stock-analysis-lead

name	stock-analysis-lead
description	Orchestrate a comprehensive US-stock investment analysis by classifying sector archetype, fetching SEC filings, dispatching six vertical equity-research skills (business model, earnings quality, balance sheet, management, industry & competition, peer comparison) as parallel agents, then synthesizing a buy/hold/sell verdict with Bull/Base/Bear target-price ranges using sector-aware thresholds and calibrated probability assignment. Persists each verdict to a JSON-Lines log so subsequent analyses of the same ticker reckon with the prior view. Use when the user asks "should I buy <ticker>", "analyze <company>", "is <ticker> a good buy now", "美股目标价 / 估值 / 投资标的分析", "evaluate <ticker>", or any full US-equity workup. NOT for trading signals, technical analysis, options, crypto, ETFs, or macro/sector calls — this skill is single-stock fundamental analysis only. NOT for A-shares, HK-shares, or any non-US listing — the entire framework is keyed to SEC filings and US GAAP and will refuse non-US tickers.
allowed-tools	Read, Grep, Glob, WebSearch, WebFetch, Bash, Agent

Stock Analysis Lead — Orchestrator

Purpose

You are the orchestrator for a Multi-Agent US-equity research workflow. Your job is to:

1. Identify the ticker and validate it is a US-listed equity
2. Review prior verdict on this ticker (if it exists) before producing a new one
3. Classify the company into a sector archetype (SaaS / Hyperscaler / Mature Cash Cow / Capital-Intensive / Cyclical / Financial / REIT) — this determines the threshold set for the Good-Company checklist and valuation norms
4. Fetch the standard data package (10-K, 10-Q, recent earnings call, current price, peer set, historical financials, earnings-revision momentum)
5. Triage which vertical worker skills to dispatch
6. Launch selected workers in parallel in a single Agent batch (six workers including the Peer Comparison Worker)
7. Consolidate worker Findings and synthesize a final verdict using the "Good Company × Good Price" framework with calibrated probability assignment
8. Append the verdict to the persistent log so future analyses on this ticker reckon with this one

Critical rule: You only fetch, triage, dispatch, and synthesize. You do NOT analyze fundamentals yourself — the workers do that. Drawing your own conclusions in parallel with the workers re-introduces the attention-dilution problem this architecture exists to solve.

Quick Reference

Step	What	Section
1	Identify ticker; validate US listing	Step 1
1.5	Select depth (Lite / Standard / Strict)	Step 1.5
1.5b	Review prior verdict on this ticker (new)	Step 1.5b
1.5c	Classify sector archetype (new)	Step 1.5c
2	Fetch the data package (incl. earnings revision)	Step 2
3	Triage which workers to dispatch	Step 3
4	Dispatch 6 workers in parallel	Step 4
5	Synthesize verdict + append to verdict log	Step 5
—	Output format	Output Format
—	Consolidation rules	Consolidation Rules

When To Use

• User asks for full investment analysis of a US-listed equity ("analyze NVDA", "should I buy MSFT", "评估 AAPL", "is GOOG overvalued").
• User invokes the skill explicitly.
• The question is fundamental-analysis-oriented (business quality, financial health, valuation, recommendation).

When NOT To Use

• Technical analysis / chart patterns / trading signals — different methodology; route to a future technical-analysis skill or decline.
• Options analysis — different instrument; this skill does not consider IV, Greeks, or strategies.
• Crypto, FX, commodities — completely different asset class.
• Sector ETFs / mutual funds / index funds — fundamental-analysis lens does not apply.
• Macro / sector calls — single-stock framework cannot answer "is tech a buy".
• Non-US listings — A-shares, HK-shares, foreign private issuers (20-F filers). Different disclosure standard; refuse explicitly.

Workflow

Step 1: Scope Identification

Extract the ticker from the user's prompt. Validate:

1. Is it a recognizable US listing (NYSE / Nasdaq / NYSE American symbol)?
2. Is it a domestic issuer (10-K filer) or a foreign private issuer (20-F filer)?
   • 20-F-only filers: out of scope. Reject with explanation.
3. Is it an ADR for a non-US company with primary listing elsewhere?
   • If the company files 10-K/10-Q as a US-domestic issuer (e.g., select dual-listed): in scope.
   • If filing 20-F only: out of scope.
4. Is it an A-share (3-digit / 6-digit Chinese symbol) or HK ticker (4-digit)?
   • Out of scope. Refuse with the standard message:

   This skill analyzes US-listed equities only (SEC 10-K/10-Q filers).
   A-shares, HK-shares, and 20-F-only foreign private issuers are out of scope.
   

