Narrative Tracker logo

Narrative Tracker

Organization
aeonfun
narrative-tracker

Track rising, peaking, and fading crypto/tech narratives with quantitative mindshare + velocity signals and explicit positioning calls

Overview

Publisheraeonfun
Repositoryaeon
Skill namenarrative-tracker
Stars
771
Forks
280
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by aeonfun on GitHub. Read the source before you install it.

Installation

Install the Narrative Tracker AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/aeonfun/aeon/tree/main/skills/narrative-tracker
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/aeonfun/aeon.git /tmp/aeon
mkdir -p .claude/skills
cp -r /tmp/aeon/skills/narrative-tracker .claude/skills/narrative-tracker
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Narrative Tracker in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Narrative Tracker on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Narrative Tracker is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Read memory/MEMORY.md for context on prior narrative observations. Read the last 3 days of memory/logs/ — specifically any prior ### narrative-tracker entries — to (a) avoid re-reporting the same narratives without new info, and (b) detect phase transitions vs the last run.

Goal

Produce a decision-grade narrative map: every narrative gets a mindshare score, a velocity arrow, a sentiment tag, named drivers, and an explicit position call. Classification without a position call is noise.

Steps

1. Ingest signals

a. X/Twitter narratives — X.AI API (primary). The primary signal is a direct curl to the X.AI Responses API with Grok's x_search tool — see ## Fetching below for the full contract. XAI_API_KEY is injected into this skill's environment via requires:; it is present and valid, so this path is required whenever the key check prints KEY_PRESENT. Set the Bash tool timeout to ≥180000 when running the curl (x_search takes 30-120s) and capture the HTTP status:

bash
FROM_DATE=$(date -u -d "3 days ago" +%Y-%m-%d 2>/dev/null || date -u -v-3d +%Y-%m-%d)
TO_DATE=$(date -u +%Y-%m-%d)
# Presence check uses the ${VAR:+x} modified expansion, NOT a bare $XAI_API_KEY — the bare
# form trips the Bash secret-expansion analyzer; the :+ form does not (it never puts the value on the line).
[ -n "${XAI_API_KEY:+x}" ] && echo KEY_PRESENT || echo KEY_UNSET
# Build the JSON payload to a file with jq (do NOT hand-assemble it), then POST the file.
# TWO SEPARATE commands on purpose: the jq (which interpolates $PROMPT/$FROM_DATE/$TO_DATE)
# is kept OUT of the ./secretcurl command — never pipe jq into secretcurl, and never put a
# $VAR in the secretcurl line, or the Bash permission analyzer blocks the network call.
# The `>` redirect to /tmp is fine in read-only mode (it is not a repo path; nothing reverts it).
PROMPT="Search X for the dominant crypto and tech narratives from ${FROM_DATE} to ${TO_DATE}. Return 12-15 distinct narrative threads. For each: 1) short label, 2) 3-5 representative @handles driving it, 3) 2-3 tweet permalinks, 4) rough mention-volume descriptor (niche / growing / saturating / cooling), 5) the strongest one-line bear case against it."
jq -n --arg p "$PROMPT" --arg fd "$FROM_DATE" --arg td "$TO_DATE" \
  '{model:"grok-4.7", input:[{role:"user",content:$p}], tools:[{type:"x_search",from_date:$fd,to_date:$td}]}' \
  > /tmp/xai-nt-payload.json
HTTP=$(./secretcurl -s -o /tmp/xai-nt.json -w '%{http_code}' --max-time 150 -X POST "https://api.x.ai/v1/responses" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer {XAI_API_KEY}" \
  -d @/tmp/xai-nt-payload.json)
echo "xai http=$HTTP bytes=$(wc -c </tmp/xai-nt.json)"

Run that block verbatim (do not hand-reassemble the JSON — the jq -n builder exists precisely so quoting/expansion can't break; keep the jq and the ./secretcurl as two separate commands). The echo "xai http=$HTTP ..." line must appear in your output — it is your proof the call ran. On HTTP=200 with a non-empty body, parse /tmp/xai-nt.json with jq -r '.output[] | select(.type == "message") | .content[] | select(.type == "output_text") | .text' and use that as the primary narrative signal (SOURCE=api).

