external-review

name	external-review
plugin	portfolio-advisor
description	Prepares a standalone adversarial review bundle of the investment thesis, DCF projections, and proposed weights for paste into an external LLM (Grok, ChatGPT, Gemini). Generates a targeted critical-analyst prompt, compiles all live thesis artifacts into a single Markdown payload, and writes it to temp/. Trigger on /external-review (primary), /bundle-thesis-review (alias), or "bundle for external review". Renamed 2026-08-28 from thesis-challenge-bundler -- see docs/architecture/skill-renames-2026-08-28.md. `/adversarial-review` is now a thin preset wrapper around this skill (full-thesis-challenge scope, no separate hardcoded prompt) rather than a second implementation.
allowed-tools	Bash, Read, Write

External Review Skill

(Renamed 2026-08-28 from "Thesis Challenge Bundler Skill" / thesis-challenge-bundler — promoted to the primary skill name since /external-review is the more intuitive trigger. Same implementation. See docs/architecture/skill-renames-2026-08-28.md. /bundle-thesis-review still works as an alias.)

Purpose

Packages the live portfolio thesis for adversarial external review — designed to be pasted into Grok, ChatGPT, or Gemini and sent with zero extra context. The external LLM receives a structured critical-analyst prompt first, then all relevant artifacts. It returns a scored critique, blind-spot flags, and challenge questions that Claude cannot self-generate objectively.

---

Core Workflow

Phase 1 — Scope the Review

Ask the user one question to set focus:

What should the external reviewer prioritize?

  1. Full thesis challenge  — conviction sizing, pillar balance, DCF conflicts, bias flags
  2. DCF assumptions only  — stress-test the fair values and scenario weights
  3. Concentration / risk  — position sizing, correlation, max drawdown exposure
  4. SA/DCF conflicts       — where smart-money conviction and DCF disagree
  5. Specific ticker(s)    — e.g. "just challenge INTC and CRWV"

Type a number, a custom focus, or "all" for the full review.

Wait for the user's choice. Default to option 1 if they say "all" or skip.

Also confirm output format:

Format: Markdown payload (paste into web UI) or ZIP (for offline/email)?
Default is Markdown. Type "zip" to get a ZIP instead.

---

Phase 2 — Generate the Prompt

Write a focused adversarial prompt to temp/thesis-challenge-{YYYY-MM-DD}/prompt.md.

Use the template in assets/templates/adversarial-prompt-template.md as the base, then inject the user's focus area from Phase 1. Pull live values:

# Get thesis name and version
python3 -c "
import json
from pathlib import Path
t = json.loads(Path('investment_screener/backend/data/theses/target-portfolio.json').read_text())
print(t['name'], '|', len(t['holdings']), 'holdings |', len(t.get('pillars',[])), 'pillars')
"

# Get current portfolio total value
python3 plugins/portfolio-advisor/scripts/validate_weights.py \
  --mode both \
  --portfolio investment_screener/backend/data/portfolio.json \
  --target investment_screener/backend/data/theses/target-portfolio.json

# Get actions summary (ACCUMULATE/TRIM/EXIT counts)
python3 plugins/portfolio-advisor/scripts/portfolio_action.py --all \
  --portfolio investment_screener/backend/data/portfolio.json \
  --target investment_screener/backend/data/theses/target-portfolio.json

The prompt must include:

• Persona: adversarial buy-side analyst, not a coach or cheerleader
• Rules of engagement: challenge every assumption, name every risk explicitly
• Focus area: injected from Phase 1
• Required output format: structured table + severity scores + top recommendations
• See template for full structure

---

Phase 3 — Build the Manifest

Create temp/thesis-challenge-{YYYY-MM-DD}/file-manifest.json.

The prompt.md MUST be first in the files array.

{
  "title": "Investment Thesis Challenge — {thesis_name} — {date}",
  "description": "Adversarial review bundle: investment thesis, DCF projections, proposed weights, and research reports. Focus: {focus_area}.",
  "excludes": ["*.pyc", "__pycache__", "node_modules", "*.png", "*.zip"],
  "files": [
    {
      "path": "temp/thesis-challenge-{YYYY-MM-DD}/prompt.md",
      "note": "PRIMARY INSTRUCTIONS — read this first before any other file"
    },
    {
      "path": "investment_screener/backend/data/theses/investment_thesis.md",
      "note": "Full investment thesis with version history, sub-strategies, holdings tables"
    },
    {
      "path": "investment_screener/backend/data/theses/target-portfolio.json"
       
    
    
       
       
    
    
       
       
    
  

If the user requested a specific-ticker focus (Phase 1 option 5), replace the full projections directory entry with individual files:

{ "path": "investment_screener/backend/data/projections/INTC.json", "note": "INTC DCF" },
{ "path": "investment_screener/backend/data/projections/CRWV.json", "note": "CRWV DCF" }

Optionally include research reports if they exist and the focus is a specific ticker:

{ "path": "investment_screener/backend/data/research", "note": "Deep-dive research reports" }

---

Phase 4 — Execute Bundle

# Create temp directory
mkdir -p temp/thesis-challenge-{YYYY-MM-DD}

# Run the canonical bundler
python3 plugins/portfolio-advisor/skills/external-review/scripts/bundle.py \
  --manifest temp/thesis-challenge-{YYYY-MM-DD}/file-manifest.json \
  --bundle   temp/thesis-challenge-{YYYY-MM-DD}/payload.md

For ZIP output:

python3 .agents/skills/red-team-bundler/scripts/bundle_zip.py \
  --manifest temp/thesis-challenge-{YYYY-MM-DD}/file-manifest.json \
  --bundle   temp/thesis-challenge-{YYYY-MM-DD}/payload.zip

---

Phase 5 — Handoff

╔══════════════════════════════════════════════════════════════════╗
║         THESIS CHALLENGE BUNDLE READY                           ║
╚══════════════════════════════════════════════════════════════════╝

✅ Payload: temp/thesis-challenge-{date}/payload.md
   ~{N} files  |  ~{tokens:,} tokens  |  Focus: {focus_area}

To use:
  1. Open the file: open temp/thesis-challenge-{date}/payload.md
  2. Select all → copy
  3. Paste into x.com/i/grok, chat.openai.com, or gemini.google.com
  4. Send — no additional prompt needed (instructions are embedded)

The reviewer will return:
  • Thesis challenge table (Pillar | Verdict | Score | Key Risk)
  • DCF assumption flags (any bear/base/bull weights that look off)
  • Blind spot / bias flags
  • Top 5 recommended changes with specific weight adjustments

Paste the response back here — I'll gate each recommendation through
the 8 hard gates before applying anything.

---

Hard Rules

1. Prompt always first in the manifest — the receiving LLM must read instructions before data
2. Never include portfolio.json (contains broker account details — use thesis JSON instead)
3. Never include .broker_cache, .env, or any credential files
4. Always confirm the manifest plan with the user before running bundle.py
5. Re-run generate_grok_prompt.py is NOT needed here — this skill bundles the full raw data, not a curated Grok prompt