5. Identify the question type:
   • Full workup: "analyze X", "should I buy X" → all 5 workers
   • Valuation check: "is X expensive", "what's a fair price for X" → 4 fundamentals workers + heavy valuation synthesis
   • Specific concern: "is X's balance sheet OK" → target the relevant worker; other workers still run for context but with lighter weighting

Step 1.5: Select Depth

Depth	When	Workers dispatched	Synthesis effort
Lite	Quick gut-check; user mentions a small position or hallway question	All 6 workers but Industry runs in Lite-Industry mode (moat name + share trend only, no full Porter); Peer Comparison runs General panel only	Minimal — Bull/Base/Bear ranges from multiples only
Standard	Default	All 6 workers, full mode	Full Good-Company score (sector-aware) + 4-method valuation + Bull/Base/Bear with calibrated probabilities
Strict	User explicitly asks for deep analysis OR position size > 5% of portfolio implied OR "long-term" / "core holding" mentioned	All 6 workers + extended cross-check between workers	Full + reverse-DCF stress test + cognitive-bias audit + 2nd-archetype check for multi-segment companies

Default to Standard unless the prompt explicitly signals Lite or Strict.

Important: Even Lite mode runs all 6 workers (in lighter form). The previous behavior of completely skipping Industry in Lite was wrong — Industry determines 2 Good-Company checklist items (moat + TAM), and skipping it distorted the score. Lite now runs a focused Industry pass (moat type + share-trend one-liner) instead.

Step 1.5b: Past Verdict Review

Before proceeding to data acquisition, check the verdict log for any prior analysis of this ticker:

LOG_FILE=~/.claude/projects/-Users-john-awesome-skills/memory/stock-analysis-verdicts.jsonl
test -f "$LOG_FILE" && grep "\"ticker\": \"<TICKER>\"" "$LOG_FILE" | tail -3 | jq

If a prior verdict exists:

• Read the most recent (and up to 3 prior) entries
• Compute time elapsed since prior verdict
• For each Bull/Bear assumption listed in the prior entry, mark whether it has been:
  • VALIDATED (assumption played out as expected)
  • INVALIDATED (assumption disproved by events)
  • STILL OPEN (insufficient time/data)
• Compute: current price vs the prior Bull/Base/Bear trajectory — which scenario is the current price closest to?
• Surface this as a "Prior Verdict Tracking" subsection in the final report

The new verdict must explicitly reckon with the prior one. If you arrive at a different verdict, state why — what new information, what assumption changes, what time elapsed.

If no prior verdict exists: skip this step.

See references/verdict-log-protocol.md for the full format and review procedure.

Step 1.5c: Sector Archetype Classification

Before applying the Good-Company checklist or valuation norms, classify the company into exactly one archetype:

• High-Growth SaaS / Software
• Mature Cash Cow / Consumer Staples
• Hyperscaler / Mega-Cap Tech Platform
• Capital-Intensive Industrial / Infrastructure / Utility
• Cyclical (materials, auto, semiconductors, travel)
• Bank / Insurance / Asset Manager (Financials)
• REIT
• Payment Network / Card Scheme / Transaction Processor (MA, V, AXP-network, PYPL, processors)

Classification rules (see references/sector-archetypes.md for full taxonomy):

• Look at revenue mix, gross margin, capex intensity, and dominant business model.
• If a company is materially multi-archetype (e.g., AMZN = Hyperscaler + Retail), pick the dominant by revenue (>60%) but flag the secondary archetype and run a secondary score on that segment in Strict mode.

Apply only the threshold set from the matching archetype in the Good-Company checklist (Step 5b) and the valuation norms (Step 5c). Also load the archetype's Analytical Addendum and pass its mandatory_line_items + specialist_checks into the worker dispatch (Step 4) as REQUIRED checks — this turns generalist coverage into sector depth (e.g., payment network → BUS-11 client incentives + EQ-15 VAS mix-shift become mandatory, and the valuation model uses a revenue_bridge). The prior version's SaaS-default thresholds (rev growth ≥15%, NRR>115%, Net Debt/EBITDA <2) systematically under-rated mature cash cows, utilities, banks, REITs, and cyclicals — that bias is now fixed by sector-aware scoring.

Document the archetype choice in the final report's Execution Status section.

Optionality test (run right after classification). Compute the visible/established business's intrinsic value (a driver-model DCF on only the segments that earn revenue today) ÷ market cap. If it is below ~30–40%, the stock is option-dominated: attach the Optionality Overlay (references/sector-archetypes.md). The overlay does NOT change the base archetype — it changes the valuation structure: Step 5c builds a sum-of-the-parts instead of relying on a single-entity target, reverse-DCF is demoted to sizing the option premium (never a mechanical downgrade), Step 5d sets probabilities from the venture tree, and the segment-materiality rule forces every material engine (e.g. a high-margin, fast-growing energy/storage segment) to be modeled, not mentioned. Tesla is the canonical case (visible ≈ 9% of price).

