decision-matrix

A structured way to compare several choices by listing what matters, assigning importance to each factor, and scoring every option. The result is a visible calculation that shows how the recommendation was reached.

In plain words
What is it for?
Use it to compare vendors, job offers, tools, projects, or other distinct alternatives with multiple trade-offs.
Why use it?
It replaces an unclear gut decision with reasoning that can be checked, discussed, and adjusted. It also shows when a small change in priorities would change the result.

Skill for Claude CodeCodex

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.

agentmods
npx agentmods add skills/jayrha/agentskills/decision-matrix
Any agent
npx skills add JayRHa/AgentSkills --skill decision-matrix
Clone the repo
git clone --depth 1 https://github.com/JayRHa/AgentSkills

Made for: Claude Code, Codex.

Per session 122 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,697 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00122 $0.01697
Opus 5 $0.00061 $0.00848
Sonnet 5 $0.00024 $0.00339
Haiku 4.5 $0.00012 $0.00170

Measured 2d ago against content hash b3b5548de93a, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

decision-matrix 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 2d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/decision_matrix.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

decision-matrix/SKILL.md · 128 lines

How it starts

The opening of the file, as written. The whole thing — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Decision Matrix

Overview

A weighted decision matrix (a.k.a. weighted scoring model, Pugh matrix, or trade study) turns a fuzzy "which should I pick?" question into a transparent, defensible number. You list the options (the things you choose between), the criteria (what matters), assign each criterion a weight (how much it matters), score every option on every criterion, and compute a weighted total. The highest total is the recommended choice.

Keywords: decision making, weighted scoring, trade-off analysis, multi-criteria decision analysis (MCDA), Pugh matrix, vendor selection, job offer comparison, prioritization.

Use this skill whenever someone must choose among 2+ discrete alternatives and wants the reasoning to be explicit rather than a gut feel. Do not force a matrix on a binary yes/no decision or a problem with a single dominant constraint — say so instead.

Workflow

Follow these steps in order. Confirm with the user at steps 1–4 before computing.

  1. Frame the decision. State the single question being answered (e.g. "Which CRM should we adopt?"). One decision per matrix. If the user has bundled several decisions, split them.

  2. List the options. Gather 2–7 concrete alternatives. Fewer than 2 is not a decision; more than ~7 becomes noisy — shortlist first. Always consider adding a baseline "do nothing / status quo" option when relevant.

  3. Elicit the criteria. Ask what factors matter. Aim for 4–8 criteria. Each must be:

    • Distinct (not measuring the same thing twice — avoid double-counting).
    • Discriminating (options actually differ on it; drop criteria where all score equally).
    • Phrased so higher = better (rename "Cost" → "Affordability", or mark it as a cost criterion to invert — see references/scoring-guide.md).
  4. Set weights. Assign each criterion a weight reflecting its importance. Use any consistent scale (1–5, or percentages summing to 100). Normalize internally so totals are comparable. Capture must-have constraints separately as pass/fail gates, not weights — a dealbreaker should eliminate an option, not just dock points (see references/scoring-guide.md, "Constraints vs. criteria").

Read the full file on GitHub · 128 lines

Files

What ships with it

4 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. 2d ago First seen · 128 lines · 122 tokens per session scan A b3b5548de93a

Subscribe to this mod's changes

decision-matrix is a skill published in the GitHub repository JayRHa/AgentSkills (4 stars, last pushed 1mo ago), licensed MIT. It adds 122 tokens to every session and 1,697 once invoked, about $0.0006 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.

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