10x Analysis

Goal: produce a falsifiable thesis on whether a company can 5x and 10x its valuation in 5 years, with named factors, weighted scores, tracked KPIs, catalyst events, and two probability percentages (5x and 10x) drawn from the same rubric — OR, on re-run, refresh that thesis against current data and report on-track / off-track / ahead per metric. Optimize for auditability: every factor score, every KPI target, and every probability change must be traceable to a source.

A 10x in 5 years implies a ~58% CAGR; a 5x in 5 years implies a ~38% CAGR. Base rates are brutal — across all U.S. public companies, roughly 1–3% achieve a 10x in any rolling 5-year window, and roughly 5–10% achieve a 5x. Lean skeptical: most 10x theses should land below 15% probability, and most 5x theses below 35%. A 50%+ 10x probability or 80%+ 5x probability requires extraordinary evidence.

Inputs

Required (one of):

• Ticker symbol for a public company — $CRCL, NVDA, MSFT (strip the $ prefix; normalize to uppercase)
• Company name for a private company — resolved to the slug used under ~/knowledge/private-companies/

Resolution: if the user provides a ticker, look first under ~/knowledge/public-companies/<TICKER>/. If they provide a name, search both public-companies/ and private-companies/. If both directories exist for the same name (rare), prefer the public one and note the duplicate.

Prerequisite: brief.md must exist

This skill builds on brief.md. If it does not exist for the resolved company, stop and instruct the user:

"No brief.md found at <expected_path>. Run /research-public-company $TICKER (or /research-private-company <name>) first, then re-run /10x-analysis."

Do not attempt to re-do research from scratch. Trust brief.md as the source of truth for facts; this skill's job is interpretation and tracking, not data collection. (You may still do small targeted web searches to fill specific gaps the brief omits — current valuation if stale, peer comps, new analyst views — but never to re-derive the whole brief.)

Storage layout

Findings live next to brief.md in the same company directory. One 10x/ subfolder per ticker / slug.

Public company:

~/knowledge/public-companies/<TICKER>/
  brief.md                       # produced by research-public-company
  10x/
    thesis.md                    # current canonical thesis: YAML frontmatter + narrative
    tracker.json                 # machine-readable KPI + catalyst tracker
    changelog.md                 # append-only delta log, newest on top
    snapshots/
      YYYY-MM-DD.md              # frozen point-in-time copies of thesis.md

Private company:

~/knowledge/private-companies/<slug>/
  brief.md                       # produced by research-private-company
  10x/
    thesis.md
    tracker.json
    changelog.md
    snapshots/
      YYYY-MM-DD.md

If 10x/thesis.md exists, run in delta mode. Otherwise run in initial mode. Always check with ls before deciding.

Anchor date and anchor valuation

The anchor is the point in time the 5-year clock starts from. It is fixed at the first run and never updated by re-runs (re-runs track progress against the anchor).

• Public company: anchor date = the date of the most recent earnings call covered in brief.md's frontmatter (financials.latest_quarter period). Anchor valuation = the market cap reported on that earnings date (or the closest available trading day). Pull this from brief.md frontmatter (market_cap_usd, price_date) and verify with a quick web search if the brief is stale.
• Private company: anchor date = the close date of the most recent funding round in brief.md's frontmatter (funding.last_round.date). Anchor valuation = the post-money valuation of that round. If the post-money is not disclosed in the brief, use the most-recently-reported secondary-market or tender-offer valuation and label it (reported, unverified).

Record both in the frontmatter as anchor_date and anchor_valuation_usd. The 5-year targets are anchor_valuation_usd * 5 (5x target) and anchor_valuation_usd * 10 (10x target); both share the same target date of anchor_date + 5 years.

Modes

Initial mode (first time)

1. Read brief.md and any transcripts under transcripts/ (public) or sources cited in the brief (private). Note: do not re-read SEC filings end-to-end unless a specific question requires it — use the brief's distilled facts.
2. Establish anchor_date and anchor_valuation_usd (see above). Compute target_5x_valuation_usd = anchor_valuation_usd * 5, target_10x_valuation_usd = anchor_valuation_usd * 10, and target_date = anchor_date + 5 years.
3. Do targeted web research to fill gaps (recent market sizing, peer comps, base-rate data, recent catalysts not in brief).
4. Score the 7 rubric factors (see Rubric below). Each factor gets a 1–5 score, confidence (low/med/high), and a one-sentence justification with at least one cited source. The same rubric drives both the 5x and 10x probability — do not score twice.
5. Evaluate the pattern match (see Pattern match below). Determine trade_trigger.fired and the status of all 4 leading indicators. Cite sources for each. This is a separate check from the rubric — do not roll it into the probability calculation.
6. Compute both probabilities — prob_5x and prob_10x — using their respective base-rate tables and the shared factor multipliers (see Probability calculation).
7. Build the KPI tracker — 5–10 measurable metrics with current value, 1-year target, 3-year target, and 5-year target. These must be metrics the company actually publishes or that are derivable from public data.
8. Build the catalyst events list — 3–7 named events with expected dates that, if hit (or missed), materially shift the thesis.
9. For each catalyst, search Polymarket and Kalshi for a matching prediction market. Capture the market URL, question text, current price, volume, and match classification (direct / proxy / none) in the catalyst's prediction_markets[] block. Persist URLs in tracker.json under catalyst_markets so they survive future runs. See Prediction market cross-reference below for endpoints and matching rules.
10. Write thesis.md (frontmatter + narrative; format below).
11. Write tracker.json (machine-readable; see tracker.json schema).
12. Copy thesis.md to 10x/snapshots/<today>.md.
13. Create 10x/changelog.md with one entry: ## <today>\n- Initial thesis. Probability: 5x <pct5>% / 10x <pct10>%. Anchor: <date>, $<anchor>B. Targets by <target_date>: 5x $<target5>B, 10x $<target10>B.<br/>Trade-trigger: fired/not-fired. Leading indicators: N/4 firing.
14. Commit and push (see Git workflow).
15. Output the full thesis to the user.