Step 2: Data Acquisition

Fetch the standard data package before dispatching workers. Workers receive paths to local copies, not URLs.

FIRST-HAND DATA RULE (mandatory). Before falling back to any aggregator (stockanalysis.com, macrotrends, etc.), pull structured financials directly from SEC EDGAR's XBRL companyfacts API with the bundled pipeline:

python3 scripts/finlib/edgar.py fetch --ticker <TICKER> --out $TMPDIR/stock-analysis-<ticker>/financials.json
# offline / cached companyfacts: python3 scripts/finlib/edgar.py parse --facts <companyfacts.json> --out financials.json

financials.json carries, for every reported figure, its source XBRL tag + period + form (revenue, operating income, OCF, capex, D&A, SBC, accounts receivable + allowance, deferred revenue, RPO, shares, debt, cash). Add it to the manifest as financials. Aggregator sites are fallback only; any reported financial number that came from a search snippet (not from financials.json or the filing) must be tagged second-hand in the manifest. Concepts EDGAR does not expose as a flat value — notably segment operating income (dimensional) — land in financials.json["gaps"]; workers must then read the relevant 10-K footnote directly, never infer them from consolidated totals.

Artifact	Primary source	Tool	Required for	Fallback
Latest 10-K	SEC EDGAR	firecrawl-download or WebFetch	All workers	Company IR page
Latest 10-Q	SEC EDGAR	firecrawl-download or WebFetch	All workers	—
Most recent earnings call transcript	Seeking Alpha (free tier)	firecrawl-search → download	Management worker	Company IR webcast (less reliable)
Current price + basic multiples (TTM PE, P/S, EV/EBITDA, market cap)	stockanalysis.com	WebFetch	Synthesis	Yahoo Finance via scrape
10-year financial history (revenue, OCF, FCF, gross margin, etc.)	stockanalysis.com	WebFetch	Earnings-quality + balance-sheet workers	Macrotrends
Peer list (2–4 closest comparables)	10-K Item 1 "Competition" + WebSearch	inline + WebSearch	Industry worker	manual selection
Analyst consensus (revenue/EPS estimates)	Yahoo Finance "Analysis" tab	scrape	Synthesis (cognitive-bias check)	Seeking Alpha
Earnings-revision momentum (30/60/90 day NTM EPS revision direction + magnitude)	Yahoo Finance "Analysis" tab; sell-side aggregators	scrape	Synthesis Step 5d (probability adjustment)	Manual count of recent target-price revisions
12-month insider Form 4 activity	SEC EDGAR	WebFetch	Management worker	—
DEF 14A (latest proxy)	SEC EDGAR	firecrawl-download	Management worker	—

Write all fetched artifacts to a scratch directory (e.g., $TMPDIR/stock-analysis-<ticker>/). Emit a data-manifest.json:

{
  "ticker": "MSFT",
  "fetched_at": "2026-05-15T03:00:00Z",
  "filings": {
    "10K": {"path": "...", "fiscal_year": 2024, "filing_date": "2025-07-30"},
    "10Q": {"path": "...", "quarter": "Q1 FY26", "filing_date": "2025-10-30"},
    "DEF14A": {"path": "...", "filing_date": "2025-10-15"}
  },
  "transcripts": [{"path": "...", "quarter": "Q1 FY26", "date": "2025-10-29"}],
  "price_data": {"current": 412.30, "ttm_pe": 32.5, "fwd_pe": 28.1, "ev_ebitda": 22.4, "market_cap_b": 3060},
  "historicals": {"path": "...", "years": 10},
  "peers": ["GOOGL", "AAPL", "AMZN"],
  "insider_activity": {"path": "..."},
  "missing": []
}

If any artifact is missing, list it in missing with reason. Workers receive the manifest; they self-skip when their required data is in missing.

Step 3: Triage

Decide which workers to dispatch using a 4-phase pass.

Phase A: Always-on (all depths — Lite runs lighter mode but no full skip)

• stock-business-reviewer
• stock-earnings-quality-reviewer
• stock-balance-sheet-reviewer
• stock-management-reviewer
• stock-industry-reviewer (Lite mode: moat name + share-trend only; Standard/Strict: full Porter analysis)
• stock-peer-comparison-reviewer (NEW) — independent 12-item ratio benchmark vs 2-4 peers; provides cross-validation for moat and market-share claims (Lite mode: General panel only; Standard/Strict: General + archetype-specific panel)

Phase B: Data-availability gating

Cross-check the manifest. If a worker's required data is in missing:

• 10-K missing → skip all 6 workers; report data-unavailable failure
• DEF 14A missing → mark management worker as DEGRADED (no proxy) but still dispatch (transcripts still useful)
• 10-year history missing → mark earnings-quality worker as DEGRADED (trend analysis impacted) but dispatch
• Transcripts missing → mark management worker as DEGRADED (no call transcript)
• Peer list missing → mark both industry AND peer-comparison workers as DEGRADED (no peers) and use sector-archetype-only norms
• Earnings revision data missing → synthesis Step 5d proceeds without momentum adjustment; note degradation

Phase C: Question-driven priority

If the user's question targets a specific dimension (e.g., "is the balance sheet safe"):

• Dispatch all 5 as usual
• In Step 5 synthesis, weight the targeted dimension's Findings more heavily; show the user explicitly why other dimensions matter for the full picture

Phase D: Sanity check — fabricated tickers

If you cannot find the ticker in SEC EDGAR's company database, do not synthesize. Return:

Ticker not found in SEC EDGAR. Verify the symbol and re-run.

Step 4: Dispatch

Critical: Launch all selected workers in a single Agent tool batch (multiple Agent invocations in one message). Sequential dispatch breaks the parallelism that justifies the architecture.

Each Agent invocation receives a prompt with:

You are <worker-name>. Load your skill via the skills: field.
Ticker: <TICKER>
Depth: <Lite|Standard|Strict>
Archetype: <archetype> — apply its sector-archetypes.md Analytical Addendum (mandatory_line_items + specialist_checks) as REQUIRED, not optional.
Manifest: <path to data-manifest.json>
Filings available at: <scratch-dir>

Return only structured Findings per your skill's Output Format.
Do NOT recommend buy/hold/sell — the orchestrator synthesizes the verdict.

Workers run in parallel. Wait for all to return before proceeding.

Step 5: Synthesis

This is the orchestrator's most important step. Do not delegate it to a worker.

5a. Consolidate Worker Findings

• Collect Findings from all 6 workers.
• Deduplicate by Finding meaning (not just by ID). Cross-worker overlap is fine and often correct.
• Assign unified IDs INV-NNN while preserving worker prefixes (BUS, EQ, BS, MGT, IND, P for peer-comparison).
• Sort by severity High → Medium → Low, then by worker order.
• Apply volume caps: Lite = 8 max Findings, Standard = 15 max, Strict = 25 max. Truncate by suppressing Low.
• Cross-reference Peer Comparison panel against Industry's moat / share claims: if peer-comparison P-01 (revenue growth) is mid-panel but Industry says "gaining share," surface the conflict in the synthesis section. The peer comparison is the independent referee.

5b. Good-Company Score — sector-aware (from references/good-company-checklist.md + references/sector-archetypes.md)

Apply the threshold set from the archetype chosen in Step 1.5c. The 10-item checklist structure is the same, but the actual thresholds (numbers and direction) vary per archetype:

1. Revenue growth — threshold per archetype (SaaS ≥15%, Mature ≥4%, Utility ≥3%, REIT FFO/share ≥4%, etc.)
2. Gross margin level — threshold per archetype's sector norm
3. Operating leverage — direction acceptable per archetype (Mature: stable OK; SaaS: positive required)
4. FCF or alternative cash metric — per archetype (SaaS: FCF growth; Mature: FCF/Revenue + coverage; REIT: AFFO; Bank: ROTCE)
5. Cohort/retention or coverage metric — per archetype (SaaS: NRR; Mature: dividend coverage; Bank: loan-loss coverage; REIT: occupancy)
6. Market share — same direction (rising or stable), sector-specific norm
7. Balance sheet leverage — Net Debt/EBITDA threshold per archetype (SaaS ≤1.5×; Mature ≤3×; Utility ≤5×; REIT ≤6×; Bank: CET1 cushion instead)
8. Management quality — qualitative across archetypes
9. Identifiable moat — 7-type taxonomy applies to all archetypes
10. TAM — sector-specific growth threshold

Scoring: PASS = 7+, "good company"; PASS = 9+, "high quality". Same scoring rubric across archetypes — only the underlying thresholds change.

Required output: document the archetype + threshold-set used so the score is auditable. Without this, the score is uninterpretable.

5c. Valuation (from references/valuation-methods.md + sector-archetype norms)

Build a driver model, don't hand-pick three P/Es. Author a model.json (drivers each tagged source: data|assumption; anchors — revenue_0, op_margin_start, da_pct, capex_pct — fed from financials.json) and run python3 scripts/finlib/valuation.py run --model model.json. It outputs DCF, reverse-DCF (what growth the current price implies), the three scenarios as one model's parameter sets, a derived terminal multiple (Gordon (1+g)/(WACC−g), never typed), and a sensitivity table. The report MUST disclose WACC, terminal g, reverse-DCF implied growth, and the sensitivity table (valuation-methods.md § Mandatory disclosure), and reconcile DCF intrinsic value against the scenario target when they diverge >10% — a point estimate without these is illustrative, not analytical. If any anchor is an assumption it prints grounding: LOW. See references/valuation-methods.md.

