morphiq-track

Pipeline Position

Step 4 of 4 — measurement + flywheel.

Input: Build Output (JSON) from morphiq-build + MORPHIQ-TRACKER.md (persistent state).
Output: Delta Report (JSON) → loops back to morphiq-rank.
Owns: MORPHIQ-TRACKER.md — generates on first run, updates every run.
Owns:
```
morphiq-track/
```
state directory — JSON state layer for prompts, results, citations.
Drives: 3 ongoing workflows (Content Optimization, Content Creation, Query Fanout Expansion).
Data contract: See
```
PIPELINE.md
```
§4 for the Delta Report, §5 for MORPHIQ-TRACKER.md, §6 for the JSON State Layer.

Purpose

Morphiq Track is the measurement and flywheel skill. It queries AI providers to measure brand visibility, computes GEO scores and Share of Voice, tracks deltas over time, and drives three ongoing workflows that feed back into the pipeline.

Workflow

Step 0: Initialize or Load State

Check if

morphiq-track/manifest.json

exists in the project root.

Missing (first run): Proceed to Step 1. The state directory will be created.
Present (subsequent run): Load
```
morphiq-track/prompts.json
```
directly — this contains the full prompt set with config, metadata, and tracking state. Skip to Step 2. If
```
recommendations.cooldown_days
```
has elapsed since
```
recommendations.last_generated
```
, generate 20 new recommendations via
```
create-prompts.py --state-dir morphiq-track/ --refresh
```
.
Migration (tracker exists but no state dir): Parse MORPHIQ-TRACKER.md §8 to bootstrap
```
prompts.json
```
, parse §7 to bootstrap
```
citations.json
```
. See
```
references/state-layer.md
```
Migration section.

For state layer specification, read

references/state-layer.md

Step 1: Generate Prompts

First run only. Generate 50 prompts across 5 GEO categories:

Category	Share	Brand Name?
Organic	45%	No
Competitor	11%	Mixed
How-to	14%	No
Brand-Specific	13%	Yes
FAQ	17%	No

Apply quality rules per category. Add temporal markers to 70%+ prompts. Include entities in comparison/technical prompts.

Run

scripts/create-prompts.py --state-dir morphiq-track/ --brand {brand} --category {category} --competitors {competitors}

. This writes

morphiq-track/prompts.json

and initializes

morphiq-track/manifest.json

For taxonomy, fanout profiles, and generation rules, read

references/prompt-taxonomy.md

Step 2: Query AI Providers

Distribute prompts evenly across 4 providers. Execute using

scripts/run-queries.py --state-dir morphiq-track/ --mode execute

. This reads prompts from

morphiq-track/prompts.json

, writes versioned results to

morphiq-track/results/track-{date}.json

, and updates

morphiq-track/manifest.json

Provider	Model	Concurrency
OpenAI	gpt-4o	Full
Perplexity	sonar-pro	2 concurrent
Anthropic	claude-sonnet-4-5-20250514 → claude-sonnet-4-20250514	Serialized
Gemini	gemini-2.5-flash	3 concurrent

Mandatory requirements for every query:

Full response text. Store the complete response — never truncate. morphiq-build's content creation workflow requires the full text for analysis.
Sub-query extraction. For each provider that exposes tool calls, extract the search queries the model issued. These feed Workflow C (Query Fanout Expansion) and invisible SoV.
Citation deduplication. After collecting citations per response, strip UTM/tracking params from URLs and deduplicate. Track
```
citation_weight
```
(number of times each URL was cited).
Retry on transient failure. Retry once with 2-second delay before marking as error. This handles rate limits.

Provider-specific requirements:

OpenAI: Iterate
```
response.output
```
for items with
```
type == "web_search_call"
```
— extract the
```
query
```
field into
```
sub_queries[]
```
. This reveals GPT's
```
site:
```
operator searches and two-phase research pattern.

Perplexity: Citations are a Perplexity-specific field. Check

response.citations

, then

response.model_extra["citations"]

, then

response.__dict__["citations"]

, then

response.choices[0].message.model_extra["citations"]

. The OpenAI-compatible client puts unknown API fields in

model_extra

Anthropic: Tool config must be
```
{"type": "web_search_20250305", "name": "web_search", "max_uses": 5}
```
. Try model
```
claude-sonnet-4-5-20250514
```
first, fall back to
```
claude-sonnet-4-20250514
```
. Response content blocks include
```
text
```
(final answer),
```
web_search_tool_result
```
(search results with URLs), and
```
server_tool_use
```
(the search call). Extract text only from
```
text
```
blocks; extract citations from both
```
text
```
block inline citations and
```
web_search_tool_result
```
block content.
Gemini: Grounding metadata returns
```
vertexaisearch.cloud.google.com
```
redirect URLs. Follow the redirect to get the real URL. If redirect fails, use the
```
grounding_chunk.web.title
```
as fallback domain:
```
{url: proxy_url, title: title, resolved_domain: title}
```
.