Delta mode (re-running)

1. Load existing thesis.md frontmatter, tracker.json, and the latest snapshot.
2. Do not update the anchor — it is fixed. Re-runs compare current state against the anchor.
3. Targeted refresh:
   • Current valuation (market cap for public; latest secondary / round / press-reported markup for private).
   • Latest values for each tracked KPI.
   • Status of each catalyst event (hit, missed, deferred, still pending).
   • Material new sources: earnings call since last run, new product launch, M&A, regulatory action, competitor move, macro regime change.
   • Prediction-market refresh: for every catalyst, re-pull the linked Polymarket / Kalshi markets from tracker.json.catalyst_markets and refresh price, volume, and resolution status. Also re-search both venues for any new markets that have opened since the last run (catalysts can become tradable mid-thesis). If a market resolved YES/NO since the last run, propagate the resolution to the catalyst's status (only when the market's question matches the catalyst — not just the topic). Never delete a previously-linked URL; mark it resolved or delisted and keep it.
4. Re-score every rubric factor. Note whether each factor moved up, down, or stayed flat vs. the previous run, and why (with a citation).
5. Re-evaluate the pattern match. Check whether trade_trigger.fired should flip (look for new PO / MSA announcements since the last run that meet the criteria — named anchor, binding contract, ≥10% forward-revenue share). Re-evaluate each of the 4 leading indicators against the latest financials. A trade_trigger.fired: false → true transition is the single most important event in the thesis's lifecycle — surface it explicitly in the changelog.
6. Recompute both probabilities (5x and 10x).
7. Mark each KPI as one of: ahead (>10% above the linear-interpolation pace to target), on_track (within ±10% of pace), off_track (>10% below pace), n/a (no current reading available). Use the time elapsed since anchor_date for the linear interpolation. KPI targets are anchored to the 10x thesis — do not duplicate KPI tracking for the 5x case (the same metrics tell the story).
8. Update thesis.md (frontmatter values, factor scores, pattern-match block, KPI current values, catalyst statuses, narrative for materially-changed sections). Do not rewrite untouched sections.
9. Overwrite tracker.json with new current values, statuses, and timestamps.
10. Freeze a new 10x/snapshots/<today>.md.
11. Prepend a dated entry to 10x/changelog.md with:
    • Probability: 5x prev% → new% (Δ) and 10x prev% → new% (Δ).
    • Trade-trigger flip if any (e.g. "Trade-trigger: not-fired → FIRED. Oracle 2.8 GW MSA signed 2026-08-12, ~$3.4B over 5 years, ~22% of FY2027E revenue [n].").
    • Leading indicator status changes (e.g. "Backlog vs. revenue: partial → firing. RPO +84% YoY vs. revenue +18% YoY in Q2 2026.").
    • Factors that moved (e.g. "Moat: 3 → 4 (Coinbase renewal terms favorable)").
    • KPI status changes (e.g. "USDC circulation: on_track → off_track").
    • Catalyst events fired or missed since last run.
    • One-line thesis summary if it materially changed.
12. Commit and push.
13. Output only the changelog entry plus the new 5x and 10x probabilities, the on/off-track scorecard, the pattern-match status (trade-trigger fired? indicators firing count?), and the thesis path. Do not re-output the full thesis.

Empty delta: if nothing material changed (no new earnings call, no catalyst fired, no KPI moved more than ±5%, no rubric factor moved, no catalyst's blended implied_probability_pct moved more than ±10pp, no new prediction market was linked, trade_trigger.fired did not flip, and no leading indicator status changed), output one line: "No material changes since YYYY-MM-DD. Probability holds at 5x % / 10x %." Do not modify files. Do not commit.

A trade_trigger.fired: false → true transition is always material — emit the changelog entry and commit even if every other field is flat.