b. WebSearch / WebFetch fallback (last-resort only). You may reach for this only after you have shown an xai http=<code> line proving Path A actually ran and returned a non-2xx code (or an empty body, or timed out). If you have no xai http= line, you did not run the call - go back and run it. Reach for the fallback only when Path A genuinely fails - KEY_UNSET, a non-2xx HTTP code, an empty parse, or a timeout. It is lower quality (WebSearch favours old high-engagement posts) and is never co-equal with Path A. Record the fetch failure in your final output with the true reason - key-unset | http-<code> | empty | timeout - never "XAI_API_KEY unavailable" when the key was set (a slow curl is a timeout, not a missing key). Then compile narratives via WebSearch (crypto narrative ${TO_DATE}, AI agent crypto trend this week) and WebFetch on individual tweet URLs; discard anything older than the 3-day window.

c. Quantitative reference points (supplement). Independently of the fetch path, cross-check mindshare against external quantitative benchmarks with one WebSearch: DefiLlama narrative tracker OR Kaito mindshare leaderboard. Pull 1-2 concrete numbers (project name, metric, link) to calibrate the mindshare scores in step 2. This is a calibration cross-check, not a narrative source. Do not paraphrase — extract facts.

d. Memory diff. Extract narrative labels mentioned in the last 3 days of ### narrative-tracker log entries. You'll compare against them in step 4.

2. Score each narrative

For each distinct narrative (merge near-duplicates aggressively — "AI agents" and "agentic crypto" are the same), assign:

FieldScaleHow to decide
Mindshare1-51 = fringe, 3 = known in the sector, 5 = dominating timelines. Base on count of distinct drivers + whether you had to dig or it surfaced unprompted.
Velocity↑↑ / ↑ / → / ↓ / ↓↓Compared to the 3-day window or prior log entries. ↑↑ = tripled in attention, ↓↓ = was loud 3 days ago, now absent.
PhaseEmerging / Rising / Peak / FadingUse the velocity + mindshare combo. Emerging = low mindshare, high velocity. Peak = high mindshare, flat/down velocity. Fading = high mindshare last week, now ↓.
SentimentBull / Mixed / Bear / CopeCope = bag-holder energy, bear narratives dressed as bull takes.
Drivers2-3 namedAccounts, projects, or funds amplifying it. Include @handles.
Bear case1 lineThe sharpest argument against. If the consensus is obviously right, say so and mark "no contrarian edge".
PositionFRONT-RUN / RIDE / FADE / WATCH / IGNOREFRONT-RUN = emerging + contrarian edge. RIDE = rising, not yet peaked. FADE = peak with weak fundamentals or reflexivity flip. WATCH = unclear. IGNORE = mindshare 1-2 with no catalyst.

Drop any narrative that ends up IGNORE unless it's structurally important — noise reduction is the goal.

3. Detect transitions

Compare today's narratives to the last 3 days of logs:

  • NEW — narrative wasn't in prior logs at all
  • PROMOTED — phase moved up (e.g. Emerging → Rising)
  • DEMOTED — phase moved down
  • DEAD — was in prior logs, now absent from all signals

Transitions are the highest-value output — the point of a daily tracker is to catch inflection points, not re-report the zeitgeist.

4. Flag reflexivity

For each narrative, flag if the story itself is moving outcomes:

  • Token prices moving on narrative alone (no fundamentals shift)
  • Projects rebranding/pivoting to ride the narrative
  • VCs publicly endorsing to manufacture legitimacy
  • Prediction markets or on-chain flows reflecting narrative belief

Only flag explicit cases with a concrete example. "Reflexivity" without evidence is hand-waving.

5. Format the notification

Keep under 4000 chars. Lead with transitions and reflexivity — those are the decisions. Classification goes below.

*Narrative Tracker — ${today}*

TRANSITIONS
• NEW: <label> — <why it matters> — <link>
• PROMOTED: <label> Rising → Peak — <what flipped>
• DEMOTED: <label> Peak → Fading — <what cooled>
• DEAD: <label> — gone

REFLEXIVITY ALERT
• <narrative> — <concrete evidence the story is moving outcomes>

POSITIONS
• FRONT-RUN: <label> (mindshare 2 ↑↑, Bull) — <driver> — <bear case> — <link>
• RIDE: <label> (3 ↑, Bull) — <driver> — <bear case>
• FADE: <label> (5 → Cope) — <driver> — <reflexivity note>

MAP
Emerging: <labels>
Rising: <labels>
Peak: <labels>
Fading: <labels>

If absolutely nothing new or notable (no transitions, no reflexivity, no FRONT-RUN/FADE calls): send a one-line update instead of the full template — *Narrative Tracker — ${today}*: no phase transitions, map unchanged from <last_date>.

