topic-momentum

name: topic-momentum description: Content-gap scanner — cross-references rising narrative signals (narrative-tracker, tweet-roundup, paper-pick, etc.) against recent article history and surfaces the top 3 uncovered angles to write next. var: "" tags: [content, meta]

${var} — Optional domain filter (e.g. "crypto", "AI", "prediction-markets"). If empty, scans every domain declared in memory/topics/content-domains.md.

Today is ${today}. Read memory/MEMORY.md before starting.

Voice

If soul/SOUL.md and soul/STYLE.md are populated, read both and match the operator's voice when drafting the suggested hook line (step 4) and the notification body. If they are empty templates or absent, use a clear, direct, neutral tone — short, declarative, position-first.

Why this skill exists

The article skill picks one trending topic per run. Skills like narrative-tracker, tweet-roundup, and paper-pick surface discrete signals. Nothing cross-references what's been covered against what keeps surfacing — so timely angles get missed or covered weeks late. This skill closes that gap: a weekly pattern detector that finds the signal the operator keeps receiving but hasn't written about yet.

Config

Domain filters and signal-source aliases live in memory/topics/content-domains.md. If the file doesn't exist, create the seed below and continue with the default (no filter):

# Content Domains

## Domains
- crypto
- AI
- prediction-markets
- macro
- protocols

## Signal Sources
(Skill log section names that produce candidate signals. Add more as you wire up trackers.)

- narrative-tracker     # rising/peaking narratives
- tweet-roundup         # topic-grouped tweet picks
- paper-pick            # research papers
- repo-actions          # GitHub-ecosystem ideas

## Topic Memory Files
(Files in memory/topics/ that hold cross-run context the gap scanner should also read.)

- market-context.md
- papers.md

If ${var} is set, restrict the gap-scan to themes that match the named domain (substring/keyword match against the theme name).

Steps

1. Load recent article coverage

Use Glob to list .md files in articles/ modified in the last 30 days (filename pattern YYYY-MM-DD.md makes this easy).

For each file:

• Read the H1 and first 2 sentences — extract the core topic and angle
• Note the date from the filename

Build a covered-topics list: [{ date, topic, angle }].

• Articles ≤ 7 days old: "very recent" → suppress re-suggestion (-5 in scoring)
• Articles 8–14 days old: "recent" → penalize (+1 only)
• Articles 15–30 days old: still penalized lightly (+3)
• Articles > 30 days old or never written: full credit (+5)

2. Load narrative signals from recent logs

Read memory/logs/ for the last 7 days (Glob memory/logs/*.md, sort by name, take last 7).

From each daily log, extract entries under each Signal Source declared in content-domains.md. For each entry, extract:

• The theme / narrative name
• Whether it was labeled "rising", "peaking", or otherwise high-signal
• How many sources / days surfaced it

Also read each Topic Memory File from content-domains.md (default: memory/topics/market-context.md, memory/topics/papers.md) for current macro themes and hot narratives.

Build a signal-map: { theme: { frequency_score, source_list, first_seen, last_seen } }.

If ${var} is set, filter signal-map to themes matching that domain.

3. Score the gaps

For each theme in signal-map:

Max score: ~14. Drop themes with net score < 2.

Rank descending. Pick top 3.

4. Develop the angles

For each top-3 gap:

• Define a specific angle — not "write about X" but "X from the angle of Y; the thing everyone's missing is Z"
• Draft a hook line (voice per the Voice section above)
• Note what triggered it (sources from step 2)
• Note last coverage date or "never"

5. Update memory

Write memory/topics/content-gaps.md (overwrite):

# Content Gaps — Last Updated: ${today}

## Top 3 Angles (Ranked by Signal Score)

### 1. <Theme Name> — Score: N/14
**Angle:** <specific take — not generic>
**Hook:** <suggested opener>
**Sources:** <what surfaced this, e.g. "narrative-tracker 4d, tweet-roundup 3d">
**Last coverage:** <date or "never">

### 2. <Theme Name> — Score: N/14
...

### 3. <Theme Name> — Score: N/14
...

---
*Generated by topic-momentum on ${today}. Consumed by: article skill, remix-tweets.*

6. Notify

Use Write to create .pending-notify-temp/topic-momentum-${today}.md:

topic momentum — ${today}

3 angles with high signal, no recent article:

1. <theme name> — <angle in one line>
2. <theme name> — <angle in one line>
3. <theme name> — <angle in one line>

full breakdown: memory/topics/content-gaps.md

Then run:

./notify -f .pending-notify-temp/topic-momentum-${today}.md

Keep total under 800 chars. Do NOT use ./notify "$(cat ...)" — write the file first, pass the path.

7. Log

Append to memory/logs/${today}.md:

## Topic Momentum
- **Themes scanned:** N
- **Gaps scored:** N
- **Top 3:** <theme1>, <theme2>, <theme3>
- **Lowest gap score included:** N/14
- **Updated:** memory/topics/content-gaps.md
- **Notification:** sent
- TOPIC_MOMENTUM_OK

If fewer than 3 scoreable gaps were found: log TOPIC_MOMENTUM_SKIP: insufficient signal (<3 themes above threshold) and stop without notifying.

Required Env Vars

None. All reads from local memory/ and articles/ dirs. No external API calls, no curl, no prefetch script needed.

Sandbox Note

No network calls required — all data comes from local memory files written by other skills. If memory/logs/ is sparse (e.g. first run), fall back to reading the Topic Memory Files declared in content-domains.md directly as the signal source. WebSearch is available as a last resort for current narrative heat if local data is too thin, but should rarely be needed.