Rubric (7 factors)

Each factor is scored 1–5 with a confidence label and one-sentence justification with at least one citation from brief.md's sources, or a new source if needed. Weights sum to 100.

Scoring discipline:

• A 3 is neutral — not the default. Most factors at 3 means you don't have a 10x story.
• Confidence labels (low / med / high) capture how sure you are of the score, not the score itself. Low confidence on a 5 is still a 5 — but it widens the probability bands.
• Each factor's justification must cite at least one source. Reuse brief.md's sources where possible; otherwise add the new URL to sources.jsonl under the brief's directory (NOT a separate ledger).

Pattern match (trade-trigger + leading indicators)

Independent of the 7-factor rubric, every thesis must answer two empirical questions drawn from cross-stock pattern studies under ~/knowledge/patterns/ (see be-10x-pattern.md for the anchor case — BE's 18.6x run from June 2025 to May 2026). These questions don't replace the rubric — they're a separate, falsifiable check that surfaces the single most load-bearing signal and four leading indicators, regardless of how the 7 factors scored.

The rubric tells you whether the story is good. The pattern match tells you whether the trade has actually triggered. A high-scoring rubric with no trade-trigger means "watchlist"; a high-scoring rubric WITH a trade-trigger means "the move may already be underway."

The trade-trigger question

Has a named hyperscaler, sovereign, DoD prime, or comparable anchor customer signed a PO (not LOI, not MOU) for ≥10% of forward 12-month revenue in the last 2 quarters?

This is the single observation that converted BE from a $17 stock into a $316 stock (Oracle 2.8 GW MSA → AEP $2.65B → Brookfield $5B → Nebius $2.6B). It is the difference between "interesting story" and "trade now." Most stocks that 10x in <24 months pass through this gate first.

Capture as trade_trigger in the frontmatter:

• fired: true | false — has the gate been crossed?
• customer: <name> — null if fired: false
• contract_type: PO | MSA | binding-supply-agreement | LOI | MOU | press-release-only
• contract_value_usd: — null if not disclosed
• forward_revenue_share_pct: — contract value / forward 12-month revenue × 100
• announcement_date: YYYY-MM-DD — null if fired: false
• source_note: — one sentence + citation

Decision impact:

• If fired: true and the company scores ≥3.5 weighted average on the rubric: surface explicitly in the narrative Confidence notes — the setup is mature.
• If fired: false regardless of rubric score: surface explicitly that the trade-trigger has not fired and name what specifically would trigger it. The thesis is a watchlist item, not a setup. Don't lower the probability mechanically — the rubric already prices forward odds — but the user reads the trade-trigger as the timing signal, not the whether signal.

LOI / MOU / press-release-only never count as fired: true. Reasoning: every 2021 BE-clean-energy round-trip was driven by non-binding announcements. The discipline is binding-contract-or-nothing.

The four leading indicators

These are observable signals that, in BE's case, led the 18x move by 12–18 months. They are not predictive in isolation — but together they tighten or loosen confidence in the rubric score. Capture each in the frontmatter as leading_indicators[] with status: firing | partial | not-firing | n/a.

Decision impact: count how many of the 4 are firing. Document the count in leading_indicators_firing_count (0–4). This number does not feed the probability calculation directly, but the narrative Confidence notes must address any factor that scored ≥4 in the rubric while ≤1 indicator is firing — that's a soft fragility flag (the story is good on paper but the empirical machinery isn't yet showing up in the financials).

Where this came from

The trade-trigger gate and the four indicators were extracted from a single anchor case study (BE 18x pattern, Section 4 — 10-factor checklist). They are deliberately conservative: BE itself scored 19/20 on the full checklist before the trade-trigger fired, so passing this 5-question subset is necessary but not sufficient. Treat it as a sanity filter, not a buy signal.

If the user is searching for new candidates rather than evaluating one they've already picked, use the separate find-next-10x skill — that's where the full screening template lives. This skill assumes you already have a ticker in hand.

Probability calculation

Use a base-rate-anchored, multiplicative model. Run the same calculation twice — once with the 5x base rates, once with the 10x base rates — using the same factor scores and multipliers. The output is prob_5x and prob_10x.

Step 1 — Base rate. Pick the closest bucket. Use the 5x column for the 5x probability and the 10x column for the 10x probability:

These are rough heuristics drawn from historical CRSP / Cambridge Associates / Pitchbook data and are deliberately conservative. 5x base rates are empirically ~3–4× the corresponding 10x base rates across most buckets — driven by the gap between a 38% and 58% required CAGR. Cite a source when adjusting the bucket boundary or rate; otherwise use these defaults.

Step 2 — Factor multipliers. For each rubric factor, apply a multiplier based on the score. The same multipliers apply to both the 5x and 10x calculation — the factors that make a company more likely to 10x also make it more likely to 5x.