For full provider config and response pipeline, read

references/provider-strategies.md

. For selection rules and distribution, read

references/query-targets.md

Step 3: Analyze Responses

5-step pipeline: extract raw response/citations/sub-queries → structured analysis (using the agent's reasoning capabilities) → brand mention validation (exact → TLD → LLM judge) → competitor filtering → entity normalization.

Input requirements for analysis: Each response must include the full response text, deduplicated citations with

citation_weight

, and extracted sub-queries. The analysis uses the

config

block from the prompts file for brand, domain, and competitors — never hardcoded values.

Step 4: Compute GEO Score

GEO = mean(provider_scores)
Weighted GEO = (Organic × 0.45) + (Competitor × 0.22) + (How-to × 0.22) + (Brand × 0.11)

Thresholds: ≥60 Excellent, ≥40 Good, ≥20 Fair, ≥10 Poor, <10 Very Poor.

For GEO methodology, read

references/query-targets.md

Step 5: Compute Share of Voice

Three SoV tiers:

Metric	What It Measures
Mention SoV	Brand name in final responses
Fanout-Weighted SoV	Weighted by prompt type fan-out depth
Influence SoV	Brand presence in sub-queries (invisible influence)

Track Conversion Gap = Influence SoV − Citation SoV.

For SoV methodology, read

references/share-of-voice.md

Step 6: Compute Deltas

Compare against previous snapshot using

scripts/diff-results.py --state-dir morphiq-track/

. The script reads

manifest.json

to auto-resolve the current (

runs[0]

) and previous (

runs[1]

) results paths, and reads

morphiq-track/citations.json

for previous citation state. Flag changes >5 points. Generate flagged actions for regressions, losses, displacement, and conversion gaps.

For delta methodology, read

references/delta-scoring.md

Step 7: Update State Layer and MORPHIQ-TRACKER.md

State layer updates (JSON — source of truth for track-owned data):

Rebuild
```
morphiq-track/citations.json
```
from current results + previous citation state (gained/lost/stable)
Update
```
morphiq-track/prompts.json
```
tracking fields (mentioned, cited, best_provider, runs_tracked, last_run)
Update
```
morphiq-track/manifest.json
```
```
updated_at
```

Tracker updates (markdown — user-facing dashboard): Project state layer data into MORPHIQ-TRACKER.md sections 5-9 and 14 (SoV, SoV Trend, Citations, Prompts, Competitors, Run History). Update remaining sections (1-4, 10-13) per tracker-spec.md rules.

For tracker specification, read

references/tracker-spec.md

. For state layer specification, read

references/state-layer.md

Step 8: Produce Delta Report

Assemble JSON (

PIPELINE.md

§4): SoV metrics, citations, per-provider data, competitors, flagged actions, content queue. Loops back to morphiq-rank.

Three Ongoing Workflows

Workflow A: Content Optimization

Identify pages with declining SoV or lost citations
Feed to morphiq-build (existing content path)
Re-track to measure impact

Workflow B: Content Creation

Collect prompts where brand is absent
Identify competitor citation sources
Generate content briefs for missing coverage
Feed to morphiq-build (new content path)

Workflow C: Query Fanout Expansion

Run

scripts/analyze-fanout.py --state-dir morphiq-track/

with optional

--scan-report

for page inventory and simulated queries

Script extracts sub-queries from latest track results, merges with scan simulated queries (fills Perplexity/Gemini gap)
Compares against site page inventory to identify unanswered sub-queries
Extracts competitor citation sources for each unanswered sub-query
Generates content briefs prioritized by citation weight (
```
site:
```
2x, citation-producing 1.5x, silent 0.5x)
Output feeds Delta Report
```
content_creation_queue
```
via
```
--fanout
```
flag on generate-report.py
Update MORPHIQ-TRACKER.md §12 (Query Fanout Coverage) and §13 (Content Creation Queue) with new entries

Reference Files

File	Purpose
`references/prompt-taxonomy.md`	Prompt types, GEO categories, fanout depth, generation rules
`references/share-of-voice.md`	SoV formulas, mention types, invisible SoV, competitive tracking
`references/provider-strategies.md`	Provider config, models, response analysis pipeline
`references/query-targets.md`	Provider selection, distribution, citation categories, GEO score
`references/delta-scoring.md`	Delta calculation, significance thresholds, flagged actions
`references/tracker-spec.md`	Full MORPHIQ-TRACKER.md specification (14 sections)
`references/state-layer.md`	JSON state layer: directory structure, file schemas, read/write rules, sync rules

morphiq-track

NPX Install

Tags

SKILL.md Content

Pipeline Position

Purpose

Workflow

Step 0: Initialize or Load State

Step 1: Generate Prompts

Step 2: Query AI Providers

Step 3: Analyze Responses

Step 4: Compute GEO Score

Step 5: Compute Share of Voice

Step 6: Compute Deltas

Step 7: Update State Layer and MORPHIQ-TRACKER.md

Step 8: Produce Delta Report

Three Ongoing Workflows

Workflow A: Content Optimization

Workflow B: Content Creation

Workflow C: Query Fanout Expansion

Reference Files