Create KSVC-validated Twitter content from research PDFs. Content types: long threads, quick takes, breaking news, shitposts, personal commentary, victory laps. Triggers on "create content", "write thread", "make a post", "shitpost", or when working with PDFs in /Users/Shared/ksvc/pdfs. REQUIRED STEPS - (1a) Explore agents to scan PDFs, (1b) RLM for deep extraction, (1c) Cross-doc synthesis with rlm-multi, (2) KSVC holdings check (preliminary), (3) write data backbone, (4a) AUDIT with RLM verification, (4a.5) Gemini web cross-validation for FAIL/UNSOURCED inferences, (4b) Final holdings verification (ALL 7 models), (4c) Stylize with kirk-mode skill, (4d) humanizer pass, (5) save draft, (6) chart decision & generation.
Create KSVC-validated Twitter content from research PDFs. Content types: long threads, quick takes, breaking news, shitposts, personal commentary, victory laps. Triggers on "create content", "write thread", "make a post", "shitpost", or when working with PDFs in /Users/Shared/ksvc/pdfs. REQUIRED STEPS - (1a) Explore agents to scan PDFs, (1b) RLM for deep extraction, (1c) Cross-doc synthesis with rlm-multi, (2) KSVC holdings check (preliminary), (3) write data backbone, (4a) AUDIT with RLM verification, (4a.5) Gemini web cross-validation for FAIL/UNSOURCED inferences, (4b) Final holdings verification (ALL 7 models), (4c) Stylize with kirk-mode skill, (4d) humanizer pass, (5) save draft, (6) chart decision & generation.
Kirk Content Pipeline
Create Twitter content from analyst research PDFs, validated against KSVC holdings.
Pipeline Steps (MANDATORY)
1a. Scan PDFs (Explore agents for broad screening)
1b. Extract insights (RLM for deep extraction - text, tables, AND charts)
1c. Cross-doc synthesis (rlm-multi for insights across sources)
2. Check KSVC holdings (preliminary - with known tickers)
3. Write content (data backbone, Serenity-heavy)
4a. AUDIT (verify draft claims against source PDFs with RLM)
4a.5. GEMINI CROSS-VALIDATION (web-verify FAIL/UNSOURCED inferences)
4b. Final Holdings Verification (check ALL 7 models with discovered tickers)
4c. Stylize (invoke kirk-mode skill for voice/character)
4d. Humanize (remove AI patterns)
5. Save draft for approval
6. Chart decision & generation (after draft crystallizes thesis)
7. PUBLISH to final folder (clean version for posting)
Never skip steps 4a-4d. Use 1a for multi-PDF screening, 1b for deep extraction, 1c for cross-doc synthesis, 4a for verification, 4a.5 for web cross-validation, 4b for final holdings check, 4c for character voice, 4d for AI pattern removal.
⚠️ CRITICAL: Step 1b extracts data. Step 1c synthesizes across docs. Step 4a VERIFIES the written content. Step 4a.5 CROSS-VALIDATES inferences.
1b: "What does each PDF say?" (per-doc extraction)
1c: "What patterns emerge across PDFs?" (cross-doc synthesis)
4a: "Does my draft accurately reflect the sources?" (source-locked verification)
4a.5: "Are the flagged inferences valid per public sources?" (web cross-validation)
4c: "Which Kirk mode fits this situation?" (character voice)
Subagent Permissions (CRITICAL)
Subagents CANNOT Read files outside the project directory. PDFs in /Users/Shared/ksvc/pdfs/ are blocked. The fix: symlink PDFs into the project directory before spawning subagents.
The main agent MUST create a symlink before Step 1a:
Then subagents Read from .claude/pdfs-scan/filename.pdf — this works because the path resolves inside the project.
Access Method
/Users/Shared/ path
Symlinked project path
Subagent Read tool (PDF)
❌ Auto-denied
✅ Works
Subagent Read tool (images)
❌ Auto-denied
✅ Works
Main agent Read tool
✅ User approves
✅ Works
Bash → RLM
✅ Any path
✅ Any path
Discovered 2026-02-07: Subagents fail with "Permission to use Read has been auto-denied (prompts unavailable)" on /Users/Shared/ paths. Symlink into project dir = full Read access. Tested: 19 PDFs, medium thoroughness, 125k tokens, zero errors.
Content Types & Voice Blends
Full guide:references/kirk-voice.md — Read this for templates and examples.
Breaking News: "Huge." / "Well well well..." → Key number → Source
Victory Lap: "$TICKER up X% since KSVC added it" → Entry/Now → Thesis validated
Step 1a: Scan PDFs with Explore Agents
Use Explore agents for broad screening when you have many PDFs to review. This is faster than RLM for initial discovery.
Step 1a.0: Check Published Threads (MANDATORY - DO FIRST)
⚠️ Before scanning any PDFs, check what Kirk has already posted.
