transcript

name	transcript
description	Pull an earnings call transcript from Financial Modeling Prep, extract qualitative signal (management commentary deltas, hedging shifts, Q&A tone), and produce a thesis-delta-first Research note. Use when user says "transcript", "pull earnings call", "transcript diff", or "ingest [TICKER] earnings".
model	opus
effort	max
allowed-tools	Read Grep Glob Edit Write Bash(date * cp * mkdir * ls * curl * grep * cat * jq * printf * awk * sed * wc * python3 *)

Convert an FMP earnings transcript into a thesis-delta-first Research note. Pulls the target quarter's transcript plus the prior 2 quarters for delta analysis. Extracts qualitative signals — new/dropped management language, hedging shifts, Q&A skepticism, specificity changes — then cross-references against the thesis's Bull Case, Bear Case, and Conviction Triggers. Writes a Research note, appends a Log entry, and suggests /sync for propagation.

This skill creates a research note AND emits a research-driven Log prefix — /sync WILL propagate to affected sector and macro notes. Run /sync TICKER and /graph last after this skill, in that order.

Arguments

$ARGUMENTS should match one of:

• Latest quarter: NVDA — fetch the most recent available transcript
• Specific quarter: NVDA Q4-2026 (format: QN-YYYY — fiscal year) — fetch a specific quarter
• Diff two quarters: NVDA --diff Q3-2026 Q4-2026 — produce a side-by-side comparison research note (source_type: analyst-report)
• List available: NVDA --list — read-only; lists every transcript FMP has for the ticker, with year/quarter and approximate length. No writes.

Ambiguous / empty → ask user to clarify ticker.

Step 0: Pre-flight (MANDATORY)

0.1: Acquire vault lock

ticker:TICKER scope per .claude/skills/_shared/preflight.md Procedure 1. Timeout: 10 minutes (transcript pulls + analysis + post-write verification + sector resolution can be slow on long calls). Capture token at Step 0.1, verify ownership (Procedure 1.5) at every subsequent Bash block, release explicitly in the final block (Step 12).

--list mode: acquire read-only lock instead (Procedure 1.2 read-only scope). Skip Steps 5-12; only fetch + display.

0.2: Rename-marker pre-flight

Procedure 2. If .rename_incomplete.TICKER exists at vault root, hard-block per contract §2.3. The transcript ingest writes a Log entry to the thesis keyed by current filename; mid-rename split would leave Log entries on one name and inbound wikilinks on another.

0.3: Thesis-existence probe

ls "Theses/$TICKER - "*.md 2>/dev/null

• 0 matches → ❌ No thesis found for [TICKER] in Theses/. /transcript ingests earnings into an existing investment case — it does NOT create one. Run /thesis [TICKER] first.
• 1 match → proceed. Capture path as THESIS_PATH.
• 2+ matches → ❌ Ambiguous ticker [TICKER] — multiple thesis files match. Disambiguate manually.

/transcript deliberately requires a thesis. Earnings transcripts without a thesis to anchor the analysis are noise — use /ingest for orphan-earnings content if needed.

0.4: FMP API key probe

if [ ! -f .data/config.json ]; then
  echo "❌ FMP API key config missing: .data/config.json"
  exit 1
fi
API_KEY=$(grep -E '"fmp_api_key"\s*:' .data/config.json | sed -E 's/.*"fmp_api_key"\s*:\s*"([^"]+)".*/\1/')
[ -z "$API_KEY" ] && { echo "❌ FMP API key missing or empty in .data/config.json"; exit 1; }
echo "FMP_KEY_OK"

0.5: Transcript cache directory

mkdir -p .data/transcripts

Raw transcripts cache to .data/transcripts/TICKER_QN-YYYY.json. Re-runs of /transcript on the same quarter read from cache (no API burn). Cache is gitignored via .data/ in .gitignore (confirmed in Live Portfolio's frontmatter).

Step 1: Resolve target quarter(s)

Default mode (latest quarter)

TICKER_URL=$(printf '%s' "$RAW_TICKER" | jq -sRr @uri)
BASE="https://financialmodelingprep.com/stable"

# List of available transcript dates
curl -sf "$BASE/earning-call-transcript-dates?symbol=$TICKER_URL&apikey=$API_KEY" > /tmp/transcript_dates_${TICKER}.json

Parse the response (array of {quarter, year, date} records sorted descending by date). Pick [0] as the target quarter (most recent reported).