Option-dominated stocks (Optionality Overlay attached) — build a sum-of-the-parts (PRIMARY lens). A single-entity scenario target cannot value a company whose price is mostly unproven future businesses. Author a sotp.json and run python3 scripts/finlib/sotp.py run --model sotp.json (valuation-methods.md Method 5): established engines as fixed/multiple legs (a material energy/storage segment modeled GWh×$/kWh×margin, not a one-line positive — segment-materiality rule), each venture as an option leg (TAM→share→take-rate→margin→exit × P(success)/time) with low/base/high ranges. The engine refuses un-sourced drivers and refuses a point-estimate option leg; the output is a per-share distribution plus option_share_of_value (modeled) and market_implied_option_share (~the % of price the market calls option) — never a single target. The gap between those two is the priced-for-perfection signal. Here reverse-DCF on the visible business only sizes the option premium; it is tautological that the visible business cannot justify the price, so it MUST NOT trigger the "implied growth infeasible → downgrade" reconciliation row below.

Run four methods in order. Use archetype-specific valuation norms (P/E ranges, EV/FCF ranges, dividend yield benchmarks) from references/sector-archetypes.md — NOT generic SaaS norms:

1. Multiples: PE, PEG, P/S, EV/EBITDA, EV/FCF (or archetype-specific alternatives like P/TBV for banks, P/FFO for REITs). For each:

   • Compare to historical percentile (5-year and 10-year)
   • Compare to peer median (using Peer Comparison Worker output for this)
   • Compare to archetype-norm range (NOT generic SaaS norm)

2. Reverse-DCF: derive the growth-and-margin assumptions implied by the current price. Use archetype-appropriate WACC (utilities 7-8%; banks 9-11% cost of equity; SaaS 9-10%) and archetype-appropriate terminal multiple. Flag if implied assumptions exceed historical track record or sector feasibility.

3. DCF: only as a sanity check. Source doc warns DCF has too much manipulation surface. For Mature Cash Cow archetype, perpetuity DCF (Gordon model) can be primary; for SaaS, never primary.

4. Scenarios: Bull/Base/Bear (see 5d).

Cross-Method Reconciliation — strict rules (replaces the previous "lean toward scenarios" fudge):

When the four methods produce conflicting signals, apply the following priority order:

Disagreement Pattern	Tiebreaker
Multiples say "cheap" but Scenarios say "Watch/Hold"	Scenarios win — multiples reflect past; scenarios reflect forward fundamentals
Multiples say "expensive" but Scenarios say "Buy"	Scenarios win IF assumptions feasible; otherwise multiples win (overpaying for growth that may not arrive)
Reverse-DCF says "market implies impossible growth"	Treat as Bear-confirming; reduce verdict tier by one — EXCEPT when the Optionality Overlay is attached: then this is tautological (visible business never justifies an option stock's price), so it only sizes the premium and must NOT cut the tier
Reverse-DCF says "market implies modest growth" + Scenarios say "Buy"	Both agree — verdict is well-supported
Peer Comparison says "expensive vs peers" + Multiples say "cheap vs history"	Investigate why: company-specific issue or sector-wide derating?

If you cannot reconcile, the default is Hold until the conflict is resolved — explicit "I don't know" is more honest than fudging.

5d. Build Bull/Base/Bear (from references/scenario-framework.md + references/scenario-probability-calibration.md)

For each scenario, specify:

• 5-year revenue (or FFO/PPNR/etc per archetype) CAGR
• Terminal operating margin
• Terminal multiple (archetype-appropriate)
• Probability weight assigned via the calibrated framework (NOT the old ad-hoc Good-Company → weight mapping)
• Computed target price

Probability assignment must show the work (see references/scenario-probability-calibration.md):

• Anchor 1: archetype base rate (e.g., Hyperscaler Bull base rate 25-35%)
• Anchor 2: independent-assumption adjustment (each independent positive assumption above 1 reduces Bull by ~5pp)
• Anchor 3: mandatory disconfirming-evidence citation (for Bull ≥25%, cite specific real-world bear thesis and explain weight)

Option-dominated stocks — derive Bull/Bear from the venture tree, not pp-subtraction (see references/scenario-probability-calibration.md): an independent P(success) per venture (the SAME number used in the SOTP option legs); Bear = Π(1−Pᵢ) (visible-only); Bull = the value-dominant venture(s) succeeding; Base = residual. This replaces the "base rate −5pp −2pp" arithmetic for these names.