# List all published threads
ls /Users/Shared/ksvc/threads/
# Read recent thread.md files to understand what topics are covered
For each published thread, note:
Topic (what was the thesis?)
Source PDFs used (check _metadata.md)
Date (how recent?)
Then when selecting a topic after scanning, REJECT any topic that:
Uses the same primary source PDF as a published thread
Covers the same thesis/angle (even if from different sources)
Would read as a repeat to Kirk's followers
Acceptable overlap:
A follow-up/update to a previous thread with NEW data (e.g., earnings confirm the thesis)
A different angle on the same sector (e.g., posted about ABF shortage, now posting about specific company earnings)
Explicitly framed as "update: here's what changed since my last post on X"
Why this exists (Case Study — ABF Substrate, 2026-02-07):
Kirk published a 10-tweet thread on Feb 5 covering Goldman's ABF shortage report (10%→21%→42%, Kinsus/NYPCB/Unimicron). On Feb 7, the pipeline picked the same Goldman report and produced a 3-tweet quick take with the same numbers, same companies, same angle. We didn't check published threads first, so we wasted a pipeline run on duplicate content when 10 other fresh topic angles were available.
When to Use
Screening 10+ PDFs to find relevant ones
Finding cross-document connections
Building a thesis from multiple sources
Don't know which PDFs matter yet
How to Scan
1. Check published threads (Step 1a.0 above)
2. List recent PDF folders and count PDFs
ls /Users/Shared/ksvc/pdfs/ | tail -5
ls /Users/Shared/ksvc/pdfs/YYYYMMDD/ | wc -l
3. Symlink PDFs into project directory (REQUIRED for subagent access)
ln -sf "/Users/Shared/ksvc/pdfs/YYYYMMDD" ".claude/pdfs-scan"
4. Split PDFs into groups and spawn parallel Explore agents
TARGET: ~5 PDFs per agent. Spawn ALL agents in a single message.
- Each agent gets a specific list of filenames to scan
- All agents run simultaneously → total time = slowest agent
- Haiku is cheap — more agents = faster with no meaningful cost increase
Agent Sizing
PDFs
Agents
PDFs/Agent
Expected Time
≤5
1
all
~25s
6-10
2
~5 each
~25s
11-15
3
~5 each
~25s
16-20
4
~5 each
~25s
21-30
5-6
~5 each
~30s
Why ~5 PDFs per agent? Sweet spot for speed. Each PDF takes ~4-8s to Read + summarize. 5 PDFs ≈ 25s per agent. Adding more PDFs per agent saves nothing (same total tokens) but makes wall-clock time worse.
Cost: Haiku is cheap. 4 agents × 5 PDFs × ~4k tokens = ~80k input tokens total — same as 1 agent doing all 20. Parallelism is free.
Cross-doc synthesis trade-off: Each agent only sees its batch, so cross-batch themes are the main agent's job. This is fine — the main agent merges all results anyway.
Example: Spawn Explore Agents
Step 1: Main agent creates symlink and lists PDFs:
Step 2: Split filenames into groups and spawn agents in parallel (single message, multiple Task calls):
# Agent 1 — first batch
Task(subagent_type="Explore", prompt="""
**THOROUGHNESS: medium**
Scan these specific PDFs for content angles:
- file1.pdf
- file2.pdf
- file3.pdf
- file4.pdf
- file5.pdf
- file6.pdf
- file7.pdf
For each PDF, Read enough pages to understand the full thesis (use judgment — some need 1-2 pages, others 1-5):
Read(file_path="/Users/dydo/Documents/agent/ksvc-intern/.claude/pdfs-scan/FILENAME.pdf", pages="1-5")
For each PDF extract:
- Company/sector, ticker, rating, price target
- Key thesis and supporting numbers
- Supply chain connections
- Potential content angles
After scanning your batch, provide:
1. Per-PDF summary (2-3 sentences each)
2. Cross-document themes within your batch
3. Which PDFs are most relevant for deep extraction
""")
# Agent 2 — second batch (SPAWN IN SAME MESSAGE as Agent 1)
Task(subagent_type="Explore", prompt="""
... same prompt with file8.pdf through file14.pdf ...
""")
# Agent 3 — third batch (SPAWN IN SAME MESSAGE)
Task(subagent_type="Explore", prompt="""
... same prompt with file15.pdf through file20.pdf ...
""")
Step 3: Main agent synthesizes results from all agents:
After all agents return, the main agent:
⚠️ WARNING: Explore agents can hallucinate specific numbers. Treat all numbers from Explore summaries as "unverified claims" until RLM grep confirms them. Component counts, percentages, and market sizing are especially prone to errors.
Capacity (tested 2026-02-07): Single Explore agent (haiku) handled 19 PDFs at medium thoroughness in 83 seconds, using 125k tokens (~4k tokens/PDF for pages 1-5). 3 agents in parallel = ~30-40s for the same batch.
Step 1b: Deep Extract with RLM
Use RLM for deep extraction from specific PDFs you've identified in Step 1a.