Edge cases:

• Empty array → ⚠️ No transcripts available for [TICKER] via FMP. Possible causes: (1) non-US listing without earnings call coverage, (2) FMP coverage gap, (3) company doesn't host public earnings calls. Skill cannot proceed.
• Latest is older than 130 days → ℹ️ Latest transcript is from [date] ([N] days old). Next earnings expected based on FMP calendar: [next-earnings-date or "unknown"]. Proceeding with this transcript anyway.

Specific quarter mode (NVDA Q4-2026)

Parse QN-YYYY syntax → quarter: N, year: YYYY. Fetch directly:

curl -sf "$BASE/earning-call-transcript?symbol=$TICKER_URL&year=$YEAR&quarter=$QUARTER&apikey=$API_KEY" > /tmp/transcript_${TICKER}_${QUARTER}-${YEAR}.json

If FMP returns empty ([] or 404) → ⚠️ Transcript not available for [TICKER] Q[N]-[YYYY]. Use /transcript [TICKER] --list to see available quarters.

Diff mode (NVDA --diff Q3-2026 Q4-2026)

Treat as TWO specific-quarter fetches. Both must succeed; if either fails, abort with the per-quarter unavailability message.

--list mode

After fetching earning-call-transcript-dates, output:

[TICKER] — transcripts available via FMP:
  Q4-2026  (reported 2026-05-22, ~14,200 words)  ← most recent
  Q3-2026  (reported 2026-02-15, ~13,800 words)
  Q2-2026  (reported 2025-11-12, ~12,900 words)
  ...

Then release lock and exit. No further steps.

Identify prior 2 quarters (for default + specific-quarter modes; not diff)

From the transcript-dates list, select the two quarters immediately preceding the target. Use these for delta extraction in Step 4. If fewer than 2 prior quarters exist (e.g., recent IPO), proceed with whatever is available and note this in the Research note's Evidence section.

Step 2: Fetch transcripts (parallel batch)

For target + prior 2 quarters (default mode) or target + diff-target (diff mode):

# Cache-first: if .data/transcripts/TICKER_QN-YYYY.json exists, skip the curl.
for QY in "${TARGET_QUARTERS[@]}"; do
  CACHE=".data/transcripts/${TICKER}_${QY}.json"
  if [ -f "$CACHE" ]; then
    cp "$CACHE" "/tmp/transcript_${TICKER}_${QY}.json"
  else
    Q=$(echo "$QY" | cut -d'-' -f1 | sed 's/Q//')
    Y=$(echo "$QY" | cut -d'-' -f2)
    # Fetch to a TEMP file, NOT straight to $CACHE. `curl -sf | tee "$CACHE"`
    # truncated $CACHE to empty before curl's failure was known, and the
    # pipeline's exit status is tee's (0) — so a failed fetch left an empty
    # $CACHE that every later run trusted as a valid cache. Promote to $CACHE
    # only after validation below.
    curl -sf "$BASE/earning-call-transcript?symbol=$TICKER_URL&year=$Y&quarter=$Q&apikey=$API_KEY" \
      > "/tmp/transcript_${TICKER}_${QY}.json" &
  fi
done
wait

# Promote only non-empty, content-bearing responses to the persistent cache;
# delete failed/empty temps so no poisoned cache is written.
for QY in "${TARGET_QUARTERS[@]}"; do
  CACHE=".data/transcripts/${TICKER}_${QY}.json"
  [ -f "$CACHE" ] && continue            # already served from cache this run
  T="/tmp/transcript_${TICKER}_${QY}.json"
  if [ -s "$T" ] && grep -q '"content"' "$T"; then
    cp "$T" "$CACHE"
  else
    rm -f "$T"                           # failed/empty fetch — leave NO cache behind
  fi
done

API failure handling:

• Target quarter fetch fails → abort with the unavailability message from Step 1.
• Prior quarter fetch fails → proceed but degrade analysis (Step 4 notes which signals had only 1 prior comparator instead of 2).

FMP response shape (post-parse): [{ symbol, period, year, quarter, date, content }]. The content field is the full transcript as plain text — typically Operator: ... Prepared Remarks: ... Q&A: ... formatting, varies by company. Length 8,000-30,000 words.