Earnings-revision momentum adjustment (see references/earnings-revision-momentum.md):

• Strong Positive momentum → Bull +5pp, Bear -5pp
• Mild Positive → Bull +2pp, Bear -2pp
• Mild Negative → Bull -2pp, Bear +2pp
• Strong Negative → Bull -5pp, Bear +5pp

Compute:

• Weighted expected price = Σ (scenario_price × probability)
• Weighted expected return = (weighted expected price − current price) / current price
• Bear-to-current ratio = bear price / current price

Decision rule (unchanged):

• Weighted return ≥ 50% AND Bear/Current ≥ 0.75 → Strong Buy odds
• Weighted return ≥ 30% AND Bear/Current ≥ 0.70 → Buy odds
• Weighted return ≥ 15% AND Bear/Current ≥ 0.80 → Watch odds (set an alert price)
• Otherwise → Hold or Trim/Sell if currently held

Note: the hard thresholds (50%/0.75, 30%/0.70, 15%/0.80) are themselves uncalibrated. They are the framework's current best guess; the verdict log enables future recalibration based on observed outcome distribution.

5d-bis. 口径 / Calculation Lint Gate (MANDATORY — deterministic)

Do not trust LLM arithmetic or labels. Emit every multiple/ratio/margin to metrics.json (per entry: id, claimed_label, stated_value, inputs, tags{basis,period,period_count}, is_trend_claim), then run python3 scripts/finlib/lint.py --metrics $TMPDIR/stock-analysis-<ticker>/metrics.json. It (via scripts/finlib/ratios.py) catches: label vs formula (an ev_* multiple without total_debt+cash_and_sti is the P/FCF-as-EV/FCF error; EV = mktcap+debt−cash) · internal consistency (stated vs recomputed-from-inputs >2% → catches "capex/rev 50%" when inputs give 67%) · single-period (period_count<4 on a leverage/trend claim → FAIL; use ≥4Q or TTM) · 口径 tags (mixing GAAP/non-GAAP/core, TTM/FY → WARN). Any FAIL blocks the report — fix it, or tag the value second-hand/unverified and lower the dependent conclusion's confidence. Feed inputs from financials.json so the recompute is meaningful.

5e. Cognitive-Bias Self-Check (from references/cognitive-bias-gates.md)

Run 6 binary checks; document each as PASS or FLAG:

1. Anchoring: Am I anchoring on past prices rather than intrinsic value? FLAG if "cheap" justification rests on historical price decline.
2. Story bias: Have I quantified the narrative (revenue, time horizon, probability)? FLAG if "AI-beneficiary" or similar appears without numbers.
3. Confirmation: Did at least one worker raise a substantive counter-thesis? FLAG if all workers agree without dissent.
4. Overconfidence: Am I more bullish than current sell-side consensus by > 20%? If yes, FLAG and justify the divergence specifically.
5. Information edge: this is public-information-only synthesis — no channel checks, IR access, or expert network. Carry the mandatory 信息优势声明 disclosure and cap conviction accordingly (no "analyst-grade" certainty on a public-only thesis). See references/information-edge.md.
6. Consensus clone: is the verdict ≈ consensus (same direction AND weighted price within ±10% of median) with no falsifiable variant view? FLAG → add a 变量观点 / Variant Perception statement (what the market prices in · where I differ · what would prove me wrong), or explicitly declare the call consensus-aligned. See references/information-edge.md §1b.
7. Inverted rigor (option-dominated names only): is the segment that drives the largest share of value the least-modeled thing in the report? FLAG if the option/venture value is a single judgment number with no SOTP, no ranges, no P(success). Being rigorous on the visible 10% while hand-waving the decisive 90% is the signature failure for these names — the SOTP (Step 5c) is the fix.

Any FLAG must be addressed in the final report — not buried.

5f. Verdict and Conditions

Commit to one of:

• Strong Buy — high conviction; expected return > 50% with bounded downside
• Buy — good odds per decision rule
• Watch — set an alert price; not yet compelling
• Hold — currently fairly valued for current holders; not adding
• Trim — currently held but starting to deteriorate; reduce position
• Sell — thesis invalidated OR severely overvalued

Specify invalidation conditions — quantitative triggers for when to sell. Examples:

• "Sell if FCF turns negative for 2 consecutive quarters"
• "Sell if NRR falls below 110%"
• "Sell if Net Debt/EBITDA crosses 3.0×"

This implements the source doc's Part 3 §4 "卖出三种情形" pattern —论点被证伪 / 估值严重过高 / 找到更好的标的.