MANDATORY for any number you'll publish. Explore agents summarize; RLM verifies.
# List images from a context
python3 rlm_repl.py exec --name report1 -c "print(list_images())"
# Get image path, then use Read tool to view
python3 rlm_repl.py exec --name report1 -c "print(get_image(0))"
Charts often contain key data (P/B trends, margin history, capacity timelines) that text extraction misses.
Extraction Validation (MANDATORY)
⚠️ After EVERY rlm_repl.py init, validate the extraction actually worked.
RLM reports chars_extracted after init. A multi-page analyst report should yield thousands of chars. If you get suspiciously few, the PDF is likely image-based and RLM only extracted metadata/headers.
Validation rule:
Chars Extracted
Expected Report Type
Action
> 5,000
Multi-page report
✅ Proceed with grep
1,000 - 5,000
Short note / partial
⚠️ Check list_images() — if many images, trigger fallback
< 1,000
Image-based PDF
❌ MUST use Read tool fallback
The threshold is context-dependent. A 20-page Goldman Sachs report yielding 666 chars is obviously broken. A 1-page pricing table yielding 800 chars might be fine. Use judgment, but when in doubt, fallback.
Mandatory Fallback when RLM extraction is low:
# Step 1: RLM init (always try first)
python3 rlm_repl.py init "/path/to/report.pdf" --extract-images
# Output: "Extracted 666 chars from 15 pages, saved 9 images"
# Step 2: Check - is 666 chars enough for a 15-page report? NO.
# → Trigger fallback
# Step 3: Check extracted images first (they may contain the data)
python3 rlm_repl.py exec -c "print(list_images())"
# View extracted images with Read tool
# Read(file_path="/path/to/extracted/image-0.png")
# Step 4: Read the PDF directly (use symlinked path for subagents)
# Read(file_path=".claude/pdfs-scan/report.pdf", pages="1-10")
# Read(file_path=".claude/pdfs-scan/report.pdf", pages="11-20")
⚠️ Path rule: Subagents must Read PDFs via the symlinked project path (.claude/pdfs-scan/), NOT from /Users/Shared/. See "Subagent Permissions" section above.
Why this exists (Case Study — ABF Substrate Shortage, 2026-02-07):
Goldman Sachs published two reports: a main ABF upcycle report (71K chars, extracted fine) and a Kinsus upgrade report (15 pages, but only 666 chars extracted). We skipped the Kinsus PDF because "the main report had everything we needed." It didn't. The Kinsus report had unique data (company-specific capacity plans, margin guidance, order book details) that would have strengthened the thread. Skipping it was lazy — the Read tool fallback takes 30 seconds and would have recovered the data.
Rules:
Never skip a relevant PDF just because RLM extraction was low. Use the fallback.
Check extracted images. RLM with --extract-images often saves chart/table images even when text extraction fails. View them with Read tool.
Log the fallback. In the extraction cache, note "extraction_method": "read_fallback" so audit knows the data source.
If fallback also fails (corrupted PDF, DRM), document it and move on. But you must TRY.
RLM Cache: Include Visual Data
When extracting, capture all data types for potential chart generation later:
Why cache visual data? Step 6 (chart generation) needs this. If you only cache text, you'll lose table structures and chart data points that make great visualizations.
Cross-Document Reasoning
Build thesis by triangulating claims across multiple reports:
# Find where multiple reports discuss the same topic
python3 rlm_repl.py exec -c "results = grep_all('DRAM.*price|ASP', max_matches_per_context=5)"
# Compare forecasts across sources
python3 rlm_repl.py exec -c "results = grep_all('2026|2027|growth|demand', max_matches_per_context=5)"
Use cross-doc to verify:
Do multiple sources agree on price forecasts?
Are supply constraint timelines consistent?
Any contradictions between reports?
Step 1b.5: Build Extraction Cache (MANDATORY)
⚠️ Why this step exists: RLM creates state.pkl during extraction, but the writing phase (Step 3) doesn't access it. Without a persistent cache, writers rely on memory, leading to errors like wrong product types, missing time periods, or source attribution mistakes.
What this does: Extracts from state.pkl (RLM's internal format) into structured JSON with context labels that the writing phase can reference.
When to Run
After Step 1b (RLM extraction) and before Step 3 (writing).
Workflow
When to Cache
Single PDF (rlm-repl)
After rlm_repl.py init completes
Multiple PDFs (rlm-repl-multi)
After all init commands complete
How to Build Cache
New in v2: Auto-generates source tags and attribution map from PDF filenames!
Single PDF (rlm-repl):
cd ~/.claude/skills/kirk-content-pipeline/scripts
# Auto-extracts from default rlm-repl state location
python3 build_extraction_cache.py \
--output /path/to/draft-assets/rlm-extraction-cache.json
Multiple PDFs (rlm-repl-multi):
cd ~/.claude/skills/kirk-content-pipeline/scripts
# Use --multi flag to load from rlm-repl-multi state
python3 build_extraction_cache.py \
--multi \
--output /path/to/draft-assets/rlm-extraction-cache.json
See:~/.claude/skills/kirk-content-pipeline/scripts/README-extraction-cache.md for full documentation.