Apply each multiplier weighted by the factor's weight share (multiplier ^ (weight / 100)) so that low-weight factors don't dominate. Compound across all 7 factors.

for target in (5x, 10x):
    prob[target] = base_rate[target]
    for each factor f:
        prob[target] *= multiplier(f.score) ** (f.weight / 100)

Step 3 — Confidence band. Compute each calculation a second time with every low confidence factor pushed one step toward neutral (5 → 4, 1 → 2, etc.). That gives the low-confidence-adjusted probability for each target. Report prob_5x_point / prob_5x_band_low and prob_10x_point / prob_10x_band_low. If point and low-band differ by more than 1.5× on either target, flag that the thesis is fragile.

Step 4 — Floor, ceiling, and consistency.

• Cap 10x at 80% and 5x at 95% (neither is a sure thing, but 5x has higher ceilings).
• Floor at 0.3% on both (don't claim 0% — surprises happen).
• Round to one decimal place.
• Consistency check: prob_5x must be ≥ prob_10x. If the calculated 5x is lower, something is wrong with the bucket assignment — re-check before reporting. If they come out equal at the cap, note it.

Step 5 — Sanity check. Compare against historical comparable trajectories for both multiples (e.g. "what fraction of stablecoin issuers, payment networks, or post-IPO fintechs of similar size 5x'd or 10x'd in 5 years?"). If either number is >2× outside that band, either add an explicit override note or revise.

Prediction market cross-reference (catalysts only)

For every catalyst in the thesis, look for a live prediction market on Polymarket and Kalshi that priced the same (or closest proxy) question. A prediction-market price is external, real-money, real-time, and forms a useful independent check on your own catalyst probability. This is decision-support — never overwrite your factor scores or probabilities with market prices, but flag any catalyst where the market disagrees with your status/materiality materially.

Both APIs are public — no auth, no key — so call them directly with curl via Bash.

Polymarket (Gamma API)

Base URL: https://gamma-api.polymarket.com (docs)

Search markets by free text. Filter to active, non-closed markets, sort by volume desc, take the top match:

curl -sG "https://gamma-api.polymarket.com/markets" \
  --data-urlencode "active=true" \
  --data-urlencode "closed=false" \
  --data-urlencode "limit=10" \
  --data-urlencode "order=volumeNum" \
  --data-urlencode "ascending=false" \
  --data-urlencode "q=<catalyst keywords>"

Each market in the response includes id, slug, question, outcomePrices (a JSON-encoded string array like "[\"0.62\", \"0.38\"]" for YES/NO), volumeNum (USD), endDate, and closed. The YES price is outcomePrices[0] (parse the JSON string), which is the market-implied probability of YES (0–1). Construct the market URL as https://polymarket.com/event/<slug>.

Kalshi (trade-api v2)

Base URL: https://api.elections.kalshi.com/trade-api/v2 (also reachable as https://external-api.kalshi.com/trade-api/v2) (docs)

Kalshi's /markets endpoint doesn't take a free-text q — search via /events or /series for the topic, then list the markets under matching events. A quick approach:

# 1) Search events (Kalshi groups markets under events; pagination via cursor)
curl -sG "https://api.elections.kalshi.com/trade-api/v2/events" \
  --data-urlencode "status=open" \
  --data-urlencode "limit=200"
# Filter the response client-side for events whose title/sub_title matches the catalyst keywords.

# 2) For a matching event, list its markets
curl -sG "https://api.elections.kalshi.com/trade-api/v2/markets" \
  --data-urlencode "event_ticker=<EVENT_TICKER>" \
  --data-urlencode "status=open"

Each market includes ticker, event_ticker, title, subtitle, yes_bid / yes_ask / last_price (in cents, 0–100 — divide by 100 for probability), volume, volume_24h, open_interest, and close_time. Mid-price = (yes_bid + yes_ask) / 2 / 100. If only last_price is non-zero, fall back to that. Market URL: https://kalshi.com/markets/<series_ticker>/<event_ticker> (use kalshi.com/markets/<event_ticker>/<market_ticker> when needed — verify the link resolves).

Matching catalysts to markets

For each catalyst, derive 2–4 keyword variants from the label and ticker (e.g. Arc mainnet → ["Arc mainnet", "Circle Arc", "CRCL Arc launch"]) and query both venues. For every candidate market, classify the fit:

• direct — the market resolves on exactly the catalyst (same event, same deadline ±1 quarter). Use this price.
• proxy — the market resolves on a related but not identical question (broader topic, different deadline, adjacent metric). Record it, but discount the signal in the narrative.
• none — no usable market exists. Record null and move on.

Pick at most one Polymarket and one Kalshi market per catalyst. Prefer direct > proxy, then higher volume > lower volume, then closer expiry > farther expiry. If volume is below $5,000 lifetime (Polymarket) or $1,000 24h (Kalshi), flag the price as low_liquidity: true — thin markets are noisy.