Content validation: confirm content is non-empty (>500 chars) and parseable. If FMP returned a stub or error payload, abort target-only branch with: ⚠️ FMP returned empty or malformed transcript content for Q[N]-[YYYY]. Cache cleared. Re-run /transcript [TICKER] to retry.

# Clear bad cache on validation failure
rm -f ".data/transcripts/${TICKER}_${QY}.json"

Step 3: Split transcript into prepared-remarks vs. Q&A

Per-quarter, split the content field into two sections:

• Prepared remarks: text from start of management presentation to the first analyst question marker
• Q&A: from first analyst question marker to end

Detection heuristics (in order — first match wins):

1. Literal section markers: Q&A, Q & A, Questions and Answers, Question-and-Answer Session
2. Operator transition phrases: we'll now open the line for questions, we'll now open the call for questions, our first question comes from
3. Speaker-pattern shift: ≥3 consecutive analyst-style speaker tags ([Name] - [Bank/Firm]) within a 200-word window

If no Q&A boundary detected (rare — happens for prerecorded reports without Q&A) → treat entire transcript as prepared remarks; Q&A-derived signals (skeptical-keyword density, evasiveness) marked N/A in Evidence.

Record per-quarter:

• prepared_remarks_text, prepared_remarks_word_count
• qa_text, qa_word_count, qa_turn_count (number of distinct speaker-change events)

Step 4: Extract qualitative signals (delta analysis)

Skipped entirely in --diff mode — diff mode produces a different output shape (see Step 7B).

Script-first (2026-07-08): signals 4.1–4.7 are deterministic word-frequency / regex math — run the helper, do NOT recompute them by hand (the generate_graph.py / lint.py precedent):

python3 .claude/skills/transcript/extract_transcript_signals.py \
  --current .data/transcripts/${TICKER}_${TARGET_QY}.json \
  --prior   .data/transcripts/${TICKER}_${PRIOR1_QY}.json \
  --prior   .data/transcripts/${TICKER}_${PRIOR2_QY}.json

It emits one JSON object with new_language, dropped_language, hedging, specificity, qa_skeptical, evasiveness, guidance — each already carrying the current/prior-2-avg values, deltas, and the ±25% / ≥20% / +50% flags that Steps 6/7/10/11/12 consume. priors_used/degraded_single_prior mark the 1-prior degradation case; qa_detected: false marks the no-Q&A fallback (4.5/4.6 → N/A). Exit 3 (self-validation: unreadable/empty current transcript) → surface the error, do not fabricate signals.

The LLM's job is Step 6 (cross-referencing these numbers against the thesis Bull/Bear/Triggers) — the genuinely qualitative work — NOT re-deriving the counts. The 4.1–4.7 sub-specs below are the reference spec the script implements (and the manual fallback if python3 is unavailable — exit ≠ 0,3). Evasiveness (4.6) carries the mandatory heuristic caveat in the script output; propagate it verbatim into the Evidence note.

4.1: New language (current Q only)

3+ word phrases appearing ≥2 times in current Q's prepared remarks but 0 times in prior 2 Qs prepared remarks. Normalize: lowercase, strip punctuation, collapse whitespace.

Filter list (exclude generic phrases): during the quarter, year over year, as we look ahead, let me start by, thank you for joining, as we discussed, going forward. Maintain a 30-phrase exclusion list inline.

Output: top 10 by frequency in current Q, descending.

4.2: Dropped language (current Q only)

Inverse of 4.1: 3+ word phrases appearing ≥2 times in BOTH prior 2 Qs prepared remarks but 0 times in current Q prepared remarks.

Output: top 10 by combined-prior-Q frequency, descending. These are the most analytically interesting — phrases management deliberately stopped using are the inverse fingerprint of new strategic framing.

4.3: Hedging density

Count occurrences of hedging vocabulary per 1000 words of prepared remarks. Vocabulary:

approximately, roughly, around, about, expected to, expecting, anticipate, anticipating, we believe, we think, we feel, should, could, might, may, likely, unlikely, probably, potentially, if all goes well, assuming, subject to, pending, tentatively, roughly speaking.

Compute per-Q rate. Report current vs. prior-2 average. Flag shifts >25% (either direction) — direction is itself the signal: management hedging less is confidence; hedging more is concern.