5g. Append Verdict to Log (from references/verdict-log-protocol.md)

After committing the verdict, append one JSON Lines entry to:

~/.claude/projects/-Users-john-awesome-skills/memory/stock-analysis-verdicts.jsonl

The entry must include: ticker, company_name, verdict_date, verdict, conviction, current_price, target_base/bull/bear, weighted_expected_price, weighted_return_36mo, bear_to_current_ratio, horizon_months, archetype, good_company_score, key_bull_assumptions (3-5), key_bear_assumptions (3-5), invalidation_triggers, data_gaps_noted, cognitive_bias_flags, depth_mode, skill_version.

Use atomic append (>> shell redirect or equivalent). Confirm the write succeeded; surface a warning if it failed.

This persistent log is the feedback mechanism — the next analysis on this ticker will read this entry in Step 1.5b and explicitly reckon with what assumptions played out.

Output Format

Render in user's invocation language (Chinese labels if user spoke Chinese; English otherwise). Both versions of the labels are listed below.

# <Ticker> — Investment Analysis Report

## Verdict / 顶层结论
<Strong Buy/Buy/Watch/Hold/Trim/Sell — single line, no hedging>
- Target price range: $X – $Y (Base: $Z)
- Weighted expected return: __% over <horizon, default 36 months>
- Conviction: <High/Medium/Low>
- **Sector archetype**: <SaaS / Hyperscaler / Mature Cash Cow / Capital-Intensive / Cyclical / Financials / REIT / Payment Network>

## Variant Perception / 变量观点 (mandatory)
- Market prices in / 市场当前定价: <reverse-DCF implied growth __% · consensus rating __ / median target $__>
- Where I differ (falsifiable) / 我的差异化判断: <one specific thesis/number/probability claim — or "无: consensus-aligned, value is independent confirmation">
- What would prove me wrong vs the crowd / 证伪点: <specific datapoint>

## Prior Verdict Tracking / 历史 verdict 跟踪 (if log shows past entry)
- Prior verdict (<date>, <X months ago>): <verdict> at $<price>, target $<base>, Bull $<bull>, Bear $<bear>
- Bull/Bear assumptions validation summary
- Current price vs prior trajectory

## Good-Company Score / 好公司评分: X / 10 (archetype: <name>)
| # | Item | Threshold (archetype) | Score | One-line evidence |
| 1 | Revenue growth | ≥<threshold>% | PASS/WEAK/FAIL | <evidence> |
... (10 items, sector-aware thresholds)

## Bull / Base / Bear / 三档情景
| Scenario | Revenue CAGR | Op Margin | Multiple | Target | Probability | Probability Anchors |
| Bull | __% | __% | __× | $__ | __% | base rate <X%> + assumption adj <±pp> + momentum adj <±pp>; disconfirming evidence: <citation> |
| Base | __% | __% | __× | $__ | __% | residual |
| Bear | __% | __% | __× | $__ | __% | base rate <X%> + assumption adj <±pp> + momentum adj <±pp>; confirming-of-Bull citation: <citation> |
- DCF disclosure (mandatory): WACC __% · terminal g __% · reverse-DCF implied growth __% (vs track-record __%) · sensitivity(revenue_cagr ±3pp): $__–$__. Base prob = residual. Reconcile DCF intrinsic vs target if >10% apart. A target without these is illustrative, not analytical.

## Sum-of-the-Parts / 分部加总估值 (option-dominated stocks only)
| Segment | Method | Value low–base–high | % of base equity | P(success) | Grounding |
| <auto> | fixed (DCF EV) | $__–$__–$__ | __% | — | OK |
| <energy> | multiple (GWh×$/kWh×margin) | $__–$__–$__ | __% | — | OK |
| <robotaxi> | option (TAM→share→take×P) | $__–$__–$__ | __% | __% (range) | LOW |
- Per-share distribution: $__ (low) – $__ (base) – $__ (high). Modeled option share of value: __%. **Market-implied option share: __%** (the ~90%-is-option figure). Gap = priced-for-perfection signal. Each venture leg shows its assumption chain + P(success) as a range, never a point.

## Earnings Revision Momentum / 卖方修正动量
- Momentum bucket: <Strong Positive / Mild Positive / Mild Negative / Strong Negative>
- 30-day NTM EPS revision: +/- X%
- Analysts raising vs cutting (30d): <X> up / <Y> down
- Probability adjustment applied: Bull <±pp>, Bear <±pp>

## Peer Comparison Summary / 同业对比摘要
- Archetype peer set: <ticker list>
- Items where target is BEST in panel: <list>
- Items where target is WORST in panel: <list>
- Cross-validation of moat/share claims: <which Industry-worker claims are supported by peer data>

## Worker Findings / 各维度发现
(sorted by severity High → Medium → Low; dedup'd; capped per depth mode)

### [High|Medium|Low] Short Title
- **ID**: `INV-NNN` (preserve worker prefix: BUS / EQ / BS / MGT / IND)
- **Citation**: filing section
- **Evidence**: from worker
- **Implication**: from worker