Implied probability and blending

For each catalyst, compute:

• polymarket_implied_pct = YES outcome price × 100 (when a direct or proxy market exists).
• kalshi_implied_pct = mid-price × 100 (or last-price × 100 if no two-sided quote).
• implied_probability_pct = simple average of the two when both exist and are non-stale; otherwise whichever exists; otherwise null.

A market is stale if its last trade or last update is older than 14 days, or if it's flagged low_liquidity. Stale markets are recorded but excluded from the blended average — note this explicitly.

Disagreement check

If implied_probability_pct differs from your internal read of the catalyst (materiality: high + status: pending ≈ you think this is a coin-flip-or-better) by more than 25 percentage points, surface it in the narrative under Confidence notes. Two patterns to watch for:

• Market << your view: the crowd doesn't think the catalyst will hit. Sanity-check your assumed timeline; consider downgrading factor 4 (product velocity) or factor 5 (execution).
• Market >> your view: the crowd thinks it's nearly priced in. Consider whether your probability already double-counts it.

Never silently overwrite your factor scores with market prices. The prediction market is a check, not the source.

Delta mode

On re-run, re-pull every catalyst's markets (the linked market may have closed/resolved; new markets may have opened). If a previously-linked market has resolved YES, mark the catalyst status: hit only if the resolution matches the catalyst (not just the broader topic). If resolved NO, mark missed. Record the price delta vs. last run as implied_probability_delta_pp (percentage points).

thesis.md frontmatter schema

---
ticker_or_slug: CRCL                  # uppercase ticker for public, kebab-case slug for private
company_type: public                  # public | private
name: Circle Internet Group, Inc.
first_analyzed: 2026-05-21
last_analyzed: 2026-05-21

anchor:
  date: 2026-03-31                    # for public: end of latest reported quarter
  valuation_usd: 28000000000          # market cap at anchor (public) or post-money (private)
  source_note: "Q1 2026 earnings call 2026-05-11; Morningstar close 2026-05-20 used as proxy for end-quarter market cap"

target:
  date: 2031-03-31                    # anchor + 5 years
  valuation_5x_usd: 140000000000      # 5x anchor
  valuation_10x_usd: 280000000000     # 10x anchor
  implied_cagr_5x_pct: 38.0
  implied_cagr_10x_pct: 58.5

current:
  date: 2026-05-21
  valuation_usd: 27750000000
  pace_5x_pct: -2.2                   # (current - anchor) / (target_5x - anchor) as % progress; computed each run
  pace_10x_pct: -1.0                  # (current - anchor) / (target_10x - anchor) as % progress; computed each run

probability:
  prob_5x_point_pct: 14.5             # 5x probability after rubric + 5x base rate
  prob_5x_band_low_pct: 9.8           # low-confidence-adjusted 5x
  prob_10x_point_pct: 4.2             # 10x probability after rubric + 10x base rate
  prob_10x_band_low_pct: 2.8          # low-confidence-adjusted 10x
  base_rate_bucket: "Mid cap, growth sector"
  base_rate_5x_pct: 10.0
  base_rate_10x_pct: 2.5

# Pattern-match gate — independent of rubric, derived from cross-stock
# pattern studies in ~/knowledge/patterns/. Used as a *timing* signal,
# not a probability adjustment.
trade_trigger:
  fired: false                        # true | false
  customer: null                      # named anchor customer; null if not fired
  contract_type: null                 # PO | MSA | binding-supply-agreement | LOI | MOU | press-release-only
  contract_value_usd: null
  forward_revenue_share_pct: null     # contract value / forward 12mo revenue × 100
  announcement_date: null
  source_note: null                   # one sentence + [n] citation

leading_indicators:
  - id: backlog_vs_revenue
    label: Backlog (or RPO) growing ≥2x faster than revenue
    status: not-firing                # firing | partial | not-firing | n/a
    note: "RPO +12% YoY vs. revenue +14% YoY in Q1 2026 — pacing similarly."
  - id: gaap_gross_margin_inflection
    label: GAAP gross margin inflecting upward 2+ quarters in a row
    status: partial
    note: "GAAP GM expanded 220 bps over 4 quarters; below the 300 bps threshold."
  - id: fcf_turn
    label: FCF turning positive (or significantly less negative) alongside revenue acceleration
    status: not-firing
    note: "FCF still −$X; no acceleration yet."
  - id: customer_concentration_shift
    label: Customer concentration shifting toward a new buyer class
    status: n/a
    note: "10-K customer-concentration disclosure not yet available for FY2025."
leading_indicators_firing_count: 0    # 0–4

factors:
  - id: market_size
    name: Market size & TAM expansion path
    weight: 20
    score: 4
    confidence: med
    rationale: "Stablecoin TAM projected $3.7T by 2030 vs. ~$320B today; Circle's USDC at ~25% share gives ~$925B addressable supply at 10x macro, well over 10x current market cap when grossed up to revenue."
    sources: [3, 8]
  - id: secular_tailwind
    name: Secular / macro tailwind
    weight: 15
    score: 3
    confidence: low
    rationale: "GENIUS Act + AI-agent settlement are real tailwinds, but ~94% of revenue tied to short-rate cycle — net neutral over a 5-year window with Fed easing."
    sources: [8, 14]
  # ... 5 more factors