4.4: Specificity (numeric mentions in prepared remarks)

Count numeric mentions: any of \$[0-9], [0-9]+\.[0-9]+%, [0-9]+%, [0-9]+\.?[0-9]*\s*(million|billion|trillion|M|B|T)\b, Q[1-4]\s+(of|FY)?\s*[0-9]{2,4}. Per 1000 words of prepared remarks.

A drop ≥20% vs prior-2 average indicates management retreated to qualitative framing — often precedes guidance walks.

4.5: Q&A skeptical-keyword density

Count occurrences in qa_text (combine all analyst questions; exclude management responses where extractable) of: concern, concerned, headwind, pressure, slowdown, decel, moderation, moderating, softer, softening, weakness, disappointed, missed, below expectations, light vs, challenged, difficult, tough, competitive pressure, pricing pressure, margin compression, cyclical, inventory correction.

Per 1000 words of Q&A. Compare to prior-2 average. Rising density = sell-side getting more skeptical. Falling density = capitulation (which is itself a signal — sell-side relief often marks local conviction tops).

4.6: Management evasiveness (Q&A turns)

For each speaker-change turn in Q&A where an analyst asks a question and management responds:

• Extract the first noun-like token sequences from the question (3-7 most-content-bearing words)
• Check whether management's first 50 words contains any of those tokens
• If 0 matches → flag as "evasive"

Compute evasive_turns / total_qa_turns. Compare to prior-2 average.

Important caveat in the Research note: this is a heuristic — disclosure-restricted topics (M&A, legal, pricing strategy) often look "evasive" by this measure but reflect legitimate non-disclosure. The skill surfaces the measure as evidence to inspect, not as a conclusion.

4.7: Guidance language patterns

Scan prepared remarks + Q&A for guidance constructs:

• Quarterly guidance: Q[1-4]\s+revenue\s+(of|in)\s+, we expect\s+Q[1-4], guiding\s+to\s+
• Annual guidance: full year, fiscal year, FY[0-9]{2,4}
• Range vs. point: between\s+\$[\d.]+\s+and\s+\$[\d.]+ (range) vs. approximately\s+\$[\d.]+ (point)

Track: did current Q widen, narrow, or eliminate guidance ranges vs. prior Q? Did it shift from annual to quarterly framing? These are explicit confidence signals.

Step 5: Read thesis for context (parallel reads)

Single tool-call block:

Tool	Target	Purpose
Read	$THESIS_PATH (already located in Step 0.3)	Bull Case, Bear Case, Conviction Triggers, recent Log entries (last 5)
Read	_hot.md	Recent Conviction Changes, Open Questions for this ticker
Read	_graph.md	Adjacency primer (Step 6 Mode A + Step 9 Mode C fanout — read once, parse in memory)

Extract from the thesis:

• bull_case_drivers: parse Bull Case section; identify 3-5 named drivers (each driver typically has a sentence-opening phrase, e.g., "Rack-scale deployment economics drive NVL72 attach rate inflection")
• bear_case_risks: parse Bear Case section; identify 3-5 named risks
• conviction_triggers: parse Conviction Triggers section; extract the → HIGH if, → LOW if, → CLOSE if falsifiable statements

These become the cross-reference targets in Step 6.

Step 6: Cross-reference signals against thesis

For each signal extracted in Step 4, attempt to match against:

6.1: Bull-case driver alignment

For each bull_case_drivers[i]:

• Tokenize the driver phrase (significant nouns + verbs)
• Search Step 4.1 (new language) for phrases overlapping ≥2 tokens with the driver
  • Match → "Bull case driver confirmed by new framing: [driver] ↔ [new phrase]"
• Search Step 4.2 (dropped language) for phrases overlapping ≥2 tokens
  • Match → "⚠️ Bull case driver may be weakening: [driver] ↔ [dropped phrase] — management stopped framing this"

6.2: Bear-case risk alignment

For each bear_case_risks[i]:

• Same tokenization
• Match against new language → "Bear case risk surfaced in management framing: [risk] ↔ [new phrase]"
• Match against dropped language → "Bear case risk de-emphasized in framing: [risk] ↔ [dropped phrase] — may indicate management confidence OR avoidance"
• Cross-reference 4.5 (Q&A skepticism) — if analysts are now asking about a previously-mute bear-case risk: Analyst questions surfaced bear-case risk: [risk]

6.3: Conviction trigger touching

For each falsifiable trigger:

• Parse the trigger's named variable (e.g., Hyperscaler capex guides flat for 2 consecutive Qs)
• Check whether current Q transcript provides evidence on that variable
• If yes → flag the trigger as "touched" with direction (firing toward HIGH / firing toward LOW / firing toward CLOSE)
• Touched triggers are MANDATORY content in the Research note's Thesis Delta section

6.4: Guidance vs. consensus framing

If 4.7 detects a guidance shift (widened range, eliminated annual guidance, etc.):

• Cross-reference whether the thesis Bull Case or Bear Case explicitly hinges on guidance-trajectory expectations
• If yes → flag in Thesis Delta as direct evidence

Step 7: Write Research note

Two output shapes depending on mode.

