adjudicate-review

adjudicate-review is a skill for Claude Code from pedrohcgs/claude-code-my-workflow. It costs 101 tokens per session (1,264 once invoked), scanned A, original, MIT.

A review-checking procedure for evaluating findings from reviewers, code tools, or other AI models against the actual source code.

In plain words
What is it for?
Classifying review comments, checking whether the reviewed files were complete, and applying only fixes supported by evidence.
Why use it?
It helps separate real defects from misunderstandings before changing correct work.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

Good fit Classifying review comments, checking whether the reviewed files were complete, and applying only fixes supported by evidence.

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Install with agentmods
npx agentmods add skills/pedrohcgs/claude-code-my-workflow/adjudicate-review
About the project

claude-code-my-workflow is a forkable setup for using Claude Code to produce and review academic papers, slides, data analyses, and replication packages. Researchers use its agents, skills, rules, hooks, and quality checks to coordinate these tasks and verify their results. The catalogue entries define the reusable workflow components for Claude Code.

pedrohcgs/claude-code-my-workflow · 1,570 stars · on GitHub · psantanna.com

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.

Any agent
npx skills add pedrohcgs/claude-code-my-workflow --skill adjudicate-review
Clone the repo
git clone --depth 1 https://github.com/pedrohcgs/claude-code-my-workflow

Made for: Claude Code.

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 adjudicate-review

README.md
[![agentmods](https://agentmods.dev/badge/skills/pedrohcgs/claude-code-my-workflow/adjudicate-review/github.svg)](https://agentmods.dev/skills/pedrohcgs/claude-code-my-workflow/adjudicate-review)
Your own site
<a href="https://agentmods.dev/skills/pedrohcgs/claude-code-my-workflow/adjudicate-review"><img src="https://agentmods.dev/badge/skills/pedrohcgs/claude-code-my-workflow/adjudicate-review/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.

agentmods 80×15 button for adjudicate-review

Your own site · 80×15
<a href="https://agentmods.dev/skills/pedrohcgs/claude-code-my-workflow/adjudicate-review"><img src="https://agentmods.dev/badge/skills/pedrohcgs/claude-code-my-workflow/adjudicate-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 101 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,264 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 $0.00101 $0.01264
Opus 5 $0.00051 $0.00632
Sonnet 5 $0.00020 $0.00253
Haiku 4.5 $0.00010 $0.00126

Measured 10d ago against content hash 44a53ca8e586, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

adjudicate-review 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 10d 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/adjudicate-review/SKILL.md · 71 lines

How it starts

The opening of the file, as written. The whole thing — 71 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Adjudicate the review; do not ingest it

A fluent, specific, line-numbered finding is not a verdict. It is a hypothesis about your work. Modern reviewers — especially models — produce objections that are confidently wrong at a meaningful rate, and some proposed fixes will introduce defects if applied. Your job is to convert findings into evidence-backed decisions.

Rule: never change correct work to satisfy a reviewer you have not checked.

0. First, was the reviewed artifact intact?

Before adjudicating anything, confirm the reviewer saw what you meant to send (see verify-artifact). Findings about missing references, truncated sections, or numbering that does not match your copy are usually artifacts of a bad upload/excerpt, not defects. Adjudicating those as real is how correct material gets broken.

1. Triage before verifying

Classify each finding:

  • Type: false statement | proof/logic gap | overclaim (headline exceeds what is established) | scope-or-consistency | exposition | artifact.
  • Severity: fatal | major | minor.
  • Which question it concerns — and do not let one clear another: reproducibility ≠ implementation fidelity ≠ statistical performance ≠ measurement validity ≠ identification/interpretation.
  • Held items: anything that re-litigates a decision the owner already made. Record, do not act.

2. Mechanical checks beat opinion

If a finding is computable, compute it: run the identity on a small adversarial case, grep for the symbol, resolve the cross-reference, execute the consuming code, count the occurrences. A two-minute check outranks any amount of reviewer confidence — in either direction. Several findings that look like taste turn out to be real, and several that look devastating evaporate.

3. Verify each finding against the actual source

Open the cited location. Ask:

  • Is the alleged text actually there, verbatim?
  • Is the missing hypothesis genuinely absent, or is it stated elsewhere — earlier in the paragraph, in the enclosing environment, imported via "the hypotheses of X", or in a governing standing assumption?
  • Does the failing case the reviewer describes actually arise under the stated conditions?

Read the full file on GitHub · 71 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. 10d ago First seen · 71 lines · 101 tokens per session scan A 44a53ca8e586

Subscribe to this mod's changes

adjudicate-review is a skill published in the GitHub repository pedrohcgs/claude-code-my-workflow (1,570 stars, last pushed 16d ago), licensed MIT. It adds 101 tokens to every session and 1,264 once invoked, about $0.0005 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 2026-08-30.

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