Step 1c: Cross-Doc Synthesis (RECOMMENDED)
Why this step exists: Steps 1a and 1b produce per-document facts. Without explicit synthesis, the pipeline gravitates toward single-source claims ("KHGEARS P/E is 20x") rather than cross-doc insights ("Taiwan brokers are more bullish than Western analysts on humanoid robotics").
When to Use
Scenario
Use 1c?
Multiple PDFs on same topic
Yes
Comparing broker views
Yes
Finding consensus/disagreement
Yes
Single PDF deep dive
No (skip to Step 2)
Breaking news (speed matters)
No (skip to Step 2)
What 1c Produces
Output Type
Example
Audit Requirement
Consensus claim
"3 of 4 brokers see DRAM ASP rising in 2H26"
Cross-doc (rlm-multi)
Comparative insight
"HIWIN at 38x vs KHGEARS at 20x - market pricing in certainty"
Cross-doc (rlm-multi)
Disagreement flag
"MS says neutral, local brokers say buy - who's right?"
Do sources agree on [market size / timeline / key risk]?
Comparison
How does [broker A] view differ from [broker B]?
Valuation
Are local vs foreign analysts pricing the same?
Timeline
Do sources agree on [catalyst / inflection point]?
Risk
What risks does one source mention that others miss?
Output Format: Synthesis Cache
After running 1c, document synthesized insights for Step 3 (writing):
## Cross-Doc Synthesis (Step 1c)
**Sources:** broker1 (永豐), broker2 (MS), broker3 (Citi)
### Consensus
- Market size: All 3 agree on $5-6B (2025) → $30-35B (2029)
- CAGR: 55-60% range across all sources
### Disagreements
- HIWIN: MS says NEUTRAL (38x too rich), 永豐 silent, Citi no coverage
- Timeline: 永豐 more bullish on 2026 ramp, MS cautious until 2027
### Comparative Insights (use in thread)
- "Taiwan brokers (永豐) bullish on KHGEARS; Western analysts (MS) more cautious on HIWIN"
- "Local coverage sees 2026 inflection; foreign houses waiting for 2027 proof points"
### Audit Flag
These synthesized claims require cross-doc verification in Step 4b:
- [ ] "3 sources agree on market size" → verify all 3 sources
- [ ] "Local vs foreign view divergence" → verify specific ratings from each
Integration with Audit (Step 4a)
⚠️ CRITICAL: Synthesized claims from Step 1c MUST be flagged for cross-doc audit in Step 4a.
In the audit manifest, mark these claims with cross-doc: true:
Cross-doc claims use rlm-repl-multi for verification, not parallel single-doc agents.
Extract with Technical Specificity
Go beyond surface numbers. Extract:
Wafer capacity (WPM)
Fab names (M15X, P4L, X2)
Yield percentages
Process nodes (1b, 1c)
Component counts per unit
Question
Extract
What
One-sentence summary
Why
Why readers should care
Who
Companies/tickers affected
When
Timeline (specific quarters)
Where
Fab locations, geography
How
Mechanism with technical detail
Step 2: Check KSVC Holdings (Initial)
⚠️ CRITICAL: This is a preliminary check. You MUST run Step 4c (Final Holdings Verification) after writing content to catch any tickers discovered during extraction.
Before querying the API, identify ALL possible identifiers for the company:
# Example: Global Unichip Corp
# Identifiers to search:
# - US ticker: N/A (not US-listed)
# - Taiwan ticker: 3443
# - Chinese name: 創意 or 全球晶圓科技
# - English name: Global Unichip, GUC
# - Stock code: 3443 TW (TWSE format)
# For Taiwan stocks, verify ticker via TWSE API first:
curl -s "https://www.twse.com.tw/en/api/codeQuery?query=3443"
# Returns: {"query":"3443","suggestions":["3443\tGUC"]}
Rules:
US stocks: Search by ticker only (e.g., "MU", "AMD", "NVDA")
Taiwan stocks: Search by stock code (e.g., "3443") - may appear as "3443 創意" in API
If unsure: Check both US and TWSE models
Step 2b: Query All 7 Models
NEVER assume a stock isn't held without checking ALL 7 models.