Step 7A: Default + specific-quarter mode

File: Research/YYYY-MM-DD - [TICKER] [QN-YYYY] - earnings.md (where YYYY-MM-DD is today's date)

Frontmatter:

---
date: YYYY-MM-DD
tags: [research, earnings, TICKER, SECTOR_TAG]
status: active
sector: [from thesis frontmatter]
ticker: TICKER
source: https://financialmodelingprep.com/stable/earning-call-transcript?symbol=TICKER&year=YYYY&quarter=N
source_type: earnings
transcript_quarter: QN-YYYY
transcript_date: YYYY-MM-DD  (from FMP `date` field — actual earnings call date)
transcript_word_count: [N]
prior_comparators: [QN-1-YYYY, QN-2-YYYY]
---

Body sections (in order — all required except where noted):

# [TICKER] [Q[N] FY[YYYY]] — Earnings Transcript

## Thesis Delta

[1-2 sentences PER cross-reference hit from Step 6. Lead with the strongest signal. Use the templates:]
- **Bull case [strengthened|weakened|unchanged]** — [specific driver from Step 6.1] ↔ [evidence from Step 4]
- **Bear case [strengthened|weakened|unchanged]** — [risk from Step 6.2] ↔ [evidence]
- **Conviction trigger touched** — [trigger text from Step 6.3] — current Q [confirms/disconfirms]: [evidence]
- **Guidance shift** (if applicable per Step 6.4) — [shift type] vs. [thesis assumption]

[If zero cross-references hit: this section reads "No direct thesis-relevant deltas. Notable framing shifts logged in Evidence below for future synthesis."]

## Summary

[2-4 paragraphs. Lead with management's CORE argument for the quarter — what mechanism are they pitching to explain the results, what forward construction are they framing? NOT a press-release re-summary; the thesis owns the business description. Capture the argument structure, the named drivers, the qualifier/hedge structure. Length proportional to transcript: 2 paragraphs for short (~8,000 words), 3-4 paragraphs for long (>20,000 words).]

## Evidence

### Language deltas

**New framing (top 5 introduced this Q)**:
| Phrase | Mentions this Q | Mentions prior 2 Qs |
|---|---|---|
| [phrase] | [N] | 0 |
| ... |

**Dropped framing (top 5 retired this Q)**:
| Phrase | Mentions prior 2 Qs combined | Mentions this Q |
|---|---|---|
| [phrase] | [N] | 0 |
| ... |

### Hedging & specificity

| Metric | Current Q | Prior 2-Q avg | Δ | Direction |
|---|---|---|---|---|
| Hedging density (per 1000 words) | [N] | [N] | [Δ%] | [more confident / more hedged / unchanged] |
| Numeric mention density (per 1000 words) | [N] | [N] | [Δ%] | [more specific / less specific / unchanged] |

### Q&A signals

| Metric | Current Q | Prior 2-Q avg | Δ |
|---|---|---|---|
| Skeptical-keyword density (per 1000 words of Q&A) | [N] | [N] | [Δ%] |
| Evasive turns (% of total Q&A turns) | [N]% | [N]% | [Δpp] |
| Total Q&A turns | [N] | [N] | — |

[Caveat: evasiveness is heuristic — disclosure-restricted topics (M&A, legal, pricing) often appear evasive but reflect legitimate non-disclosure.]

### Guidance language (if Step 4.7 surfaced changes)

[Specifics: range width change, point-vs-range shift, annual-vs-quarterly shift, dropped guidance, added guidance]

## Contradiction Check

[Specific to which thesis assumption the transcript contradicts or supports. Pull from Step 6 cross-references. Examples:]
- [[Research/path-to-prior-research-note]] argued [specific thesis] — current Q transcript supports / contradicts via [evidence].
- Bear case (per [[Theses/TICKER]] §Bear Case) hinges on [risk]; current Q transcript [evidence for/against].