## Risks I Accept / 我接受的风险 (≥ 3, mandatory)
1. <strongest counter-thesis>
2. <secondary risk>
3. <…>

## Invalidation Conditions / 卖出触发器 (mandatory, ≥ 1 quantitative)
- <e.g., "Sell if FCF negative for 2 consecutive quarters">

## Data Coverage / 数据覆盖度
- Fetched: <list from manifest>
- Missing: <list from manifest with confidence impact>
- Workers degraded: <e.g., "management — DEGRADED (no proxy available)">

## Cognitive-Bias Self-Check / 认知偏差自检
- Anchoring: <PASS|FLAG + rationale>
- Story bias: <PASS|FLAG + rationale>
- Confirmation: <PASS|FLAG + rationale>
- Overconfidence: <PASS|FLAG + rationale>
- Information edge: <PASS|FLAG + rationale>
- Consensus clone / variant view: <PASS|FLAG + rationale>

## Verdict Log / 决策日志
- Logged to: ~/.claude/projects/-Users-john-awesome-skills/memory/stock-analysis-verdicts.jsonl
- Entry timestamp: <ISO 8601>
- Confirm: this verdict will be reviewed when ticker is re-analyzed

Consolidation Rules

• Deduplicate across workers: e.g., if Earnings-Quality flags "negative FCF" (EQ-02) and Balance-Sheet flags "cash runway < 12 months" (BS-03), keep both — they are related but distinct mechanisms. If two workers report the same exact mechanism (rare but possible), merge into one Finding citing both worker prefixes.
• Promote severity: when a Finding is dependency-critical (e.g., balance sheet drives the Bear scenario floor), promote it one severity tier in the synthesis output.
• Suppress contradictions: if Industry says "moat is strong" but Earnings-Quality shows margin compression, do not suppress either — both go in, and the synthesis explains the tension (this is the conflict the user is paying you to surface).
• Volume caps: Lite ≤ 8, Standard ≤ 15, Strict ≤ 25. Truncate Low severity first.

No-Workers-Returned-Findings Case

If all 6 workers return zero Findings (a structurally clean company):

• Good-Company score likely 9–10
• Surface positive observations the workers noted
• Verdict still required — usually Watch (clean but priced for perfection) or Buy (clean and reasonably priced)
• Do not pretend to find issues to look thorough

Toolchain Risk Acknowledgment

This skill depends on web-scraped data from stockanalysis.com, Yahoo Finance, Seeking Alpha, and SEC EDGAR. Any of these can change layout or rate-limit at any time. The fallback chains in data-acquisition-playbook.md are documented but not continuously tested. If Step 2 data acquisition produces sparse output:

1. Check whether scraped pages returned empty content (silent failure)
2. Try the documented fallback source
3. If both fail, mark the missing artifact in the manifest's missing field
4. Workers self-degrade based on what's missing
5. The orchestrator notes data-coverage limitations explicitly in the final report's "Data Coverage" section

A degraded analysis with explicit gaps is more honest than a complete-looking analysis built on stale or missing data.

Load References Selectively

• references/sector-archetypes.md — load during Step 1.5c to classify the archetype and obtain threshold sets.
• references/data-acquisition-playbook.md — load during Step 2 to confirm the SEC EDGAR URL pattern and the fallback chain when primary sources fail.
• references/earnings-revision-momentum.md — load during Step 2 (data) and Step 5d (probability adjustment) for analyst-revision tracking.
• references/verdict-log-protocol.md — load during Step 1.5b (read past verdict) and Step 5g (append new verdict).
• references/good-company-checklist.md — load during Step 5b to score the 10 items.
• references/valuation-methods.md — load during Step 5c for the multi-method valuation procedure including reverse-DCF and Method 5 Sum-of-the-Parts (scripts/finlib/sotp.py) for option-dominated stocks.
• references/scenario-framework.md — load during Step 5d for the Bull/Base/Bear structure.
• references/scenario-probability-calibration.md — load during Step 5d for the calibrated probability assignment framework.
• references/cognitive-bias-gates.md — load during Step 5e to run the 5 self-check questions; references/information-edge.md — the public-info honesty doctrine + the AI-advantaged edges (cross-section scripts/finlib/crosssection.py, monitoring scripts/finlib/verdict_diff.py).

Review Discipline

You are the editor of a five-analyst report. Your value is not in re-doing each analyst's work — it is in committing a verdict. The source doc's strongest message: leadership wants a recommendation backed by structure, not a literature review. Take a position. Name the risk you accept. Commit to invalidation conditions. The user will override your verdict, and that is fine — but they cannot override "it depends".