kpis:
  - id: usdc_circulation_usd
    label: USDC in circulation
    current_value: 77000000000
    current_as_of: 2026-03-31
    target_1y: 110000000000
    target_3y: 250000000000
    target_5y: 500000000000
    units: USD
    source_note: "Q1 2026 earnings; long-term consistent with mgmt 40% CAGR guidance"
    status: on_track                  # ahead | on_track | off_track | n/a (set on each run)
  # ... 4-9 more KPIs

catalysts:
  - id: arc_mainnet_launch
    label: Arc mainnet launch
    expected_window: 2026-Q3
    direction: bull
    materiality: high
    status: pending                   # pending | hit | missed | deferred
    note: "Per Q1 2026 call: mainnet expected summer 2026."
    # Linked prediction markets (persist these URLs across runs — they are the
    # back-pointer for spot-checking the crowd's view of this catalyst over time).
    # Omit the entries if no market exists; never delete a previously-linked URL,
    # only mark it stale / resolved.
    prediction_markets:
      - source: polymarket
        market_id: "arc-mainnet-launch-by-2026"
        market_url: "https://polymarket.com/event/arc-mainnet-launch-by-2026"
        question: "Will Circle's Arc mainnet launch by Sep 30, 2026?"
        match: direct                 # direct | proxy
        yes_price: 0.62               # 0–1, market-implied probability of YES
        volume_usd_lifetime: 145000
        end_date: 2026-09-30
        last_seen: 2026-05-21
        low_liquidity: false
        stale: false
      - source: kalshi
        market_ticker: "KXCIRCLEARC-26SEP30"
        market_url: "https://kalshi.com/markets/kxcirclearc/kxcirclearc-26sep30"
        question: "Circle Arc mainnet live on or before 2026-09-30?"
        match: proxy
        yes_bid: 0.55
        yes_ask: 0.61
        yes_mid: 0.58
        last_price: 0.58
        volume_24h: 1800
        close_time: 2026-09-30T23:59:00Z
        last_seen: 2026-05-21
        low_liquidity: false
        stale: false
    implied_probability_pct: 60.0     # blended avg of non-stale markets; null if no market
    implied_probability_source: "polymarket+kalshi blended"
  # ... 2-6 more catalysts

# Source IDs reference brief.md's sources.jsonl + any new URLs added here.
---

tracker.json schema

A compact, machine-readable mirror of the frontmatter for quick programmatic re-read in delta mode.

{
  "ticker_or_slug": "CRCL",
  "company_type": "public",
  "anchor": { "date": "2026-03-31", "valuation_usd": 28000000000 },
  "target": {
    "date": "2031-03-31",
    "valuation_5x_usd": 140000000000,
    "valuation_10x_usd": 280000000000,
    "implied_cagr_5x_pct": 38.0,
    "implied_cagr_10x_pct": 58.5
  },
  "catalyst_markets": {
    "arc_mainnet_launch": [
      {
        "source": "polymarket",
        "market_url": "https://polymarket.com/event/arc-mainnet-launch-by-2026",
        "question": "Will Circle's Arc mainnet launch by Sep 30, 2026?",
        "first_linked": "2026-05-21",
        "match": "direct"
      },
      {
        "source": "kalshi",
        "market_url": "https://kalshi.com/markets/kxcirclearc/kxcirclearc-26sep30",
        "question": "Circle Arc mainnet live on or before 2026-09-30?",
        "first_linked": "2026-05-21",
        "match": "proxy"
      }
    ]
  },
  "runs": [
    {
      "date": "2026-05-21",
      "current_valuation_usd": 27750000000,
      "prob_5x_point_pct": 14.5,
      "prob_5x_band_low_pct": 9.8,
      "prob_10x_point_pct": 4.2,
      "prob_10x_band_low_pct": 2.8,
      "factor_scores": { "market_size": 4, "secular_tailwind": 3, "moat": 4, "product_velocity": 4, "execution": 3, "unit_economics": 2, "tail_risk": 2 },
      "kpi_status": { "usdc_circulation_usd": "on_track", "cpn_volume_usd": "ahead", "rldc_margin_pct": "off_track" },
      "catalysts_fired": [],
      "catalysts_missed": [],
      "catalyst_implied_pct": { "arc_mainnet_launch": 60.0 },
      "trade_trigger_fired": false,
      "leading_indicators_firing_count": 0,
      "leading_indicators_status": {
        "backlog_vs_revenue": "not-firing",
        "gaap_gross_margin_inflection": "partial",
        "fcf_turn": "not-firing",
        "customer_concentration_shift": "n/a"
      },
      "summary": "Initial thesis."
    }
  ]
}

runs[] is append-only — every analysis run adds a new entry. This is what lets the delta mode compute deltas. The trade_trigger_fired and leading_indicators_* fields are captured per-run so the delta mode can detect the moment the trigger fires (the most important event in the thesis's life) and the moment any indicator flips.