RECOMMENDED: Use tradebook for accurate entry prices and current status
# FASTEST METHOD: Check tradebook for entry price + status
# (Works for all models - US and TWSE)
curl -s "https://kicksvc.online/api/twse-model2" | \
jq '.tradebook[] | select(.ticker == 6285 or .ticker == 3491) |
{ticker, enterDate, enterPrice, todayPrice, profitPercent, exitDate}'
# Returns:
# {
# "ticker": 6285,
# "enterDate": "Wed, 28 Jan 2026 00:00:00 GMT",
# "enterPrice": 162.0,
# "todayPrice": 207.5, # ⚠️ May be stale! Use Yahoo Finance for current
# "profitPercent": 28.09, # ⚠️ Based on stale todayPrice
# "exitDate": null # null = still holding
# }
⚠️ CRITICAL: API's todayPrice and profitPercent can be STALE (hours or days old). Always verify current price with Yahoo Finance API (Step 2d).
FALLBACK: Check equitySeries (slower, less data)
# Check ALL 5 US models
for i in 1 2 3 4 5; do
echo "=== USA-Model $i ==="
curl -s "https://kicksvc.online/api/usa-model$i" | \
jq --arg t "MU" '.equitySeries[0].series[] | select(.Ticker == $t) |
{ticker: .Ticker, return: .data[-1].value}'
done
# Check ALL 2 TWSE models (search by stock code)
for i in 1 2; do
echo "=== TWSE-Model $i ==="
curl -s "https://kicksvc.online/api/twse-model$i" | \
jq '.equitySeries[0].series[] | select(.Ticker | contains("3443")) |
{ticker: .Ticker, return: .data[-1].value}'
done
Why still use equitySeries?
Historical tracking: Shows return % evolution over time (.data[] array)
Verification: Confirms position is still active
Fallback: If tradebook is unavailable or empty
Entry date discovery: First data point (return ≈ 0) indicates entry date
Example: Finding entry date from equitySeries
# Get all data points to find entry date
curl -s "https://kicksvc.online/api/twse-model2" | \
jq '.equitySeries[0].series[] | select(.Ticker | contains("6285")) | .data[0]'
# Returns: {"date": "2026-01-28 00:00:00", "value": 0}
# Entry date: Jan 28, 2026
Step 2c: Verification and Fallback Strategy
Use all three data sources for robustness:
Data Source
When to Use
What It Shows
Limitation
tradebook
Primary
Entry date, entry price, exit status
todayPrice may be stale
equitySeries
Verification
Return % over time, position status
No entry price/date
filledOrders
Fallback
Actual trade orders, prices
Empty if model didn't reset recently
Recommended workflow:
# 1. PRIMARY: Get entry details from tradebook
TRADEBOOK=$(curl -s "https://kicksvc.online/api/twse-model2" | \
jq '.tradebook[] | select(.ticker == 6285)')
# 2. VERIFY: Cross-check with equitySeries
EQUITY=$(curl -s "https://kicksvc.online/api/twse-model2" | \
jq '.equitySeries[0].series[] | select(.Ticker | contains("6285"))')
# 3. FALLBACK: If tradebook empty, check filledOrders
if [ -z "$TRADEBOOK" ]; then
curl -s "https://kicksvc.online/api/twse-model2" | \
jq '.filledOrders[] | select(.ticker | contains("6285"))'
fi
Cross-verification example:
# Check if tradebook and equitySeries agree on position status
TRADEBOOK_HELD=$(curl -s "https://kicksvc.online/api/twse-model2" | \
jq '.tradebook[] | select(.ticker == 6285 and .exitDate == null) | .ticker')
EQUITY_HELD=$(curl -s "https://kicksvc.online/api/twse-model2" | \
jq '.equitySeries[0].series[] | select(.Ticker | contains("6285")) | .Ticker')
# If both show position, high confidence
# If only one shows position, investigate discrepancy
Fallback: Check filledOrders (if tradebook empty)
If equitySeries is empty OR tradebook is empty (rare, but possible after model reset):
# Check ALL US models - filledOrders
for i in 1 2 3 4 5; do
echo "=== USA-Model $i filledOrders ==="
curl -s "https://kicksvc.online/api/usa-model$i" | \
jq '.filledOrders[] | select(.ticker == "MU") | {ticker, price, quantity}'
done
# Check ALL TWSE models - filledOrders
for i in 1 2; do
echo "=== TWSE-Model $i filledOrders ==="
curl -s "https://kicksvc.online/api/twse-model$i" | \
jq '.filledOrders[] | select(.ticker | contains("3443")) | {ticker, price, quantity}'
done
When data sources disagree:
Scenario
Action
tradebook shows position, equitySeries doesn't
Trust tradebook (equitySeries may lag)
equitySeries shows position, tradebook doesn't
Investigate - check filledOrders
filledOrders shows buy but no current position
Position was closed - check tradebook.exitDate
All three empty
Position not held in this model
Step 2e: Document Holdings with Accurate Returns
CRITICAL: Always calculate actual returns using:
Entry price from tradebook.enterPrice
Current price from Yahoo Finance API (NOT KSVC API's stale todayPrice)
Output format (with accurate data):
**KSVC Holdings Check:**
- ✅ WNC (6285.TW) - Held in TWSE Model 2
- Entry: Jan 28, 2026 @ NT$162
- Current: NT$187 (Yahoo Finance)
- Gain: +15.4% (actual, not API's stale 28%)
- ✅ UMT (3491.TWO) - Held in TWSE Model 2
- Entry: Jan 28, 2026 @ NT$1,120
- Current: NT$1,280 (Yahoo Finance)
- Gain: +14.3% (actual, not API's stale 23%)
- ❌ Not held in TWSE Model 1 or USA Models 1-5
**Note:** API's equitySeries and tradebook.todayPrice can lag hours/days behind market.
Always use Yahoo Finance for current prices.
