verdict

verdict is a skill for Claude Code from duthaho/skillhub. It costs 201 tokens per session (2,147 once invoked), scanned A, original, MIT.

A technology decision skill that compares software options against stated project needs and constraints. It produces a written recommendation supported by research and weighted criteria.

In plain words
What is it for?
Use it to compare two to four options, assess project health and community feedback, and record why one option fits best.
Why use it?
It makes choices such as databases, frameworks, libraries, or buy-versus-build decisions easier to explain and review later.

Skill for Claude Code

Written for Claude Code: installed under .claude/. Also seen: mentions subagents.

Part of the research plugin — 7 skills shipped together

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/duthaho/skillhub/verdict
Any agent
npx skills add duthaho/skillhub --skill verdict
Clone the repo
git clone --depth 1 https://github.com/duthaho/skillhub

Made for: Claude Code.

Or install research, the plugin that ships this one along with the rest of its 7 skills.

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 verdict

README.md
[![agentmods](https://agentmods.dev/badge/skills/duthaho/skillhub/verdict.svg)](https://agentmods.dev/skills/duthaho/skillhub/verdict)
Your own site
<a href="https://agentmods.dev/skills/duthaho/skillhub/verdict"><img src="https://agentmods.dev/badge/skills/duthaho/skillhub/verdict.svg" alt="Measured on agentmods" height="20"></a>
Per session 201 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,147 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 5.1 $0.00201 $0.02147
Opus 5 $0.00101 $0.01073
Sonnet 5 $0.00040 $0.00429
Haiku 4.5 $0.00020 $0.00215

Measured 6d ago against content hash 9d1e321c8921, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

verdict 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 6d 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.

.claude/skills/verdict/SKILL.md · 168 lines

How it starts

The opening of the file, as written. The whole thing — 168 lines — stays where its author put it; the contents beside it link to each section on GitHub.

verdict — technology decision briefs

/verdict <option A> vs <option B> [vs <option C>] for <context> [constraints] /verdict revisit <path-to-previous-brief> — re-check an old verdict (see Revisit mode)

Answer one question: given this context, which option should we pick, and what evidence supports that? You research every option the same way, score against explicit weighted criteria, and write the decision up so it can be defended in a design review — or revisited later when the facts change.

Step 0 — Frame the decision

Extract from the request (ask 1–2 questions only if genuinely blocking):

  • Options (2–4). If the user names only one ("should we use X?"), add the status-quo/do-nothing option and the 1–2 strongest alternatives — say you did.
  • Decision context: what is being built, scale (users/QPS/data volume), team size and existing skills, current stack, timeline.
  • Constraints & dealbreakers: licensing limits, budget, compliance, must-run-on-prem, language lock-in, etc.
  • Criteria weights — defaults below; honor plain-language overrides ("ops burden matters most", "we optimize for hiring").

State every assumption you make in the brief header. A verdict against the wrong context is worse than no verdict.

Step 1 — Fan out: parallel research sub-agents (per option)

Spawn one research sub-agent per option, concurrently (one message, multiple Agent calls, subagent_type: "Explore"). Each covers five angles and returns structured findings — every claim with a url + 1-line evidence, anything unverified marked as such:

  1. Project health (for OSS/tools; skip for pure approaches): GitHub keyless REST — stars trend, commit/release cadence, open-vs-closed issue velocity, bus factor, age, backing org/funding. A beautiful README with a dead repo behind it is a trap; this angle catches it. Note: keyless GitHub REST allows 60 req/hr shared across all sub-agents — budget calls, and on a 403 rate-limit mark the angle unknown rather than retrying in a loop.
  2. Community sentiment: HN Algolia (hn.algolia.com/api/v1/search?query=...)
    • web search + Reddit fallback — what practitioners report after adopting: praise, recurring pain points, migration-away stories. Weight experience reports over launch-day hype — and over SEO comparison content ("X vs Y in 2026" listicle farms are marketing surface, not adoption evidence; a real practitioner thread outweighs ten of them).
  3. Technical claims: official docs + independent benchmarks/comparisons for the capabilities the context actually needs. Label every claim vendor-made vs independently verified. Note version/date — a 2022 benchmark may be void.
  4. Operational story: licensing (and any recent license changes), managed vs self-hosted options, pricing shape, upgrade pain, hiring pool / learning curve for the stated team.
  5. Security posture: CVE history and severity pattern, GitHub security advisories, how fast past vulnerabilities were patched, safety of the default configuration, and any compliance certifications the context requires. This evidence feeds the Maturity & health and Operational burden scores (no separate criterion) — but an unpatched-critical-CVE pattern can be a dealbreaker in its own right.

Read the full file on GitHub · 168 lines

Files

What ships with it

1 file 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. 6d ago First seen · 168 lines · 201 tokens per session scan A 9d1e321c8921

Subscribe to this mod's changes

verdict is a skill published in the GitHub repository duthaho/skillhub (9 stars, last pushed 13d ago), licensed MIT. It adds 201 tokens to every session and 2,147 once invoked, about $0.0010 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