--- name: thesis_challenge_bundler 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 /bundle-thesis-review, /external-review, or "bundle for external review". allowed-tools: Bash, Read, Write --- # Thesis Challenge Bundler Skill ## 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: ```bash # 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.** ```json { "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", "note": "Live thesis JSON: all holdings, target weights, agentRationale, pillar structure" }, { "path": "investment_screener/backend/data/projections", "note": "DCF projections for all tickers: bear/base/bull scenarios, fair values, analyst logs" }, { "path": "PortfolioAnalysis/strategic-reviews", "note": "Latest portfolio review JSON: action recommendations, drift analysis" } ] } ``` If the user requested a specific-ticker focus (Phase 1 option 5), replace the full `projections` directory entry with individual files: ```json { "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: ```json { "path": "investment_screener/backend/data/research", "note": "Deep-dive research reports" } ``` --- ### Phase 4 — Execute Bundle ```bash # Create temp directory mkdir -p temp/thesis-challenge-{YYYY-MM-DD} # Run the canonical bundler python3 plugins/portfolio-advisor/skills/thesis-challenge-bundler/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: ```bash 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