Trump truth monitor

Trump Truth Monitor

Pulls Donald Trump's Truth Social feed via the trumpstruth.org RSS mirror, classifies posts into market-relevant topic buckets, and hands the candidate list off for LLM-level market-impact grading.

When to use

• User asks what Trump has posted recently
• Pre-market gap on policy-sensitive sectors (semis, China ADRs, autos, energy, banks, defense) — check whether a Trump post is the trigger
• Building the "Catalyst (now)" lens of stock-deep-dive for a name with policy exposure (TSM, NVDA, AAPL, F, GM, XOM, BAC, RTX, LMT)
• market-session-tracker pre-market protocol — add a Trump-feed pass

If the user wants tweet history beyond ~5 days, this skill is insufficient — the RSS mirror only exposes the latest ~100 posts. Route to Factba.se / Roll Call (paid) or note the limitation explicitly.

Data source

trumpstruth.org/feed — a public third-party mirror of @realDonaldTrump on Truth Social. RSS 2.0 XML with these fields per item:

Field	Meaning
<pubDate>	RFC 2822, original Truth Social post timestamp
<link>	trumpstruth.org/statuses/{mirror_id}
<truth:originalUrl>	truthsocial.com/@realDonaldTrump/{truth_id} — the primary source
<description>	Full post body with HTML (links + ellipsis spans)

The mirror typically lags the original by ≤2 minutes. Single feed pull returns ~100 most recent posts, covering ~5 days at Trump's typical cadence.

CLI

Read mode — fetch.py

# Default: last 24h, keyword-filtered, markdown
python3 .claude/skills/trump-truth-monitor/scripts/fetch.py

# Wider window
python3 .claude/skills/trump-truth-monitor/scripts/fetch.py --hours 72

# All posts in feed regardless of keyword
python3 .claude/skills/trump-truth-monitor/scripts/fetch.py --hours 72 --all

# Single topic
python3 .claude/skills/trump-truth-monitor/scripts/fetch.py --topic tariff_trade

# JSON output (for chaining)
python3 .claude/skills/trump-truth-monitor/scripts/fetch.py --json

Topic buckets defined in script: tariff_trade, semi_tech, energy, fed_macro, crypto, geopolitical.

Archive mode — archive.py

# Append new posts to journal/trump-feed/YYYY-MM-DD.md (idempotent)
python3 .claude/skills/trump-truth-monitor/scripts/archive.py

# Custom output dir
python3 .claude/skills/trump-truth-monitor/scripts/archive.py --out /path/to/dir

# Silent unless something new was added
python3 .claude/skills/trump-truth-monitor/scripts/archive.py --quiet

The archive de-dupes by mirror status_id — re-running on the same feed is a no-op. Designed to be scheduled (see launchd/README.md). Once archived, posts persist locally even if trumpstruth.org goes down.

Workflow

1. Decide window. Default 24h. Use 48–72h when investigating a multi-day move. Use --all when context-grazing.
2. Decide scope. If user asks generally → no --topic. If user names a domain (关税 / 半导体 / 油 / 加密) → pass --topic.
3. Pull feed. Run fetch.py with chosen flags. Always include --hours — never default to "all of feed" silently.
4. Second-pass grading. Script output is candidates, not signals. For each post:
   • Read the full text before assigning impact. Headlines and keyword tags lie.
   • Assign a market-impact tier: high / med / low / noise
   • high = concrete action with $ figure, %, date, named country/company (e.g. "25% tariff on Mexican imports effective June 1", "Section 232 on chips")
   • med = directional signal without specifics (e.g. "We'll be tough on China", "must invest in America")
   • low = brand alignment with sector (e.g. "American Energy DOMINANCE", "Crypto Capital of the World" — already-priced policy stance)
   • noise = keyword matched but body is endorsement / personal / off-topic (e.g. "support the Military" in a Senate endorsement)
5. Anchor on original URL. When quoting, always cite truth:originalUrl (the truthsocial.com link), not the mirror.
6. Render output. For multi-post stretches, group by tier — high first, then med, then a one-line low/noise tally.

Output template

# Trump's Truth — {WINDOW}

## High-impact (potential market mover)
- [{utc_time}] {one-line summary} — `tier: high` · {topic tags}
  > "{verbatim short quote ≤2 sentences}"
  - Original: {truthsocial.com URL}
  - Possible market read: {sector / ticker level expectation, anchored}

## Medium-impact (directional, no specifics)
- [{utc_time}] {one-line summary}
  - Original: {URL}

## Noise (matched keyword, low signal)
- {N} posts ({topic distribution}) — endorsements / personal — not enumerated

⚠ Trump may delete or contradict within hours. Position decisions should require independent confirmation (sector ETF tape, peer reaction, official release).

Anti-patterns

Mistake	Reality
Treating script output as "market signal"	Script is a keyword filter. LLM must read each post and tier.
Quoting a mirror URL as the source	Always link truth:originalUrl (truthsocial.com). Mirror is a convenience.
Reporting Senate endorsements as "policy news"	Politics-only posts with military / energy keywords are noise — filter at tier=noise.
"Trump said X about Y" with no link	Always include the truthsocial.com link. User must be able to verify.
Pretending tweets are durable	Trump posts can be deleted or retracted within hours. If consulted >12h after, note staleness.

Integration

• market-session-tracker pre-market protocol: insert a Trump-feed --hours 14 pull as step 0 (covers post-prev-close to pre-market). If high-tier post exists touching watchlist sectors, escalate to the explanation slot for any gap.
• stock-deep-dive lens 4 (Catalysts): when the symbol has policy exposure (semis / China ADR / auto / energy / defense / bank), run a Trump-feed --hours 168 and surface high-tier hits.
• gdelt can confirm market has already picked the post up (i.e. major outlets are reporting it). Trump feed = original; GDELT = market-validated.

Limitations

• Mirror dependency: trumpstruth.org is third-party. If it goes down, the live fetch.py fails — but the archived posts under journal/trump-feed/ remain readable.
• 5-day depth via mirror: a single feed pull only exposes the last ~100 posts. Anything older than ~5 days that wasn't archived in time is lost. Schedule archive.py (see launchd/) to grow a permanent local record.
• No X feed: Trump's X (Twitter) account is separate. This skill does not cover X posts. If user asks about X specifically, note the gap.
• Truth posts are not press releases. Treat as primary-but-volatile speech: original URL is authoritative for what was said, but the policy implementation may diverge (or never happen).

Local archive — searching past posts

Once archive.py has been running, journal/trump-feed/YYYY-MM-DD.md accumulates a complete record. To investigate a past day or query historically:

# All tariff-related posts ever archived
grep -l "tariff" journal/trump-feed/*.md

# Specific company mention
grep -B2 -A8 -i "nvidia\|tsmc" journal/trump-feed/*.md

# Posts on a specific date
cat journal/trump-feed/2026-05-26.md

The archive is plain markdown — grep-friendly, git-trackable.

Related skills

• gdelt — market-validated news coverage of a Trump post
• stock-deep-dive — caller for catalyst-lens enrichment
• market-session-tracker — caller for pre-market protocol
• sec-edgar — confirm whether a tweet translates into an actual filing (rare but does happen for trade-policy items affecting specific companies)