Agent skill · Security

narrative-drift-monitor

Use when the user asks to "check if our surfaces have drifted from the canon", "watch for competitor repositioning", or "define when we should reposition"; produces a drift report — self-drift per flagship surface vs the narrative-registry canon over time (via wayback.py, change history Measured with as-of dates), competitor-repositioning alerts, an explicit repositioning-trigger condition set, and a D1/W1/M1 message-shift retro (intended vs actual pull-through, evidence-labeled) — feeding the TALE L drift-audit sub-items and the narrative-whiplash guardrail fact base. Not for the first-time c

aaron-he-zhugithub.com/aaron-he-zhuGitHub ↗
claude-codeApache-2.0
Install
npx skills add aaron-he-zhu/aaron-marketing-skills --skill narrative-drift-monitor --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 1
SKILL.md size: 14 KB
Bundled scripts: none
Version: 19.1.0
Requires: Claude Code and compatible agent-skill hosts
Path: narrative/evaluate/narrative-drift-monitor/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 2,508
Language: Python
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Narrative Drift Monitor Watches a live narrative for drift after it has landed — the surfaces that have quietly drifted away from the [narrative-registry](../../../protocol/narrative-registry/SKILL.md) canon over time, the competitors that have repositioned, the explicit conditions that should (and should not) trigger a repositioning of your own message, and a D1/W1/M1 message-shift retro of intended-vs-actual pull-through. It is the last move of the TALE **Evaluate** phase and feeds two [TALE](../../../references/tale-benchmark.md)-`L` items — *a message-consistency pass is run before any flagship surface ships a major change* and the cross-surface *matches-the-canon* check over time — plus it is the recorded **fact base for the narrative-whiplash guardrail under A** (re-cutting the narrative faster than the market can absorb it, with no triggering evidence). It measures change history with `scripts/connectors/wayback.py` (Measured, each snapshot carrying an as-of date) and reads competitor narrative context from [category-narrative-mapper](../../trace/category-narrative-mapper/SKILL.md); it never scores. **Scope guard**: this skill produces the drift report and repositioning-tr

What's inside
Steps it walks through
  1. Quick Start
  2. Skill Contract
  3. Handoff Summary
  4. Data Sources
  5. Instructions
  6. Save Results
  7. Reference Materials
  8. Next Best Skill
More from aaron-marketing-skills
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About this skill
What does the narrative-drift-monitor skill do?

Use when the user asks to "check if our surfaces have drifted from the canon", "watch for competitor repositioning", or "define when we should reposition"; produces a drift report — self-drift per flagship surface vs the narrative-registry canon over time (via wayback.py, change history Measured with as-of dates), competitor-repositioning alerts, an explicit repositioning-trigger condition set, and a D1/W1/M1 message-shift retro (intended vs actual pull-through, evidence-labeled) — feeding the TALE L drift-audit sub-items and the narrative-whiplash guardrail fact base. Not for the first-time c

How do I install it?

Run `npx skills add aaron-he-zhu/aaron-marketing-skills --skill narrative-drift-monitor --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From aaron-he-zhu/aaron-marketing-skills, a repository with 2,508 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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