verdict-report

name	verdict-report
plugin	agent-finance
description	Produces a decision-support verdict from the filled Excel model + full research cache. Reads out_* DCF outputs, computes a probability-weighted fair-value range (Bull/Base/Bear), derives a 5-level signal (ACCUMULATE / ADD / HOLD / REDUCE / AVOID), and emits a structured action plan across three time horizons. Use last in the pipeline, after excel-financial-model has been validated and opened in Excel (formulas must be calculated). Never labels output as investment advice.
allowed-tools	Read, Write, Grep, Glob, Bash

verdict-report

Purpose

Bridge the gap between the research note (report-composer) and a decision. Produce a single verdict document that:

1. Audits the Excel model — reads every computed output cell, flags anomalies (TV% > 75%, checks != OK, WACC < g).
2. Derives a fair-value range — re-runs the model in-memory for each scenario to get Bull / Base / Bear value-per-share.
3. Emits a probability-weighted signal — uses the working-view weights from bull-bear-thesis.md to compute an expected value and compare to current price.
4. Produces a structured action plan — three horizons: 0–3 months, 3–12 months, 1–3 years — with specific milestones, tripwires, and kill conditions sourced from the research cache.

Hard rules

1. Never label output as investment advice. The disclaimer line is fixed and verbatim: "This report is decision-support research only. It is not investment advice."
2. Every figure cites source. Format: source: <filename or URL> as-of=<date>.
3. Refuse delivery if model not recalculated. If out_value_per_share reads None (formulas not evaluated), stop and instruct the user to open the file in Microsoft Excel once.
4. Refuse delivery if bull-bear-thesis missing. The working-view weights are required to compute the expected value.
5. Never invent scenario values. If a scenario VPS cannot be computed (e.g. bull/bear model inputs not available), mark it null with a gap: reason.
6. Reporting currency stated on every monetary figure.

Inputs

Input	Required	Source
ticker	✅	CLI --ticker
Filled Excel model (recalculated)	✅	${PROJECT_DIR}/output/agent-finance/models/<TICKER>_<DATE>.xlsx
bull-bear-thesis.md	✅	${PLUGIN_DATA}/companies/<TICKER>/bull-bear-thesis.md
valuation-multiples.json	✅	${PLUGIN_DATA}/companies/<TICKER>/valuation-multiples.json
model_inputs/values.json	✅	Needed to re-run bull/bear scenarios
model_inputs/sources.json	✅	Source annotations for re-run
model_inputs/peer_comps.json	optional	Needed for comps-implied VPS
risk-assessment.json	optional	Enriches tripwires section
earnings-analysis.json	optional	Next earnings date / catalyst calendar

Methodology

Step 1 — Read Excel outputs

Open the model with openpyxl data_only=True. Via cell_map.json read:

Logical name	Meaning
out_value_per_share	DCF Base value per share
out_upside	(VPS / current price) − 1
out_tv_pct_ev	Terminal value as % of EV — flag if > 0.75
out_wacc	WACC
out_enterprise_value	Enterprise value
out_equity_value	Equity value
out_comps_avg_vps	Comps-implied average value per share

If any of the above is None, abort with:

"Excel formulas not evaluated. Open the model in Microsoft Excel once to recalculate, then re-run verdict-report."

Step 2 — Derive Bull / Bear VPS

Re-invoke fill_model.py twice in-memory (no file save):

• Bull: override in_scenario = 1, re-read out_value_per_share
• Bear: override in_scenario = 3, re-read out_value_per_share

Fair-value range = [vps_bear, vps_base, vps_bull]

Step 3 — Parse working-view weights

Regex-parse bull-bear-thesis.md for the working-view line:

bull XX% / bear YY%

Extract bull_weight and bear_weight. Base weight = 1 - bull_weight - bear_weight.

Step 4 — Expected value & signal

ev = bull_weight × vps_bull + base_weight × vps_base + bear_weight × vps_bear

Signal thresholds (vs current price p):

Condition	Signal
ev > p × 1.20	ACCUMULATE
ev > p × 1.05	ADD
p × 0.95 ≤ ev ≤ p × 1.05	HOLD
ev < p × 0.95	REDUCE
ev < p × 0.80	AVOID

Step 5 — Catalyst calendar

Parse milestones from bull-bear-thesis.md (lines starting with "Milestone:"). Tag each as Q1/Q2/Q3/Q4 or year-based. Sort by proximity to today.

Step 6 — Action plan (3 horizons)

Combine milestones, tripwires from risk-assessment.json, and guidance check points from earnings-analysis.json into three horizon buckets.

Step 7 — Write outputs

1. ${PLUGIN_DATA}/companies/<TICKER>/model_inputs/verdict.json — machine-readable
2. ${PROJECT_DIR}/output/agent-finance/reports/<TICKER>_verdict_<DATE>.md — human report

Output schema

verdict.json

{
  "ticker": "DOL",
  "as_of": "2026-05-28",
  "reporting_currency": "CAD",
  "current_price": 174.95,
  "dcf_vps": {
    "bull": null,
    "base": 60.94,
    "bear": null
  },
  "comps_implied_vps": 26.78,
  "bull_weight": 0.60,
  "base_weight": 0.0,
  "bear_weight": 0.40,
  "expected_value_weighted": 0.0,
  "signal": "HOLD",
  "signal_conviction_pct": 60,
  "upside_base_pct": -65.1,
  "tv_pct_ev": 0.889,
  "tv_flag": true,
  "wacc": 0.0581,
  "gaps": [],
  "sources": [
    "cell_map.json",
    "bull-bear-thesis.md",
    "valuation-multiples.json"
  ]
}

verdict.md — fixed section order

# <TICKER> — <Company> — Decision-Support Verdict
> This report is decision-support research only. It is not investment advice.
> Reporting currency: <CCY>. As-of: <DATE>.

## 1. Model Audit
### 1.1 Validity
### 1.2 Computed outputs
### 1.3 Flags & anomalies

## 2. Fundamental Verdict
### 2.1 Quality of business
### 2.2 Growth trajectory
### 2.3 Financial health
### 2.4 Valuation (DCF range + comps)
### 2.5 SIGNAL
**[ACCUMULATE | ADD | HOLD | REDUCE | AVOID]**
Conviction: XX% | Assumption: [working-view label]

## 3. Action Plan
### 3.1 Immediate (0–3 months)
### 3.2 Medium-term (3–12 months)
### 3.3 Long-term (1–3 years)

## 4. Key Assumptions to Monitor (Tripwires)
[If X changes → signal flips to Y]

## Sources

Invocation

python "$CLAUDE_PLUGIN_ROOT/skills/verdict-report/build_verdict.py" \
  --ticker DOL \
  --model "$CLAUDE_PROJECT_DIR/output/agent-finance/models/DOL_2026-05-28.xlsx" \
  --cache "$CLAUDE_PLUGIN_DATA/companies/DOL" \
  --output "$CLAUDE_PROJECT_DIR/output/agent-finance/reports/DOL_verdict_2026-05-28.md"

Or via the orchestrator when the user asks for a "verdict", "moves", "recommendation", "buy or sell", or "que faire sur".

Pipeline position

company-profile → ... → bull-bear-thesis → model-input-builder →
excel-financial-model → report-composer → [verdict-report]

verdict-report is optional and on-demand. The pipeline delivers without it; it is triggered explicitly by user intent.

Dependencies

• excel-financial-model must have run and the xlsx must have been opened in Excel once (formulas evaluated).
• bull-bear-thesis must have produced bull-bear-thesis.md with a working-view section containing explicit bull/bear weights.
• fill_model.py is imported directly for in-memory scenario re-runs.