[If no specific contradictions: "Transcript reinforces existing thesis framing — no contradictions surfaced."]

## Source Excerpts

[3-7 verbatim quotes that anchor the Thesis Delta points. Quote blocks formatted as Obsidian blockquotes (`>`). Each quote attributed by speaker if extractable:]

> [CEO Name]: "...verbatim quote..."

> [Analyst Name, Firm]: "...verbatim question..."

[Quotes should be SHORT — 1-3 sentences each. The reader can read the full transcript via the source: URL if they need more.]

Required ## Key Segments section (per /ingest content-quality check #5 when source_words >15,000):

## Key Segments

[3-5 sub-sections mirroring the transcript's structural progression. Each 2-5 sentences. Examples:]

### Prepared Remarks - Strategic Framing
[Management's positioning of the quarter — what story are they telling about the business right now]

### Prepared Remarks - Segment Discussion
[Per-segment color: which segments management emphasized, which they de-emphasized]

### Prepared Remarks - Forward Construction
[Guidance philosophy, capex commentary, capital allocation framing]

### Q&A - Most Skeptical Exchange
[The single most-pointed analyst question and management's response — verbatim or paraphrased + commentary]

### Q&A - Most-Disclosed New Information
[Where management revealed something that wasn't in prepared remarks — often the highest-value information from any earnings call]

Include ## Key Segments ONLY when transcript_word_count > 15,000. For shorter transcripts (rare — most US large-cap calls run 10-20k words), omit per /ingest Step 2 spec.

Step 7B: --diff mode

File: Research/YYYY-MM-DD - [TICKER] [Q1] vs [Q2] - transcript diff.md

Frontmatter:

---
date: YYYY-MM-DD
tags: [research, earnings, comparison, TICKER, SECTOR_TAG]
status: active
sector: [from thesis frontmatter]
ticker: TICKER
source: [https://financialmodelingprep.com/stable/earning-call-transcript URLs for both quarters — comma separated]
source_type: analyst-report
diff_quarters: [Q1-YYYY, Q2-YYYY]
---

Body:

# [TICKER] [Q1-YYYY] vs [Q2-YYYY] — Transcript Diff

## Thesis Delta
[Cross-reference findings against thesis — same structure as Step 7A but framed as "between these two quarters"]

## Summary
[2-3 paragraphs framing the analytical comparison — what story does management tell differently between these two quarters? Lead with the most-load-bearing change.]

## Evidence

### Side-by-side language shift

| Theme / Phrase | [Q1-YYYY] frequency | [Q2-YYYY] frequency | Direction |
|---|---|---|---|
| [top 15 phrases with biggest absolute change] |

### Side-by-side hedging & specificity

| Metric | [Q1-YYYY] | [Q2-YYYY] | Δ |
|---|---|---|---|
| Hedging density | | | |
| Numeric density | | | |
| Q&A skeptical density | | | |
| Evasiveness % | | | |

### Strategic framing shifts (qualitative)

- **Introduced between quarters**: [list]
- **Dropped between quarters**: [list]
- **Re-framed**: [phrases that changed wording but kept meaning — e.g., "Hopper transition risk" → "Blackwell ramp velocity"]

## Contradiction Check
[Same scope as 7A]

## Source Excerpts
[Paired quotes — one from each quarter — anchoring the largest shifts]

Step 7.5: Post-write verification gate

Re-read the just-written Research note. Apply the verification checks from /ingest Step Post-write verification:

• Structural checks 1-4 (frontmatter parseability, required fields, body non-empty with at least one ## section, last line not mid-sentence): block on failure → restore via deletion + abort.
• Content-quality check 5 (proportional body word count floor): for source_type: earnings with transcripts typically >15,000 words → body must be ≥2,500 words AND have a ## Key Segments section with ≥3 sub-sections. For diff mode source_type: analyst-report → body must be ≥800 words (1,500-5,000 source-word bucket).
• Content-quality check 7 (section structural minimum): all 4 required sections present with non-empty content — ## Thesis Delta, ## Summary, ## Evidence, ## Contradiction Check.
• Domain validator #8 (source_type: earnings signature — applies to Step 7A): MUST contain quarterly-period token, ≥2 numeric currency figures, ticker-shaped token. The transcript content itself supplies these; the Research note's restating Evidence section should also contain them.

