Phase 6: theme trend-scan (B) + competitor gap research (A)
- theme_scan.py: tags entries by 4 practitioner themes (tool-call/context/ compute/trust), counts NEW arrivals per cron cycle (falsification check for one-day-cluster vs trend). Idempotent: re-run = 0 new. - schema.sql: theme_tags table (separate from core entries schema) - oracle-pipeline.sh: wire theme_scan after summarize - A result (footnote): unified discipline layer unoccupied; adjacent OSS entrants exist (agentgateway, lelu) but no portable unified layer. Verified: theme_scan classifies 5 seed/extra items, 2nd run = 0 new.
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Epictetus
@@ -21,6 +21,9 @@ cd "$ORACLE_DIR" || { echo "FATAL: cannot cd $ORACLE_DIR"; exit 1; }
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echo "=== Summarization Engine ==="
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python3 summarize.py
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echo
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echo "=== Phase 6 theme trend scan (new arrivals this cycle) ==="
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python3 theme_scan.py
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echo
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echo "=== Soft-cap archive (dry-safe default: 30d / 5000 cap) ==="
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python3 archive.py --days 30 --cap 5000
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echo
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+13
@@ -31,3 +31,16 @@ CREATE TABLE IF NOT EXISTS run_log (
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sources_failed TEXT, -- JSON list of sources that errored/skipped
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notes TEXT
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);
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-- Theme tags: Phase 6 trend-tracking. Tags entries by the 4 practitioner
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-- resource-discipline themes so we can measure RECURRING theme frequency
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-- across FRESH entries (not persistence of specific rows). Counts new
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-- arrivals per cron cycle -> the falsification check for the "one-day cluster
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-- vs real trend" question. Separate table, never mutates the core entries schema.
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CREATE TABLE IF NOT EXISTS theme_tags (
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entry_id INTEGER NOT NULL,
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theme TEXT NOT NULL,
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first_seen_cycle TEXT DEFAULT (strftime('%Y-%m-%dT%H:%M:%SZ', 'now')),
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PRIMARY KEY (entry_id, theme),
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FOREIGN KEY (entry_id) REFERENCES entries(id)
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);
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+128
@@ -0,0 +1,128 @@
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#!/usr/bin/env python3
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"""Phase 6 trend-tracking: theme-based arrival counter.
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The question this answers: is the 2026-07-08 practitioner cluster a real TREND
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or a one-day COINCIDENCE?
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Design (per review):
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- Tag by THEME, not by entry ID.
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- Count NEW theme-tagged ARRIVALS per cron cycle (only classify rows that
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have no theme_tags yet -> fresh entries each run).
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- If new theme arrivals stay ~0 all week => coincidence.
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- If 2-4+ new relevant items/cycle across sources => trend.
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Only then does the ponytail-generalization idea graduate from a one-day
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read to something worth further investment.
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Themes:
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tool-call : tool-call gating on confidence / reliability of tool use
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context : context / token compression before window ceiling
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compute : routing to smallest sufficient model / inference cost
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trust : trust boundaries on what a model may learn / vetted adapters
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Run: python3 theme_scan.py # classify + report new arrivals
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python3 theme_scan.py --history # also print per-cycle history
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"""
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import argparse
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import os
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import re
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import sqlite3
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import sys
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from collections import Counter
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DB = os.path.join(os.path.dirname(__file__), "oracle.db")
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# Theme -> regex over title+summary+extracted text (case-insensitive).
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# Deliberately keyword-anchored, not semantic — cheap, auditable, reproducible.
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THEME_PATTERNS = {
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"tool-call": re.compile(
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r"\b(tool[- ]?call|tool[- ]?use|competence gate|confidence gate|"
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r"gate[d]? tool|action gate|tool reliability|function call gate)\b",
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re.I),
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"context": re.compile(
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r"\b(context (compress|window|ceiling|summar)|semantic compress|"
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r"token (compress|budget)|compress (context|session)|context (limit|overflow))\b",
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re.I),
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"compute": re.compile(
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r"\b(small(er|est)? model|route to|inference cost|cpu (tts|infer)|"
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r"cheap(er)? model|model routing|tiny model|on[- ]device (llm|model))\b",
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re.I),
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"trust": re.compile(
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r"\b(trust(ed)? (adapter|lora)|vetted adapter|learn (only|what).*adapter|"
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r"trust boundary|what a model (can|may) learn|auditable (adapter|skill))\b",
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re.I),
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}
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def main():
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ap = argparse.ArgumentParser()
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ap.add_argument("--history", action="store_true",
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help="Print per-cycle new-arrival history after the run")
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args = ap.parse_args()
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if not os.path.exists(DB):
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print("No oracle.db — nothing to scan")
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return
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conn = sqlite3.connect(DB)
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conn.row_factory = sqlite3.Row
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cur = conn.cursor()
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# Ensure theme_tags table exists (defensive; schema.sql creates it).
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cur.execute("""CREATE TABLE IF NOT EXISTS theme_tags (
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entry_id INTEGER NOT NULL,
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theme TEXT NOT NULL,
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first_seen_cycle TEXT DEFAULT (strftime('%Y-%m-%dT%H:%M:%SZ','now')),
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PRIMARY KEY (entry_id, theme))""")
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# Only rows not yet classified -> fresh arrivals this cycle.
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cur.execute("""
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SELECT e.id, e.source, e.title,
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COALESCE(e.summary,'') AS summary,
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COALESCE(e.extracted_text,'') AS extracted
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FROM entries e
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WHERE e.id NOT IN (SELECT entry_id FROM theme_tags)
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""")
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fresh = cur.fetchall()
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new_counts = Counter()
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for row in fresh:
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blob = f"{row['title']} {row['summary']} {row['extracted']}"
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for theme, pat in THEME_PATTERNS.items():
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if pat.search(blob):
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cur.execute(
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"INSERT OR IGNORE INTO theme_tags (entry_id, theme) VALUES (?, ?)",
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(row["id"], theme))
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new_counts[theme] += 1
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conn.commit()
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print("=== Theme trend scan ===")
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print(f" Fresh (unclassified) entries this cycle: {len(fresh)}")
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if new_counts:
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print(" NEW theme arrivals this cycle:")
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for theme in ("tool-call", "context", "compute", "trust"):
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if new_counts.get(theme):
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print(f" {theme}: +{new_counts[theme]}")
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else:
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print(" NEW theme arrivals this cycle: 0")
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# Cumulative context for the trend question.
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cur.execute("SELECT theme, COUNT(*) AS c FROM theme_tags GROUP BY theme")
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cum = {r["theme"]: r["c"] for r in cur.fetchall()}
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print(f" Cumulative theme_tags totals: {cum}")
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if args.history:
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print("\n Per-cycle new arrivals (by first_seen_cycle):")
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cur.execute("""
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SELECT substr(first_seen_cycle,1,10) AS day, theme, COUNT(*) AS c
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FROM theme_tags GROUP BY day, theme ORDER BY day, theme
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""")
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for r in cur.fetchall():
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print(f" {r['day']} {r['theme']}: {r['c']}")
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conn.close()
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if __name__ == "__main__":
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main()
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