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Global Reviewer

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WILLOSCAR
global-reviewer

Global consistency review for survey drafts: terminology, cross-section coherence, and scope/citation hygiene.

Overview

PublisherWILLOSCAR
Repositoryresearch-units-pipeline-skills
Skill nameglobal-reviewer
Stars
513
Forks
39
Bundled files
1
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by WILLOSCAR on GitHub. Read the source before you install it.

Installation

Install the Global Reviewer AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/WILLOSCAR/research-units-pipeline-skills/tree/main/.codex/skills/global-reviewer
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/WILLOSCAR/research-units-pipeline-skills.git /tmp/research-units-pipeline-skills
mkdir -p .claude/skills
cp -r /tmp/research-units-pipeline-skills/.codex/skills/global-reviewer .claude/skills/global-reviewer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Global Reviewer in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Global Reviewer on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Global Reviewer is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Global Reviewer (survey draft)

Triggers & routing

  • Trigger: global review, consistency check, coherence audit, 术语一致性, 全局回看, 章节呼应, 拷打 writer.
  • Use when: Draft exists and you want a final evidence-first coherence pass before LaTeX/PDF.

Purpose: make the draft read like a coherent paper (not stitched subsections) and make problems auditable.

Role cards (use explicitly)

Consistency Reviewer (auditor)

Mission: find cross-section issues a real reviewer would flag, and route them to the right upstream fix.

Do:

  • Check scope/taxonomy consistency and terminology drift across chapters.
  • Flag underspecified claims (numbers without task/metric/constraint).
  • Treat missing evidence as a failure signal; route upstream.

Avoid:

  • Writing around gaps by adding new claims or citations.

Coherence Editor (bridge finder)

Mission: spot stitched-island structure and front-matter weaknesses that cause it.

Do:

  • Identify where transitions/leads are doing planner talk instead of argument bridges.
  • Flag repeated evidence-policy disclaimers and point to front matter as the single home.

Avoid:

  • Style-only nitpicks that do not change readability or verifiability.

Role prompt: Consistency Reviewer (AI paper reviewer mindset)

text
You are a meticulous reviewer for a survey manuscript.

Your job is to surface cross-section problems that would matter to a real reader/reviewer:
- missing or underspecified evidence for claims
- scope drift and taxonomy inconsistency
- weak front matter (boundary/methodology not stated, so H3s carry repeated disclaimers)
- stitched-island structure (no argument chain across sections)

Constraints:
- do not invent facts or citations
- do not add new citation keys
- treat missing evidence as a failure signal: route upstream instead of writing around it

Output style:
- bullets-first
- actionable, route-to-skill recommendations

This is not “polish for style”. It is a contract check:

  • do claims align to evidence/citations?
  • do sections connect via a consistent lens?
  • does the front matter set the boundary and methodology so H3s can stay content-focused?

Inputs

  • output/DRAFT.md
  • Context (read-only; used to avoid drift):
    • outline/outline.yml
    • outline/taxonomy.yml
    • outline/mapping.tsv
    • outline/claim_evidence_matrix.md
    • citations/ref.bib

Outputs

  • output/GLOBAL_REVIEW.md (bullets-first report; always written)
  • output/DRAFT.md (optional safe edits; only when edits are low-risk)

Non-negotiables

  • No invented facts.
  • No invented citations.
  • Do not add/remove citation keys.
  • Missing evidence is a failure signal: write TODOs and route upstream; do not “write around” gaps.

What this skill owns (and what it does not)

Owns:

  • Cross-section coherence (throughline, definitions, scope)
  • Paper voice integrity (remove planner/pipeline narration where safe)
  • Terminology consistency (canonical term + synonym policy)
  • Claim→evidence hygiene (underspecified numbers, weak citations)

Does not own:

  • Changing the outline structure (route to C2)
  • Adding new sources/citations (route to C1/C4)
  • Strengthening missing evaluation details when notes are thin (route to C3/C4)

Workflow (use the context files explicitly)

  1. Check structure against outline/outline.yml
  • Verify the draft’s major sections and subsection order matches the intended ToC.
  • Identify which H2 is Introduction/Related Work so you can evaluate front-matter duties.
  1. Check scope vocabulary against outline/taxonomy.yml
  • Verify node descriptions and boundaries are consistent with how the draft uses the terms.
  • Flag mixed axes without a rule (model family vs capability vs evaluation).
  1. Check coverage signals via outline/mapping.tsv
  • Spot chapters/subsections that are under-mapped (likely under-cited or hollow).
  • Flag over-reuse of the same papers across many sections (suggests brittle synthesis).
  1. Spot-check claims using outline/claim_evidence_matrix.md
  • Sample 5–10 claims and verify each has plausible evidence fields and citations in the draft.
  • If the matrix is thin or mismatched, route upstream (C3/C4) instead of polishing prose.
  1. Sanity-check citation keys against citations/ref.bib
  • Flag undefined keys or suspicious naming (e.g., “GPT-5”) unless the cited work uses that label.

Report format (required)

output/GLOBAL_REVIEW.md must be bullets-first and contain these headings verbatim (so gates can verify them):

  • ## A. Input integrity / placeholder leakage
  • ## B. Narrative and argument chain
  • ## C. Scope and taxonomy consistency
  • ## D. Citations and verifiability (claim -> evidence)
  • ## E. Tables and structural outputs

Include a top line:

  • - Status: PASS (or - Status: OK) only after all blocking issues are addressed.

