Jonathan KirkwoodandClaude Fable 5
b1d7aed9f4
- Deterministic scoring.py (quant 60 / qual 40 / flags -15, profitability heaviest) - Per-company JSON ledger with forecast-target chaining deck N-1 -> N - Single-shot sandbox agent with guided-JSON fallback ladder (no tool loop) - Portfolio dashboard with sparklines, KPI hit rates, BDEF category bars - 48 unit tests green; endpoints smoke-tested; npm check+build green Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
71 lines
3.9 KiB
Markdown
71 lines
3.9 KiB
Markdown
# Boardroom Map
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Drop portfolio-company board decks in and have a **panel of local LLMs** running
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on your DGX Sparks grade them against the **BDEF v1.1 framework** (Girdley +
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Munger/Buffett). A deterministic scorer turns the panel's grades into a 0–100
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composite and appends it to each company's **running scorecard ledger**; the web
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dashboard shows the trends. There is **no frontier model and no cloud key** — in
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the default air-gapped mode your confidential decks never leave your hardware.
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Boardroom Map is a *control plane*: nothing serves or runs on your StartOS box
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(it only SSHes to the Sparks and extracts deck text on CPU).
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## Setup (run the Actions in order)
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1. **Configure Sparks** — SSH host/user/key for your head Spark (and optionally a
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second), plus the work directory and image tags. Then **Test Spark Connection**.
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2. **Configure Models** — the catalog of local models to serve (alias → HF id →
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which Spark → port), and serving knobs. For air-gapped runs every model must be
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on the **head Spark** and present in its HF cache.
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3. **Configure Graders** — the panel: one entry per grader, each a model + a
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persona (the lens it grades through — e.g. a Munger inversion skeptic, a
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Girdley operator, a skeptical CFO) + an optional temperature.
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4. **Configure Grading** — the BDEF rubric override (empty = the built-in
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BDEF v1.1), the **Network Mode** (air-gapped vs local-services), the
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extractor + adjudicator models, deck retention, and the scoring weights.
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5. **Configure Companies** — one entry per portfolio company: its inbox **slug**,
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display name, KPI aliases, and **pinned KPI targets**. Pin the profitability
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thresholds especially — profitability carries the heaviest weight.
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## Grading decks
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1. Drop each company's deck into its inbox folder, e.g.
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`inbox/acme-widgets/2026-Q2-deck.pdf` (PDF / DOCX / TXT / MD), via the
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**Web UI** or the service's `inbox` directory.
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2. Run **Grade Decks** (or enable *auto-grade on drop*).
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3. Watch the activity log. The service extracts text locally, serves the needed
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models on the Sparks **in waves** (so a panel can span more models than fit in
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GPU memory at once), runs the structured KPI extractor, then each grader, then
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the adjudicator, and finally computes the composite and updates the company's
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ledger. Read the results on the dashboard or via **View Latest Scorecard**.
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## How the score works
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The composite is **0–100 = quantitative 60 + qualitative 40 − red flags (max 15)**:
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- **Quant 60** — profitability KPI attainment **30** (the heaviest single slice),
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other measurable KPIs **20**, and **forecast integrity 10**: deck N's actuals
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are chained against what deck N−1 promised, so sandbagging and quietly moved
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goalposts cost points.
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- **Qual 40** — eight BDEF categories (A–H), up to 5 points each, scored by the
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panel with evidence quotes (thin evidence scales the score down).
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- **Red flags** — up to **−15**; silently dropped KPIs are flagged automatically,
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and flags raised by only one grader are damped.
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Pinned targets from **Configure Companies** are graded every quarter whether or
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not the deck mentions them — a deck cannot improve its score by going quiet.
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## Network modes
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- **Air-gapped (default):** grader containers join an `--internal` Docker
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network — they can reach only the on-Spark model proxy, with zero internet
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egress. Models are served from a pre-pulled HF cache. All models must be on the
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head Spark. Strongest confidentiality.
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- **Local services:** graders may also reach LAN services (e.g. SearXNG) and the
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second Spark. This network has egress unless you firewall it — use only when you
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accept that graders can reach the network.
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The original decks are extracted to plain text on the StartOS box; only that
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text is shipped to the Sparks, and it is wiped from the Sparks after the job (the
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scorecards and ledgers are kept on your StartOS box).
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