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.
npx skills add JayRHa/AgentSkills --skill decision-matrixgit clone --depth 1 https://github.com/JayRHa/AgentSkillsWrote 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.
[](https://agentmods.dev/skills/jayrha/agentskills/decision-matrix)<a href="https://agentmods.dev/skills/jayrha/agentskills/decision-matrix"><img src="https://agentmods.dev/badge/skills/jayrha/agentskills/decision-matrix/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.
<a href="https://agentmods.dev/skills/jayrha/agentskills/decision-matrix"><img src="https://agentmods.dev/badge/skills/jayrha/agentskills/decision-matrix.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00122 | $0.01697 |
| Opus 5.5 | $0.00049 | $0.00679 |
| Sonnet 5.5 | $0.00024 | $0.00339 |
| Haiku 4.5 | $0.00012 | $0.00170 |
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 1mo 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.
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.
-
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.
-
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.
-
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).
-
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").
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.
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.
- 1mo ago First seen · 128 lines · 122 tokens per session scan A b3b5548de93a
decision-matrix is a skill published in the GitHub repository JayRHa/AgentSkills (12 stars, last pushed 2mo ago), licensed MIT. It adds 122 tokens to every session and 1,697 once invoked, about $0.0005 per session on Opus 5.5. 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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