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 agentmods add agents/vladolaru/claude-code-plugins/decision-reviewergit clone --depth 1 https://github.com/vladolaru/claude-code-pluginsWrote 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/agents/vladolaru/claude-code-plugins/decision-reviewer)<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>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 | $0.00037 | $0.03944 |
| Opus 5 | $0.00018 | $0.01972 |
| Sonnet 5 | $0.00007 | $0.00789 |
| Haiku 4.5 | $0.00004 | $0.00394 |
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.
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-authoredreview-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, andinvalidated_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 stablefNids and checks carry stablecNids. Use those ids with their target kind; never use display order. - Output Directory: Directory where you write your findings.
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.
- today Changed · +4 lines f78fcd3669bf
- 3d ago First seen · 246 lines · 37 tokens per session scan A 78b6ac443150
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.
Other agents, from other repositories
development
Read this document when setting up the repository, running the server, choosing verification commands, researching Home Assistant APIs, or orienting yourself in the architecture. Behavioral rules about when testing is required remain in AGENTS.md; test implementation details live in tests/AGENTS.md.
chronology-builder
Isolated worker that reads case documents iteratively and extracts sourced timeline events (date, neutral fact, mandatory document+locus provenance, undisputed/alleged/contested status, party attribution). Deduplicates and cross-references across documents and languages. Emits events.json for the legal-chronology…
extractor-items
Extract objects from document chunks (items, props, treasures, notable objects).
implement-taskplanner
You produce a .tasks.md implementation plan from a feature plan. Assume the implementation agents have zero codebase context and questionable taste. Document everything: exact files, complete code, test commands, expected output. Bite-sized TDD tasks. DRY. YAGNI.
doc-writer
Writes the summary document from the provided input file.
data-engineer
Expert in data pipelines, ETL/ELT, data modeling, analytics, and data infrastructure. Use for data processing, transformation, warehouse design, and analytics pipelines. Triggers on data pipeline, etl, elt, analytics, warehouse, pandas, spark, dbt, airflow, data model, csv, parquet, bigquery.