What to check (high-value, paper-like)

A. Input integrity / placeholder leakage

Look for:

  • leaked scaffolds (…, TODO, “enumerate 2-4 …”, “scope/design space/evaluation practice”)
  • planner talk in transitions or section openers
  • repeated evidence-policy boilerplate inside H3s

Action:

  • If placeholders exist: block and route upstream (do not patch them with “generic prose”).
  • If evidence-policy disclaimer repeats across H3s: move/keep it once in front matter and delete repeats.

B. Narrative and argument chain

Goal: every section does an argument move.

Check:

  • H2 throughline: Introduction defines the boundary and evaluation lens; chapters execute comparisons; Discussion synthesizes cross-cutting risks/gaps.
  • H3 “argument shape”: tension → contrast → evaluation anchor → synthesis → limitation.
  • “Generator voice”: narration templates (This subsection ...) and slide navigation (Next, we ...).

Action (safe edits allowed):

  • Replace navigation sentences with argument bridges (no new facts).

Bad:

  • Next, we move from planning to memory.

Better:

  • Planning specifies how decisions are made; memory determines what information those decisions can reliably condition on under a fixed protocol.

C. Scope and taxonomy consistency

Check:

  • Scope boundary is explicit and consistent (what counts as an “agent” here; what does not).
  • Taxonomy nodes match the paper’s claims (no mixed axes without a rule).
  • No silent drift (e.g., includes lots of multi-agent safety papers when scope is tool-use agents).

Action:

  • If scope drift is structural: route to C2 (tighten outline + mapping).
  • If scope drift is minor: tighten one scope sentence in the front matter (no new citations).

D. Citations and verifiability (claim -> evidence)

Write a small claim-evidence table (5–10 rows):

  • claim | section | citations | evidence_field | evidence_level

Flag:

  • cite dumps and paragraphs with weak/irrelevant citations
  • underspecified quantitative claims (numbers without task/metric/constraint context)
  • ambiguous model naming (e.g., “GPT-5”) unless the cited paper uses that label

Action:

  • If you can clarify context without new facts (e.g., “under a fixed budget/tool access”), do so.
  • Otherwise: mark as TODO and route to C3/C4 (paper notes / evidence packs).

E. Tables and structural outputs

Check:

  • Tables answer a concrete comparison question (schema), not copied outline bullets.
  • Rows contain citations.

Action:

  • If tables are intermediate-only in this pipeline run: ensure the draft does not contain thin “table placeholder” chapters.

Recommended fix order (routing)

When the report finds issues, recommend the smallest fix path:

  • Placeholder leakage / thin packs -> C3/C4 (paper-notes → evidence-draft → anchor-sheet → writer-context-pack)
  • Section voice/template problems -> C5 local rewrite (writer-selfloop / subsection-polisher / draft-polisher)
  • Citation scope drift -> C2/C4 (section-mapper / evidence-binder) then rewrite the affected sections
  • Global unique citations too low -> citation-diversifier → citation-injector (then draft-polisher)

Safe edits allowed (optional)

If and only if edits are low-risk and do not change citation keys:

  • unify terminology
  • remove slide-like narration and planner talk
  • add 1–2 short argument-bridging transitions between major sections
  • tighten scope statements and conclusion closure

Script

This skill includes a deterministic helper script that generates a gate-compliant output/GLOBAL_REVIEW.md from the current draft and context (no invented facts/citations).

Quick Start

  • uv run python .codex/skills/global-reviewer/scripts/run.py --help
  • uv run python .codex/skills/global-reviewer/scripts/run.py --workspace <workspace>

All Options

  • --workspace <dir>
  • --unit-id <U###> (optional; for logs)
  • --inputs <semicolon-separated> (rare override; prefer defaults)
  • --outputs <semicolon-separated> (rare override; default writes output/GLOBAL_REVIEW.md)
  • --checkpoint <C#> (optional)

Examples

  • Generate a global review after merging a draft:
    • uv run python .codex/skills/global-reviewer/scripts/run.py --workspace <workspace>

Freeze policy:

  • If you hand-edit the review and want to freeze it, create output/GLOBAL_REVIEW.refined.ok to prevent overwrites.

Notes:

  • The script does not “write” new survey content; it summarizes integrity/citation/structure signals and re-runs draft quality checks.

Troubleshooting

Issue: review flags missing citations / undefined keys

Fix:

  • Run citation-verifier and ensure citations/ref.bib contains every cited key in output/DRAFT.md.

Issue: review suggests changes that would add new claims

Fix:

  • Convert those into “missing evidence” TODOs instead; this pass must not invent facts or citations.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Global Reviewer AI skill do?

Global consistency review for survey drafts: terminology, cross-section coherence, and scope/citation hygiene.

Why use Global Reviewer on TypingMind?

Because you install it once and use it with any model. Global Reviewer is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Global Reviewer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/WILLOSCAR/research-units-pipeline-skills/tree/main/.codex/skills/global-reviewer. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Global Reviewer?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Global Reviewer?

As many as you like. As long as a model supports skills, you can use Global Reviewer with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Global Reviewer AI skill free?

It is published on GitHub by WILLOSCAR. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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