tomevault-io Outer Review
Process an external AI review to extract actionable tasks. The human commissions the external review and saves the raw output; this skill processes it.
When to Use
- When
/outer-review [filepath]is invoked with a new outer review file - After a human has commissioned and saved an external AI analysis
Why External Review Matters
The Unfinishable Map's content is primarily generated and reviewed by Claude-based systems. This creates risk of:
- Confirmation bias: The reviewing system shares assumptions with the generating system
- Blind spot persistence: Gaps in Claude's knowledge propagate undetected
- Style homogenization: Content converges toward patterns Claude favors
- Coherence inflation: Arguments seem stronger than they are because the reviewer finds them compelling for the same reasons the writer did
External AI systems (GPT-4+, Gemini, etc.) have different training, different biases, and different blind spots. Their disagreements are informative even when wrong.
Prerequisites
The human must:
- Commission a review from an external AI system (ChatGPT, Gemini, etc.)
- Save the raw output to
obsidian/reviews/outer-review-YYYY-MM-DD-[system-slug].md - Add basic frontmatter (or leave for this skill to add)
Instructions
1. Read the Review File
The filepath is provided as an argument: /outer-review obsidian/reviews/outer-review-2026-01-15-site-chatgpt-5-2-pro.md
Read the file and assess its current state.
2. Add/Fix Frontmatter
Ensure the file has proper frontmatter:
---
title: "Outer Review - [System Name]"
created: YYYY-MM-DD
modified: YYYY-MM-DD
human_modified: null
ai_modified: YYYY-MM-DDTHH:MM:SS+00:00
draft: false
topics: []
concepts: []
related_articles:
- "[[project]]"
ai_contribution: 90
author: [Human who commissioned it]
ai_system: [external-system-id]
ai_generated_date: YYYY-MM-DD
last_curated: null
---
**Date**: YYYY-MM-DD
**Reviewer**: [System name and version]
**Type**: Outer review (external AI analysis)
## About This Review
An "outer review" is an analysis performed by an external AI system rather than the Claude-based workflow that generates most site content. This provides an independent perspective, reducing the risk of self-reinforcing blind spots.
[External system's review content follows...]
3. Normalize Links
External AI systems often produce footnote-style markdown links like ([Source][1]) with reference definitions at the end. Run the link normalization script:
uv run python scripts/normalize_review_links.py obsidian/reviews/outer-review-YYYY-MM-DD-*.md
This script:
- Removes self-references to unfinishablemap.org (just noise)
- Converts external footnote refs to inline links like
(See [Chalmers](url)) - Adds an "External Sources" section with properly labeled academic references
- Preserves inline links that are already well-formatted
Also convert any Map URLs to internal wikilinks:
https://unfinishablemap.org/tenets/→[[tenets]]https://unfinishablemap.org/topics/free-will/→[[free-will]]https://unfinishablemap.org/concepts/qualia/→[[qualia]]
4. Verify Claims Against Sources
External AI reviewers can also be wrong. Before accepting criticism, verify key claims by fetching the cited sources.
For each significant claim the reviewer makes about what an author says:
- Identify the source: Look for URLs in the External Sources section or inline citations
- Fetch the source: Use WebFetch to retrieve the actual paper/article
- Check the claim: Does the source actually say what the reviewer claims?
What to verify:
- Direct quotes or paraphrases of philosophers' positions
- Claims about what a paper "explicitly states" or "clearly shows"
- Attributions of specific arguments or frameworks to specific authors
- Claims that the Map misrepresents a source
Add verification notes to the review file:
After verifying, add a ## Verification Notes section to the review file documenting what you checked:
## Verification Notes
**Verified claims:**
- ✓ Reviewer correctly notes that Chalmers treats organizational invariance as contingent, not necessary (confirmed in "Absent Qualia" paper)
- ✓ Five constraints framework is indeed from Saad (2025), not Chalmers & McQueen
**Unverified claims:**
- ? Could not access Springer PDF to confirm Saad's exact formulation
**Disputed claims:**
- ✗ Reviewer says Chalmers "explicitly endorses" Many-Worlds—but the cited passage says "considerable sympathy," which is weaker than endorsement
This prevents blindly accepting external criticism that may itself be inaccurate.
5. Evaluate Review Quality
Read through the review and categorize findings, taking verification results into account:
High value findings (verified or plausible):
- Logical gaps not previously noticed
- Counterarguments not addressed
- Inconsistencies between pages
- Missing connections that should exist
- Novel framings of existing positions
- Misattributions confirmed by source checking
Lower value findings:
- Objections already addressed elsewhere
- Misunderstandings of the position
- Requests to adopt a different position entirely
- Style preferences that don't affect clarity
- Claims that failed verification (the reviewer was wrong)
6. Generate Tasks
For high-value findings, create tasks in obsidian/workflow/todo.md:
### P1: [Specific issue from outer review]
- **Type**: [research-topic | expand-topic | refine-draft | cross-review]
- **Notes**: From outer review YYYY-MM-DD. [Brief description of the issue and why it matters]
- **Review file**: `reviews/outer-review-YYYY-MM-DD-[slug].md`
- **Source**: outer-review
- **Generated**: YYYY-MM-DD
Important: Always include the Review file field with the path to the outer review. This allows the refine-draft skill to read the full context and verification notes when addressing the issue.
Priority guidance:
- P1: Logical errors, internal contradictions, unaddressed strong objections
- P2: Missing connections, expansion opportunities, clarity improvements
- P3: Style suggestions, minor enhancements
7. Log to Changelog
Prepend to obsidian/workflow/changelog.md (add immediately after frontmatter, before existing entries):
## [current time from prompt] - outer-review
- **Status**: Success
- **Reviewer**: [System name]
- **File**: [[filepath without .md extension]]
- **Claims verified**: [count]
- **High-value findings**: [count]
- **Tasks generated**: [count with priorities]
8. Commit
Create a git commit:
feat(auto): outer-review - process [system name] analysis
- Identified [N] actionable issues
- Generated [N] tasks (P1: X, P2: Y, P3: Z)
Evaluating Impact (After Tasks Complete)
After tasks from an outer review have been completed, evaluate the review's value:
- Count completed tasks: How many issues were addressed?
- Assess depth: Did the review surface deep insights or obvious issues?
- Track new content: What articles/sections resulted from the review?
- Note patterns: What kinds of issues does this external system catch that internal review misses?
This helps calibrate future review frequency and system selection.
Important
- This skill requires manual invocation with a filepath argument
- The human commissions and saves the external review; this skill processes it
- External systems may have different biases—their criticism isn't automatically correct
- The goal is diverse perspective, not consensus
- Some external criticism will be based on misunderstanding—that's expected
- Focus on actionable findings that improve content quality
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