If NOT held in any model:
**KSVC Holdings Check:**
- ❌ Not held in any of 7 models (checked USA 1-5, TWSE 1-2)
- Content angle: Industry analysis / Market observation
Integration Strategies
Situation
Approach
Example
Held (US)
Call out position
"KSVC Model1 holds $MU at $412 entry"
Held (TW)
Call out position
"KSVC台股Model1持有台積電 (2330)"
Not held
Industry framing
"Memory cycle benefits $MU, SK Hynix"
Win
Victory lap
"$MU +15% since Model1 added it"
Step 2d: Current Price Check (Yahoo Finance API - REQUIRED)
⚠️ CRITICAL: ALWAYS use Yahoo Finance for current prices. KSVC API's todayPrice can be stale.
US stocks:
# Get current price
TICKER="MU"
curl -s -A "Mozilla/5.0" "https://query1.finance.yahoo.com/v8/finance/chart/$TICKER?interval=1d&range=1d" | \
jq '.chart.result[0].meta.regularMarketPrice'
# Get full market data
curl -s -A "Mozilla/5.0" "https://query1.finance.yahoo.com/v8/finance/chart/$TICKER?interval=1d&range=1d" | \
jq '.chart.result[0].meta | {symbol, regularMarketPrice, currency, regularMarketTime}'
# 1. Get entry price from tradebook
ENTRY=$(curl -s "https://kicksvc.online/api/twse-model2" | jq '.tradebook[] | select(.ticker == 6285) | .enterPrice')
# 2. Get current price from Yahoo Finance
CURRENT=$(curl -s -A "Mozilla/5.0" "https://query1.finance.yahoo.com/v8/finance/chart/6285.TW?interval=1d&range=1d" | jq '.chart.result[0].meta.regularMarketPrice')
# 3. Calculate actual gain
echo "Entry: NT\$$ENTRY | Current: NT\$$CURRENT | Gain: $(awk "BEGIN {printf \"%.1f\", ($CURRENT - $ENTRY) / $ENTRY * 100}")%"
Step 3: Write Content
See references/kirk-voice.md for full templates and examples.
Thread Numbering Convention
Format
When to Use
No number on Tweet 1
Recommended - cleaner hook, stands alone if quoted/shared
2/, 3/, etc.
Standard thread format - signals "2 of N"
1/ on first tweet
Optional - explicit "thread incoming" signal
Why skip number on first tweet:
Hook tweet often gets shared standalone
"1/" makes it look incomplete out of context
Cleaner visual presentation
Format preference: Use / not ) - it's the established Twitter thread convention.
✅ Recommended:
Humanoid robots going from science fair to factory floor. Taiwan supply chain getting interesting.
2/ TLDR:
- Market: $5.3B (2025) to $32.4B (2029)...
❌ Avoid:
1/ Humanoid robots going from science fair...
Pick Content Type
What kind of content? (Thread / Quick Take / Breaking / Shitpost / Commentary / Victory Lap)
Look up the formula in kirk-voice.md
Apply the blend
Technical Specificity
❌ Vague: "NAND supply is tight"
✅ Specific: "YMTC adding 135k WPM at Wuhan Fab 3. Still won't close the gap - Samsung X2 conversion delayed to Q2."
❌ Vague: "HBM margins are good"
✅ Specific: "SK Hynix HBM yields at 80-90%. Samsung stuck at 60% on 1c DRAM."
Always include: specific numbers, time frames, fab names, comparisons.
Referential Clarity (Learned 2026-02-08)
Never use vague pronouns or shorthand when the referent hasn't been introduced.
In thread format, each tweet may be read semi-independently. If earlier tweets discuss a concept as a category (e.g., "ASIC revenue"), don't suddenly refer to it as "the project" in a later tweet — the reader has no antecedent for "the project."
❌ Vague: "MS thinks the project is the 3nm Google TPU"
(What project? The thread never introduced "a project.")
✅ Clear: "MS thinks the main client/program is the 3nm Google TPU"
(Names what MS is identifying — who's buying and what they're building.)
Rule: When a shorthand ("the project", "this deal", "the play") saves words but costs clarity, it's not saving anything. Name the thing directly. A few extra words that prevent the reader from pausing to re-read are always worth it.
When shifting from category to specific: If the thread discusses an abstract category (ASIC revenue, memory supply) and then pivots to a specific entity (Google TPU, Samsung fab), bridge the transition. Don't assume the reader already knows which specific thing drives the category.