On verification failure:

• Structural failure → delete the partial Research note, log failure, abort the entire skill run with diagnostic.
• Content-quality failure → delete the Research note (treat as contaminated, not partial), report which check failed with diagnostic (body words, source words, missing tokens). Retain the raw transcript cache so user can re-run after fixing the analysis prompt or content-extraction logic.

Step 8: Append thesis Log entry

Edit $THESIS_PATH's ## Log section. Append:

### YYYY-MM-DD
- Transcript ingested: [QN-YYYY] — [most-significant thesis-delta finding from Step 6 in plain prose, 1 sentence]. See [[Research/YYYY-MM-DD - TICKER QN-YYYY - earnings]].

For --diff mode:

### YYYY-MM-DD
- Transcript diff: [Q1] vs [Q2] — [most-significant shift in plain prose]. See [[Research/YYYY-MM-DD - TICKER Q1 vs Q2 - transcript diff]].

Prefix Transcript ingested: (or Transcript diff: for diff mode) is intentionally NON-skill-origin. This is a research-driven Log entry — the Research note created in Step 7 represents real new analytical content that should propagate to sectors and macro notes. /sync Step 2.5 will treat the change as research-driven and run Steps 3-5 normally.

Do NOT add this prefix to _shared/log-prefixes.md skill-origin list. Adding it would silently break /sync propagation from every earnings transcript ingest — exactly the wrong behavior.

Step 9: Graph-primer propagation fanout

Per .claude/skills/_shared/graph-primer.md Mode C (Propagation-fanout primer — same as /ingest Step 3.5).

Using _graph.md (already read in Step 5):

• T = TICKER
• S = thesis's sector: from frontmatter
• M = macro references found in the Research note's body (parse [[Macro & Technology/...]] wikilinks)

Compute:

• direct_targets = {THESIS_PATH} (the ticker's own thesis — always a direct target)
• sector_candidates = sector_reverse[S] - direct_targets
• macro_candidates = ∪{macro_reverse[m]} for m in M - direct_targets - sector_candidates

Surface to user in Step 12 report. Wikilinks for direct_targets are already in the Research note (the thesis link in ## Thesis Delta and ## Source Excerpts). For sector_candidates and macro_candidates — these are advisory; the user reviews and adds wikilinks before /sync if they want propagation to those neighbors.

Missing-graph fallback: per contract — log ℹ️ _graph.md absent/unparseable — graph primer skipped and proceed. Step 8's Log entry + Step 7's Research note still propagate via /sync based on body-content grep alone.

Step 10: Update _hot.md

Follow .claude/skills/_shared/hot-md-contract.md. Read first (already read in Step 5), then edit. Do NOT touch Latest Sync / Sync Archive.

1. Active Research Thread: per contract's same-ticker-continuation rule — likely set to [TICKER] earnings analysis (QN-YYYY).
2. Open Questions: if Step 6.3 surfaced touched conviction triggers OR Step 4.5 surfaced rising Q&A skepticism, append 1-2 open questions for /surface later: - [TICKER] QN: [trigger touched in direction X] — re-evaluate conviction at next data point.
3. Latest Sync and Sync Archive: do NOT touch (owned by /sync).
4. Recent Conviction Changes: do NOT touch (a transcript ingest is research input, not a conviction decision — the user calls /status if a conviction change is warranted).

Cap enforcement: per contract — apply drops if over soft cap; abort _hot.md update if over hard cap (DON'T block the primary /transcript operation).

Step 11: Material-finding advisories

If Step 6 surfaced any of:

• A conviction trigger fired toward LOW or CLOSE → suggest /status [TICKER] [field] [current]→[new] with the rationale phrased from the trigger
• A conviction trigger fired toward HIGH → suggest /status [TICKER] conviction [current]→high
• Bear-case risk strengthened by ≥2 evidence vectors → suggest /stress-test [TICKER]
• Bull-case driver weakened by ≥2 evidence vectors → suggest /deepen [TICKER] --section "Bull Case"
• Q&A skepticism +50% vs. prior 2-Q avg → suggest /stress-test [TICKER] (analyst skepticism leading official conviction is a real edge)

These print as → Consider: [command] lines in the Step 12 report. Never auto-run.