6. Send via ./notify

7. Log record

This skill is read-only, so the workflow's read-only guard writes its ### narrative-tracker log entry from your captured output; a self-written entry would be a duplicate. Don't append to memory/logs/ yourself - put this record in your final output: the full structured output (not just the notification - include all narratives considered, even IGNOREd ones, so future diffs work). If a full run produced nothing actionable, record NARRATIVE_TRACKER_OK with the narrative labels seen (so tomorrow's diff still has a baseline).

Guidelines

  • Quantitative over vibes. Every narrative gets mindshare 1-5 and a velocity arrow — no exceptions. If you can't score it, drop it.
  • Transitions > classification. A daily tracker's value is catching moves, not listing the weather.
  • Named drivers only. "Crypto Twitter is excited about X" is not a driver. "@handle + @handle + @fund" is.
  • Position calls are mandatory for Emerging/Rising/Peak narratives. If signals are genuinely ambiguous or contradictory, WATCH is an acceptable call — but never omit a position entirely and never invent conviction you don't have.
  • Ruthless dedup. Same narrative under two labels = one narrative. Merge, don't split.
  • Call out cope. Manufactured narratives, coordinated shilling, and dead-cat bounces get tagged explicitly.
  • Prioritize topics tracked in MEMORY.md over generic market chatter.

Fetching

XAI_API_KEY is injected into this skill's environment (declared in requires:). It is present and valid. The primary fetch path is a direct curl to https://api.x.ai/v1/responses with Authorization: Bearer {XAI_API_KEY} (model grok-4.7, "tools":[{"type":"x_search"}]). There is no network sandbox blocking this - earlier versions of this skill claimed there was, and that is stale and false. Just make the call.

Rules:

  1. Check, don't assume. Run [ -n "${XAI_API_KEY:+x}" ] && echo KEY_PRESENT || echo KEY_UNSET (the ${VAR:+x} form, not bare $XAI_API_KEY — the bare form trips the secret-expansion analyzer and falsely reads as unset). If KEY_PRESENT (it will be), Path A (the curl in step 1a) is required before any fallback.
  2. Allow enough time. The x_search call typically takes 30-120s. When you invoke the Bash tool for the curl, set the tool's timeout to at least 180000 (180s) and keep --max-time 150 on the curl so it fails cleanly rather than hanging. A slow curl is not a missing key — never treat a timeout as key-unavailable.
  3. Capture the HTTP status so the fallback decision is fact-based (see the skeleton in step 1a). HTTP=200 with a non-empty parsed body → use it.
  4. Fall back only on a real failure, recording the true reason — key-unset (only if the check printed KEY_UNSET), http-<code> (non-2xx), empty (200 but nothing parsed), or timeout. Never write "XAI_API_KEY unavailable" when the key was set.

WebSearch / WebFetch are last-resort fallbacks only — lower quality, never primary or co-equal. Do not reach for them while the key works.

Environment Variables Required

  • XAI_API_KEY — X.AI API key for Grok's x_search tool. Declared in requires:, so it is injected into this skill's environment and is the primary fetch path for the narrative signal (step 1a). If it is ever unset, the skill degrades to WebSearch/WebFetch at lower quality.
  • Notification channels configured via repo secrets (see CLAUDE.md).

Frequently asked questions

What does the Narrative Tracker AI skill do?

Track rising, peaking, and fading crypto/tech narratives with quantitative mindshare + velocity signals and explicit positioning calls

Why use Narrative Tracker on TypingMind?

Because you install it once and use it with any model. Narrative Tracker is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Narrative Tracker in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/aeonfun/aeon/tree/main/skills/narrative-tracker. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Narrative Tracker?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Narrative Tracker?

As many as you like. As long as a model supports skills, you can use Narrative Tracker with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Narrative Tracker AI skill free?

Yes. It is published on GitHub by aeonfun under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