catalyst_markets is the durable back-pointer to the prediction markets you found — keyed by catalyst id, with one entry per (source, market_url) pair, plus the date you first linked it. Treat this as append-only too: never delete a previously-linked URL. If a market resolves or delists, mark it on the entry (resolved_date, resolved_outcome, delisted: true) but keep the URL so it's still browsable from the snapshot history. Each runs[] entry records the current implied probability per catalyst in catalyst_implied_pct for delta tracking.

thesis.md narrative body

After the frontmatter:

# 10x thesis — <Company Name> (<TICKER or slug>)

**Anchor:** <anchor_date> at $<anchor_valuation>B market cap (public) / post-money (private).
**Targets by <target_date>:** 5x → $<target_5x>B (CAGR <cagr_5x>%) · 10x → $<target_10x>B (CAGR <cagr_10x>%).
**Current:** $<current_valuation>B as of <current_date> (5x pace <pace_5x>%, 10x pace <pace_10x>%).
**Probability by <target_date>:** **5x: <prob_5x>%** (band: <band_5x_low>%) · **10x: <prob_10x>%** (band: <band_10x_low>%).

## One-paragraph thesis
A single dense paragraph stating the cleanest version of the bull case — what must be true for the company to 10x (the 5x case is the same story with less of it). No hedging language; no marketing adjectives. State the load-bearing assumption explicitly.

## Factor scorecard
| # | Factor | Weight | Score | Confidence | One-line rationale |
|---|---|---:|---:|---|---|
| 1 | Market size & TAM expansion | 20 | 4 | med | … |
| 2 | Secular tailwind | 15 | 3 | low | … |
| … | … | … | … | … | … |

## Pattern match — trade-trigger + leading indicators

Independent check against the cross-stock pattern in [`patterns/be-10x-pattern.md`](../../patterns/be-10x-pattern.md). Surfaces the *timing* signal separately from the rubric.

**Trade-trigger:** `fired` / `not fired`. One sentence on the named anchor customer, contract type, value, forward-revenue share, announcement date, and citation. If `not fired`, name what specifically would trigger it (e.g. "would fire if Oracle / AWS / Azure signs a multi-GW PO covering ≥10% of FY2027E revenue").

**Leading indicators:**

| # | Indicator | Status | One-line note |
|---|---|---|---|
| 1 | Backlog (or RPO) ≥2x revenue growth rate | firing / partial / not-firing / n/a | … |
| 2 | GAAP gross margin inflecting upward 2+ qtrs | … | … |
| 3 | FCF turn alongside revenue acceleration | … | … |
| 4 | Customer concentration shifting to new buyer class | … | … |

**Firing count:** N/4. If any rubric factor scores ≥4 while ≤1 indicator is firing, note the fragility here.

## What has to be true
3–5 bullets. Concrete, falsifiable. Not "execute well" — instead "USDC supply must reach $500B by 2031 (vs. $77B today)" or "Arc must capture >$50B TVL within 24 months of mainnet launch."

## KPIs to track
Table of the KPIs from frontmatter, with current value, 1y/3y/5y targets, current status, and a one-line interpretation per row.

## Catalysts to watch
Table of catalyst events from frontmatter, with expected window, direction (bull/bear), materiality, current status, the **market-implied probability** if a Polymarket or Kalshi market exists, and a one-line note. Render the linked prediction markets as clickable URLs in a sub-row or footnote — the goal is that the reader can jump from this table to the live market in one click and see the latest price themselves. Example:

| # | Catalyst | Window | Dir | Materiality | Status | Implied % | Note | Markets |
|---|---|---|---|---|---|---:|---|---|
| 1 | Arc mainnet launch | 2026-Q3 | bull | high | pending | 60% | Mgmt guided summer 2026 | [Polymarket](https://polymarket.com/event/arc-mainnet-launch-by-2026) · [Kalshi](https://kalshi.com/markets/kxcirclearc/kxcirclearc-26sep30) |
| 2 | … | … | … | … | … | — | No usable market | none |

When `match: proxy`, suffix the link with `(proxy)`. When a market is stale or low-liquidity, suffix with `(stale)` or `(thin)`. When no market exists, write `none` so the reader knows you looked and didn't find one — don't leave the cell blank.

## What kills the thesis
3–5 concrete failure modes. Specific enough to be falsifiable — not "competition increases" but "Tether USAT captures >40% share of GENIUS-compliant USD stablecoin issuance by end of 2027, locking USDC out of new bank-issued / Treasury-issued distribution".