Step 4a: Audit (MANDATORY — MUST USE SUBAGENTS)
⚠️ WHY THIS STEP EXISTS: We learned that RLM extraction (Step 1b) is not the same as verification. Explore agents hallucinate numbers. Writers make inferences. This step catches errors BEFORE publishing.
⚠️ STRUCTURAL GATE: You (the main agent) are the WRITER. You cannot also be the AUDITOR. You MUST delegate audit to fresh-context subagents. See the "WARM STATE TRAP" section in the audit-content skill for why.
Step 4a Process (3 actions, in order)
Action 1: Generate audit manifest
Write audit-manifest.md with all claims, sources, and search hints.
This is the handoff document for the audit agents.
Action 2: Spawn Explore agents (MANDATORY — do NOT skip this)
Spawn 1 Explore agent per source PDF via Task tool.
Each agent gets: the manifest + its assigned PDF path + claim list.
Each agent returns: JSON with PASS/FAIL/UNSOURCED per claim.
⚠️ WARM STATE TRAP: If RLM is already loaded from Step 1b, you WILL be tempted to "just grep it yourself." DO NOT. The audit-content skill explains why: you wrote the draft, so you already "know" the answers. Self-auditing is confirmation bias, not verification.
Self-check: If you are about to type rlm_repl.py exec during Step 4a, STOP. You are skipping the gate.
Action 3: Collect results and write audit report
Aggregate agent results into audit-report.md.
MUST include audit_agent_ids from the Task tool responses.
If audit_agent_ids is empty, the audit is invalid.
Invoke the audit-content skill for full process details:
/audit-content
What Gets Verified
Claim Type
Example
How to Verify
Company names
"KHGEARS"
RLM grep + TWSE API
Ticker formats
"4571 TW"
TWSE API
Numbers
"62 harmonic reducers"
RLM grep exact count
Percentages
"19% cost"
RLM grep in source
P/E ratios
"20x"
RLM grep analyst target
Ratings
"BUY"
RLM grep recommendation
Timelines
"2H27"
RLM grep + verify context
Attributions
"shipping to X"
Must be explicit in source, not inferred
When to Proceed
All PASS: Save draft (Step 5)
Any FAIL: Fix the claim, re-audit
UNSOURCED: Either remove, add caveat ("reportedly"), or find source
Do NOT save draft with FAIL status. UNSOURCED claims need explicit decision.
Step 4a.5: Gemini Web Cross-Validation (RECOMMENDED)
⚠️ WHY THIS STEP EXISTS: RLM audit (Step 4a) is source-locked — it only checks claims against the cited PDF. This over-flags reasonable inferences that go beyond one report but are well-documented publicly. Step 4a.5 gives flagged claims a second chance via web-grounded search.
Case Study (Old Memory Squeeze, 2026-02-07):
Draft said "capacity getting cannibalized for HBM and DDR5"
RLM audit FAIL: MS report says "exiting DDR4" / "cannibalization" but doesn't name HBM/DDR5 as destination
Gemini confirmed: TrendForce, DigiTimes, The Elec all document the DDR4→HBM/DDR5 shift
Result: Claim restored with dual attribution (MS + public sources)
Same thread: "Samsung, Kioxia, Micron all reducing MLC NAND" — MS only confirmed Samsung, said Kioxia/Micron "could" reduce. Gemini confirmed all three are actively reducing per TrendForce (41.7% YoY MLC NAND capacity decrease).
When to Use
RLM Audit Result
Use Gemini?
Why
FAIL — wrong number
No
Number errors need source correction, not web search
FAIL — inference beyond source
Yes
Inference may be valid per public sources
FAIL — misattribution
Maybe
Check if correct attribution exists publicly
UNSOURCED — claim not in cited PDF
Yes
Claim may be common industry knowledge
PASS
No
Already verified
How to Run
# For each FAIL/UNSOURCED claim that looks like a reasonable inference:
gemini -p "Search the web: [specific factual question about the inference].
I need external industry sources (TrendForce, DigiTimes, The Elec, Reuters,
company earnings calls) from 2025-2026 confirming or denying this."
Key: Ask Gemini to search the web explicitly. Without "search the web", Gemini may read local files instead.
# Taiwan stock example: 3443
for i in 1 2; do
echo "=== TWSE-Model $i ==="
curl -s "https://kicksvc.online/api/twse-model$i" | \
jq '.equitySeries[0].series[] | select(.Ticker | contains("3443")) |
{ticker: .Ticker, return: .data[-1].value}'
done
# US stock example: MU
for i in 1 2 3 4 5; do
echo "=== USA-Model $i ==="
curl -s "https://kicksvc.online/api/usa-model$i" | \
jq --arg t "MU" '.equitySeries[0].series[] | select(.Ticker == $t) |
{ticker: .Ticker, return: .data[-1].value}'
done
3. Compare Step 2 vs Step 4c results:
**Holdings Verification:**
Step 2 (Initial): Claimed "Not held"
Step 4c (Final): ✅ Found in TWSE Model 1 (+2.22%)
**Action Required:** Update draft to reflect actual position
4. If holdings status changed, update draft:
# Before (Step 2):
"I don't have a position here, but watching..."
