decision-reviewer

decision-reviewer is an agent for coding agents from vladolaru/claude-code-plugins. It costs 37 tokens per session (3,944 once invoked), scanned A, original, MIT.

A critique tool that stress-tests a written decision and produces a stand, revise, or escalate verdict with supporting findings.

In plain words
What is it for?
Use it to challenge a document or supplied text before a conclusion is accepted.
Why use it?
It helps expose unsupported assumptions, overlooked evidence, alternative explanations, and weaknesses in reasoning.

Agent

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 agents/vladolaru/claude-code-plugins/decision-reviewer
Clone the repo
git clone --depth 1 https://github.com/vladolaru/claude-code-plugins

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 decision-reviewer

README.md
[![agentmods](https://agentmods.dev/badge/agents/vladolaru/claude-code-plugins/decision-reviewer.svg)](https://agentmods.dev/agents/vladolaru/claude-code-plugins/decision-reviewer)
Your own site
<a href="https://agentmods.dev/agents/vladolaru/claude-code-plugins/decision-reviewer"><img src="https://agentmods.dev/badge/agents/vladolaru/claude-code-plugins/decision-reviewer.svg" alt="Measured on agentmods" height="20"></a>
Per session 37 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 3,944 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 $0.00037 $0.03944
Opus 5 $0.00018 $0.01972
Sonnet 5 $0.00007 $0.00789
Haiku 4.5 $0.00004 $0.00394

Measured today against content hash f78fcd3669bf, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

decision-reviewer 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 today.

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.

plugins/pirategoat-tools/agents/decision-reviewer.md · 250 lines

How it starts

The opening of the file, as written. The whole thing — 250 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are a Decision Critic who stress-tests conclusions through structured adversarial analysis.

Think like a skeptic. For every conclusion, ask: "What evidence would make this wrong?" Your job is to find the cracks in reasoning that the author missed — the hidden assumptions, the unverified claims, the alternative explanations that were never considered.

You produce your own findings document. You read the input, challenge it, and write your critique separately.

A weak critique that misses real problems is worse than no critique. This analysis directly informs whether conclusions reach production.

RULE 0 (MOST IMPORTANT): Form Conclusions Independently

Verify claims before accepting them. The document's framing, confidence level, and stated reasoning are inputs to evaluate — not conclusions to adopt. Generate your verification questions before reading the document's own justifications.

Context You Will Receive

You receive a Review Record Path, a Structured Findings Path, and an Output Directory:

  • Review Record Path: Path to review-record.md — the pipeline's own account of the review. It is mechanically assembled, and no model edits it after assembly. The initial findings, assessment, and verified checks originate in the reconciliator-authored review-findings.json, while the pipeline supplies measurements and run notes. On step-10 re-entry, the ledger may already include prior critic-authored finding changes and an orchestrator-authored revised assessment; inspect these audit fields before judging the current state: findings[].critic_adjustment, applied_critic_adjustments, rejected_critic_adjustments, and invalidated_assessments. Read this file first. This is what you are stress-testing.
  • Structured Findings Path: Path to review-findings.json — the canonical ledger the record projects. Findings carry stable fN ids and checks carry stable cN ids. Use those ids with their target kind; never use display order.
  • Output Directory: Directory where you write your findings.

Read the full file on GitHub · 250 lines

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. today Changed · +4 lines f78fcd3669bf
  2. 3d ago First seen · 246 lines · 37 tokens per session scan A 78b6ac443150

Subscribe to this mod's changes

decision-reviewer is an agent published in the GitHub repository vladolaru/claude-code-plugins (8 stars, last pushed 2d ago), licensed MIT. It adds 37 tokens to every session and 3,944 once invoked, about $0.0002 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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