## Base rates and analogues
- Bucket assignment with both base rates (5x and 10x) — justify the bucket.
- 2–4 historical analogues (companies of similar profile at a similar inflection point) and what fraction 5x'd vs. 10x'd. Cite the data.

## Probability derivation
Show **both** calculations side by side: 5x base rate and 10x base rate, the shared factor multipliers, and the two final numbers. Make this reproducible.

## Confidence notes
- Which factor scores are most fragile and why.
- What single piece of new information would shift the probability most.
- Open questions the brief.md couldn't answer.

## Sources
Numbered list referencing source IDs used. Reuse `brief.md`'s sources where possible; new URLs added during this analysis should also be appended to the brief's `sources.jsonl` with today's date in `cited_in`.

Changelog entry (delta mode)

Prepend to 10x/changelog.md:

## <YYYY-MM-DD>
- **Probability — 5x:** <prev_5x>% → <new_5x>% (Δ <signed_5x>%). Band: <prev_5x_low>% → <new_5x_low>%.
- **Probability — 10x:** <prev_10x>% → <new_10x>% (Δ <signed_10x>%). Band: <prev_10x_low>% → <new_10x_low>%.
- **Valuation:** $<prev>B → $<curr>B. Pace vs targets: 5x <pace_5x>%, 10x <pace_10x>% (<status: ahead/on_track/behind>).
- **Trade-trigger:** <fired this run? prev → curr — if newly fired, name customer + contract type + value + forward-revenue share + citation. If still not fired, omit unless near-miss worth flagging>.
- **Leading indicators:** <each indicator that flipped, prev_status → curr_status, with one-line cause>. Firing count: <prev>/4 → <curr>/4. Drop unchanged.
- **Factor moves:** <list each factor that changed score, with prev → curr and one-line cause>. Drop unchanged factors.
- **KPI status changes:** <KPI: prev_status → curr_status, with current value>. Drop unchanged.
- **Catalysts fired:** <list>. **Catalysts missed:** <list>. **Catalysts newly pending:** <list>.
- **Prediction-market moves:** for each catalyst with a linked market, `<catalyst_id>: <prev_implied>% → <new_implied>% (Δ <signed>pp)`. Note new markets linked this run and any markets that resolved.
- **New material info:** 1–3 bullets summarizing what new earnings / events / sources drove the changes, with citations.
- **Thesis-killer check:** any of the "what kills the thesis" conditions tripped? List or "none".
- **Sources added:** N new URLs — describe what they say.

If no change is material, see Empty delta above — emit one line and do not write files.

Git workflow

Run from $KNOWLEDGE_ROOT (default ~/knowledge):

git add <public-companies|private-companies>/<TICKER or slug>/10x/
git commit -m "<message>"
git push

Commit message format:

• Initial mode: 10x($TICKER): initial thesis — 5x <pct5>% / 10x <pct10>% (e.g. 10x($CRCL): initial thesis — 5x 14.5% / 10x 4.2%).
• Delta mode: 10x($TICKER): <YYYY-MM-DD> — 5x <prev5>%→<new5>% / 10x <prev10>%→<new10>% (<one-line driver>) (e.g. 10x($CRCL): 2026-08-12 — 5x 14.5%→17.2% / 10x 4.2%→5.1% (Q2 beat, Arc mainnet on track)).

Rules:

• Stage explicitly — git add .../10x/. Never git add -A or git add .. If you added new URLs to the parent sources.jsonl, stage that file explicitly too.
• No commit on empty delta.
• One commit per company per run.
• If git push fails (no upstream, network, conflict), report it and stop. The local commit remains on disk.
• Never run destructive git operations.

Style rules

• Cite every non-trivial claim with the same [n] convention as brief.md, referencing the brief's sources.jsonl IDs where possible.
• Be brutally explicit about the load-bearing assumption. The reader should be able to find the one sentence that, if wrong, collapses the thesis.
• No marketing language. Strip "transformative", "revolutionary", "best-in-class" etc. State what would actually happen.
• No fabrication. If you can't source a market-size or peer comp, say so and either lower confidence or drop the claim.
• Numbers carry units and dates. Never "$77B" alone — always "$77B at Q1 2026 quarter-end".
• Falsifiability over comprehensiveness. Each tracked KPI must have a numeric target that can be checked next quarter. "Grow market share" is not a KPI; "USDC share of stablecoin transaction volume ≥65% in Visa Onchain Analytics" is.

What to skip

• Don't re-explain what the company does — brief.md already covers it. The thesis assumes the reader has read the brief.
• Don't summarize the rubric in prose; the table IS the summary.
• Don't include a "conclusion" — the two probabilities (5x and 10x) are the conclusion.
• Don't run when brief.md is missing. Stop and instruct the user.
• Don't update the anchor on re-runs. Ever. The whole point is to track progress against a fixed starting line.
• Don't bundle the probability with a buy/sell recommendation. This is a probability analysis, not investment advice.