Step 12: Release lock and report

Final Bash block — lock release per preflight §1.7.

LOCK_FILE="<paste-from-Step-0.1>"
EXPECTED_TOKEN="<paste-token-captured-from-Step-0.1>"
if [ -f "$LOCK_FILE" ] && grep -q "token: $EXPECTED_TOKEN" "$LOCK_FILE"; then
  rm -f "$LOCK_FILE" && echo "=== LOCK RELEASED ($LOCK_FILE) ==="
else
  echo "⚠️ Lock ownership check failed at release — skipping rm."
fi
# Keep cache (.data/transcripts/*) — useful for diff mode re-runs
# Only clear /tmp scratch
rm -f /tmp/transcript_*.json /tmp/transcript_dates_*.json

Report

✓ /transcript [TICKER] [QN-YYYY | --diff Q1 Q2] complete

Research note:    [[Research/YYYY-MM-DD - TICKER QN-YYYY - earnings]]
Transcript date:  YYYY-MM-DD (Q[N] FY[YYYY])
Transcript size:  ~[N] words
Prior comparators: [Q-1, Q-2]

Thesis delta findings:
  Bull case:       [strengthened | weakened | unchanged]  — [1-line summary]
  Bear case:       [strengthened | weakened | unchanged]  — [1-line summary]
  Triggers touched: [N]  ([list with direction])

Signal extracts (most analytically interesting):
  New framing introduced: [top 3 phrases]
  Dropped framing:        [top 3 phrases]
  Hedging shift:          [direction, %]
  Q&A skepticism shift:   [direction, %]
  Touched trigger(s):     [list, or "none"]

Graph primer:
  Direct targets:      [list of theses]
  Sector candidates:   [list of theses sharing sector — review for wikilinking]
  Macro candidates:    [list of theses sharing macro refs — review for wikilinking]

Thesis Log: appended ("Transcript ingested:") — research-driven prefix
_hot.md: updated (Active Research Thread, Open Questions if applicable)
Transcript cache: .data/transcripts/TICKER_QN-YYYY.json (retained for diff mode re-runs)

Suggested next steps:
  1. /sync [TICKER]          — propagate to sector + macro notes
  2. /graph last             — reconcile adjacency
  → Consider: [Step 11 advisories, if any]

Design constraints (xxx DO NOT VIOLATE xxx)

1. Earnings transcripts require an existing thesis. Step 0.3 hard-aborts when no thesis exists. The whole value of /transcript is delta-against-thesis-context; orphan-earnings ingestion is /ingest's job.

2. The Log prefix Transcript ingested: is NOT skill-origin. It must propagate via /sync because the Research note carries genuine new analytical content (management framing shifts, trigger touches) that affects sector competitive-dynamics framing and macro thread continuity. Adding this prefix to _shared/log-prefixes.md skill-origin list silently disables every transcript's propagation.

3. Heuristic signals are flagged as heuristics in the Research note. Hedging density, evasiveness percentage, skeptical-keyword density are all imperfect text-frequency measures. The Research note must explicitly caveat each (especially evasiveness, which is highly false-positive on disclosure-restricted topics).

4. Transcript cache is gitignored. .data/transcripts/ lives under .data/ which is in .gitignore per Live Portfolio's documentation. Re-runs read from cache; first runs populate it. No transcript content ever lands in version control.

5. Diff mode is a separate output shape. Treating diff as "two ingests then a manual side-by-side" loses the value — the cross-quarter Evidence table is the primary deliverable. Don't conflate the modes.

6. ## Key Segments section is required for transcripts >15,000 words. Per /ingest content-quality check #5. Most US large-cap earnings calls cross this threshold. The 5-segment structure (Strategic Framing, Segment Discussion, Forward Construction, Most-Skeptical Exchange, New Information) reliably captures a 60-90 minute call's analytical substrate.

7. Wholesale tier is the assumed entitlement. The earnings-transcript endpoint is gated on FMP wholesale tier (or higher). Lower tiers return 403/404 on this endpoint — the skill aborts cleanly with an FMP error rather than silently degrading.

8. Never re-fetch when cache is fresh. First-pass writes to .data/transcripts/TICKER_QN-YYYY.json. Subsequent runs (e.g., user iterating on prompt phrasing for the Research note) read cache. FMP wholesale has generous rate limits but every avoided fetch is one less API touchpoint and ~3-8 seconds faster per run.