# After (Step 4c):
"KSVC holds GUC in TWSE Model 1 (+2.22% since entry). Watching..."
Decision Matrix
Step 2
Step 4c
Action
Not held
Not held
✅ No change needed
Not held
HELD
❌ UPDATE DRAFT - change content angle
Held
Held
✅ Verify return % is current
Held
Not held
❌ UPDATE DRAFT - position was closed
Output Format
**Step 4c: Final Holdings Verification**
✅ Verified ALL 7 models (USA 1-5, TWSE 1-2)
**Tickers checked:**
- 3443 (GUC): ✅ Found in TWSE Model 1 (+2.22%)
- $MU: ❌ Not held
- $AMD: ✅ Found in USA Model 3 (+12.5%)
**Changes required:**
- Update draft line 32: Add KSVC position note for GUC
- Update draft line 45: Add KSVC position note for AMD
Step 4c: Stylize (MANDATORY)
Why this step exists: The data backbone (Step 3) is Serenity-heavy - precise, comprehensive, verified facts. Step 4c transforms it into Kirk's authentic voice with emotional range and character.
DECLARE SOURCE (MANDATORY) - before any chart generation
"I am charting [METRIC] from [SOURCE] page [X]"
"Source contains these exact values: [list them]"
Generate with chart-factory
/chart-factory
Chart Generation Workflow
Draft complete → identify chartable claims
↓
Pull data from RLM cache (NOT from draft text)
↓
⚠️ DECLARE SOURCE (state metric + page + exact values)
↓
Save source image FIRST (before generating)
↓
Generate with chart-factory (use theme-factory)
↓
Verify with verification agent
↓
Save to assets folder
Source Declaration (LEARNED FROM MISTAKE)
⚠️ Why this exists: We once created a "component count" chart but saved a "cost %" source image. The metrics didn't match, making the source invalid for verification.
Before generating ANY chart, you MUST:
Step
Action
Example
1. State
"I am charting [METRIC] from [SOURCE]"
"I am charting hardware cost % from 永豐 p.20"
2. Show
Screenshot the exact source table/chart
Save as source_hardware_cost_p20.png
3. Confirm
"Source contains: [exact values]"
"19%, 16%, 13%, 52%"
4. Flag
If transforming data, justify it
"I am NOT transforming - using values as-is"
Red flags - STOP if you notice:
Source shows % but you're charting counts (metric mismatch)
Source has 15 items but chart has 5 (cherry-picking)
Source image doesn't contain your chart's numbers (wrong source)
Company name romanized/guessed from Chinese (fabricated data)
Ticker suffix assumed without checking (TT vs TW)
Company & Ticker Verification
⚠️ LEARNED FROM MISTAKE: We fabricated "Chuing" for 祺驊 (4571). Official name is "KHGEARS".
# Always verify Taiwan company names via TWSE API
curl -s "https://www.twse.com.tw/en/api/codeQuery?query=4571"
# Returns: {"query":"4571","suggestions":["4571\tKHGEARS"]}
Never romanize Chinese names (祺驊 ≠ "Chuing")
Use TW suffix for general audience (TT = Bloomberg only)
After generating, spawn Explore agent with thoroughness: quick for focused verification:
Task(subagent_type="Explore", prompt="""
**THOROUGHNESS: quick**
**CONTEXT ISOLATION: You have NO external conversation history. Work ONLY from this prompt.**
CHART VERIFICATION TASK
Chart: /path/to/chart.png
Type: bar
Source Data (expected):
{"2025": 5.3, "2026": 8.3, "2027": 13.0}
Source Context:
永豐 p.3 - "2025年全球人型機器人規模約53億美元"
Task:
1. Read the chart image
2. Extract numbers from visual
3. Compare to expected data
4. Check for unit consistency (B vs M, % formatting)
Return ONLY JSON:
{
"verified": true/false,
"numbers_in_chart": [...],
"numbers_in_source": [...],
"discrepancies": [...],
"notes": "..."
}
""")
Verification checks data → chart integrity. Source accuracy is RLM's responsibility (Step 4a).
Thoroughness = quick: Single-pass verification, focused on specific data points. Fast visual-to-data check.
Save Charts
Save to: draft/YYYY-MM-DD-topic-assets/
Include:
Generated charts (chart1_.png, chart2_.png)
Source images from PDF (for traceability)
generate_charts.py script (reproducibility)
Step 7: Publish to Final Folder
After approval, publish clean version to /Users/Shared/ksvc/threads/.
File Organization Convention
CRITICAL: Flat folder structure, one folder per post.
