opus 5 $0.00055 $0.01308
Sonnet 5 $0.00022 $0.00523
Haiku 4.5 $0.00011 $0.00262

Measured 10d ago against content hash e446aba4d10b, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

constraint-elimination 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 10d 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.

skills/constraint-elimination/SKILL.md · 182 lines

The source is not reproduced here

No licence file

A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.

Read it on GitHub

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. 10d ago First seen · 182 lines · 109 tokens per session scan A e446aba4d10b

Subscribe to this mod's changes

constraint-elimination is a skill published in the GitHub repository sina-heidariaan/cold-run (0 stars, last pushed 19d ago), with no licence file. It adds 109 tokens to every session and 2,616 once invoked, about $0.0005 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 2026-08-31.

Related

Other skills, from other repositories

llm-as-judge-evaluation

Evaluate LLM outputs using frontier models as judges. Use for pairwise model comparison, quality scoring with custom rubrics, and automated evaluation pipelines. Covers position bias mitigation, statistical significance, and generating preference data for DPO/RLHF.

synthetic-sciences/openscience · 56 tokens

hugging-face-evaluation

Add and manage evaluation results in Hugging Face model cards. Supports extracting eval tables from README content, importing scores from Artificial Analysis API, and running custom model evaluations with vLLM/lighteval. Works with the model-index metadata format.

synthetic-sciences/openscience · 55 tokens

skill-optimizer

Use when creating, running, debugging, or documenting skill-optimizer workbench evals; working with agent skill cases, suites, graders, traces, Docker workspaces, OpenRouter model matrices, or the skill-optimizer SDK/CLI.

fastxyz/skill-optimizer · 52 tokens

pdf

Use this skill when a task requires reading, creating, splitting, merging, or otherwise manipulating PDF files.

fastxyz/skill-optimizer · 23 tokens

llm-evaluator

Evaluate LLM outputs systematically using LLM-as-judge, human evaluation frameworks, and regression testing. Use when assessing model quality, comparing models, or preventing quality regression.

chandrudp29/skillhub · 39 tokens

mle-workflow

Production ML engineering workflow — data contracts, reproducible training, evaluation gates, deployment, and monitoring. Use when building, reviewing, or hardening ML systems beyond notebooks.

chandrudp29/skillhub · 39 tokens