analytics

name	analytics
description	Vault-level content analytics — file creation trends, skill adoption, contact engagement, content distribution, pipeline outcome metrics (insights/tasks/changelog per meeting), and unprocessed backlog detection. Outputs to _analytics/ folder.
user-invocable	true
argument-hint	["overview|skills|contacts|pipeline|backlog|help"]

/analytics -- Vault Content Analytics

Analyse the vault as a dataset — file metadata (names, dates, paths, counts), not file contents. Answers "how is my system performing?" rather than "what did I learn from this conversation?"

Standalone skill — no dependency on ops-base or ops-config.

Key distinction from /insights: The insights skill extracts knowledge from conversations (decisions, learnings, patterns about people and projects). This skill analyses the vault itself — creation velocity, skill adoption, engagement frequency, content distribution.

---

Design Principles

• Metadata and structured fields only. Read filenames, paths, dates, and file sizes. Never read prose content — the only in-file reads allowed are lightweight classification (H1 heading, first line) and structured-field scans for the pipeline subcommand: regex-matching date:/type: lines in _insights.yaml, created: lines in _tasks.yaml, and - **YYMMDD: bullets in CHANGELOG.md. Field values only; summaries, rationale, and notes are never read.
• Non-destructive. Only writes to _analytics/ — never modifies existing files.
• Snapshot-based. Each run produces a dated snapshot. Historical snapshots enable trend comparison.
• Privacy-aware. Contact names appear in analytics (they are folder names, not extracted content). Mark private contacts with private: true in _meta.yaml to exclude them from output.

---

Output Location

All output goes to {vault_root}/_analytics/:

_analytics/
├── YYMMDD-vault-overview.md       <- /analytics overview
├── YYMMDD-skill-adoption.md       <- /analytics skills
├── YYMMDD-contact-engagement.md   <- /analytics contacts
├── YYMMDD-pipeline-report.md      <- /analytics pipeline
├── YYMMDD-backlog-report.md       <- /analytics backlog
└── .archive/                      <- older snapshots (auto-archived)

Archive policy: When writing a new snapshot, move any existing file of the same type to .archive/ with its date prefix preserved. Keep the latest of each type at the top level.

---

Vault Detection

Determine vault root:

1. Walk up from cwd looking for _contacts/ or _projects/ markers
2. Fallback: $OBSIDIAN_VAULT environment variable
3. Fallback: cwd itself

---

File Discovery

The core scanning step shared by all subcommands.

YYMMDD File Detection

Scan vault recursively (max depth 8, skip .archive/, .git/, node_modules/, clones/). Match files where the basename starts with 6 digits followed by a separator:

Pattern: /^(\d{6})[-_ ]/

Validate the 6 digits as a plausible date:

• Year (positions 1-2): 22-29 (2022-2029)
• Month (positions 3-4): 01-12
• Day (positions 5-6): 01-31

Metadata Collected Per File

Field	Source
date	Filename prefix (YYMMDD)
path	Relative path from vault root
directory	Parent directory (2 levels from vault root)
extension	File extension (.md, .txt, .pptx, etc.)
skill_type	Classified by path and filename keywords (see below)
contact	Extracted from _contacts/{name}/ path segment, if present

Skill Classification

Classify each file by path first, keywords second:

Step 1 — Path-based classification (highest confidence):

Path contains	Classification
_inbox/	inbox
_analytics/	analytics (skip — don't count own output)
_outbox/	outbox

Step 2 — Keyword-based classification (filename, case-insensitive):

Keywords	Classification
samtal, call, transcript	transcript
agenda, facilitator	agenda (dual-mode docs, CR-018 — check before preparation)
förberedelse, preparation, prep-	preparation
meeting, möte, standup, board, alignment, kickoff, weekly, sprint, retro, sync, sammanfattning, summary	ops/meeting

Step 3 — Directory-context classification (for files that didn't match keywords):

Directory pattern	Classification
meetings/ or moten/ or möten/ anywhere in path	ops/meeting
ops/ anywhere in path	ops
_resor/ or resor/	travel
ppc/ or marketing/ or marknads	marketing
.txt extension	raw-text
.pptx, .pdf, .docx, .xlsx	office-doc
.png, .jpg, .jpeg, .heic	image

Step 4 — Fallback: uncategorized

Privacy Filtering (CR-009)

Before including a contact in named output:

1. Resolve classification for the contact folder:

   • If _meta.yaml exists and has classification field: use it
   • Else if _meta.yaml exists and has private: true: treat as personal
   • Else check folder name against privacy_defaults in base.yaml:
     • _contacts/a1-* or _contacts/a2* → family
   • Else default: professional

2. Apply filtering by subcommand:

   • /analytics contacts: Exclude family and personal contacts from output
   • /analytics overview, /analytics skills: Include all files in aggregate counts without contact attribution
   • /analytics backlog: Include all files (backlog is about content gaps, not people)

3. Never expose family/personal contact names in any output file. Aggregate their files into totals only.

---

Subcommands

overview -- Vault-wide metrics snapshot

Trigger: /analytics overview or /analytics (default subcommand)

Steps:

1. Run file discovery (scan vault)
2. Compute summary metrics:
   • Total YYMMDD files, date range, unique active dates, active months
   • Current pace (files/day for current quarter)
   • Active-day density — active days / calendar days for the current quarter (e.g. 84/91), plus files per active day. The gap between files/day and files/active-day shows whether growth comes from busier days or from fewer idle days.
   • Trend direction (current quarter rate vs previous quarter rate)
3. Compute yearly totals with year-over-year growth multiplier
4. Compute content type × quarter pivot — the core analytical view showing how each skill type's volume evolves over time. Columns: transcript, ops/meeting, preparation, ops, travel, raw-text, uncategorized, other. Use · for zero cells.
5. Compute content type × month pivot (last 12 months) — same columns, monthly granularity for recent trends
6. Compute monthly activity bar chart (last 12 months)
7. Compute quarterly trend table with deltas
8. Compute distributions:
   • By skill type — table with count and percentage
   • By top-level directory — top 15 areas with count (private contacts aggregated as single anonymous row)
   • By file extension — count per extension
   • By day of week — weekday vs weekend pattern with bar chart
9. Compute top 5 busiest dates — date, day of week, count
10. Write to _analytics/YYMMDD-vault-overview.md

Output format:

# Vault Analytics — Overview
Generated: YYYY-MM-DD

---

## Summary

| Metric | Value |
|--------|-------|
| Total files | N |
| Date range | YYYY-MM-DD → YYYY-MM-DD |
| Active months | N |
| Avg files/month | N.N |
| Unique dates | N |
| Current pace | N.N files/day (QN YYYY) |
| Active days (current quarter) | N/N |
| Files per active day | N.N |
| Trend | ↑ N% vs previous quarter |

---

## Yearly Growth

| Year | Files | Growth |
|------|------:|-------:|
| YYYY | N | — |
| YYYY | N | N.Nx |

---

## Content Type × Quarter

| Quarter | transcript | ops/meeting | preparation | ops | travel | raw-text | uncategorized | other | **Total** |
|---------|------:|------:|------:|------:|------:|------:|------:|------:|------:|
| YYYY-QN | N| N| ·| N| ·| N| N| ·| **N** |
| YYYY-QN * | N| N| N| ·| N| ·| N| N| **N** |

\* partial quarter

---

## Content Type × Month (Last 12)

| Month | transcript | ops/meeting | preparation | ops | travel | raw-text | uncategorized | other | **Total** |
|-------|------:|------:|------:|------:|------:|------:|------:|------:|------:|
| YYYY-MM | N| N| ·| N| ·| N| N| ·| **N** |

---

## Monthly Activity (Last 12 Months)

| Month | Files | |
|-------|------:|---|
| YYYY-MM | N | ████████████ |

---

## Quarterly Trend

| Quarter | Files | Delta |
|---------|------:|------:|
| YYYY-QN | N | — |
| YYYY-QN | N | +N |
| YYYY-QN (partial) | N | -N |

---

## Content Distribution

### By Skill Type

| Type | Files | % |
|------|------:|----:|
| ops/meeting | N | N% |
| transcript | N | N% |
| preparation | N | N% |
| ... | | |

### By Directory (Top 15)

| Directory | Files |
|-----------|------:|
| org/meetings | N |
| *(private contacts, N folders)* | *N* |

### By File Extension

| Extension | Files |
|-----------|------:|
| .md | N |
| .txt | N |

### By Day of Week

| Day | Files | |
|-----|------:|---|
| Monday | N | █████████████ |

---

## Busiest Dates

| Date | Day | Files |
|------|-----|------:|
| YYYY-MM-DD | Thu | N |

---

skills -- Skill adoption analysis

Trigger: /analytics skills

Steps:

1. Run file discovery
2. Build skill share pivot (absolute) — same columns as the overview pivot (transcript, ops/meeting, preparation, ops, travel, raw-text, uncategorized, other), one row per quarter. Use · for zero cells.
3. Build skill share pivot (percentage) — same layout but percentages per quarter.
4. For each skill type, compute per-skill summary:
   • First appeared — which quarter the skill type first produced files
   • Total files across all time
   • Current share — percentage in the most recent full quarter, with ↑/↓/→ arrow vs previous quarter
   • Peak quarter — quarter with most files from this skill
5. Compute structured vs unstructured ratio by quarter. Structured = transcript + ops/meeting + preparation + ops. Unstructured = everything else. Show absolute counts, and a trend line from first to last full quarter.
6. Write to _analytics/YYMMDD-skill-adoption.md

Output format:

# Vault Analytics — Skill Adoption
Generated: YYYY-MM-DD

---

## Skill Share Over Time (absolute)

| Quarter | transcript | ops/meeting | preparation | ops | travel | raw-text | uncategorized | other | Total |
|---------|------:|------:|------:|------:|------:|------:|------:|------:|------:|
| YYYY-QN | N| N| ·| N| ·| N| N| ·| N |
| YYYY-QN * | N| N| N| ·| N| ·| N| N| N |

## Skill Share Over Time (%)

| Quarter | transcript | ops/meeting | preparation | ops | travel | raw-text | uncategorized | other |
|---------|------:|------:|------:|------:|------:|------:|------:|------:|
| YYYY-QN | N%| N%| ·| N%| ·| N%| N%| · |

---

## Per-Skill Trends

### transcript
- First appeared: YYYY-QN
- Total files: N
- Current share: N% (↑ vs previous quarter N%)
- Peak quarter: YYYY-QN (N files)

### ops/meeting
[Same structure for each skill with >0 files]

---

## Structured vs Unstructured

| Quarter | Structured | Unstructured | Structured % |
|---------|----------:|-------------:|-------------:|
| YYYY-QN | N | N | N% |

Trend: Structured content share grew from **N%** (YYYY-QN) to **N%** (YYYY-QN).

---

contacts -- Contact engagement analysis

Trigger: /analytics contacts

Steps:

1. Run file discovery, filtered to _contacts/ paths
2. Apply privacy filter — resolve classification per contact (see Privacy Filtering section). Exclude family and personal contacts from named output. Report count and total files of excluded contacts as an anonymous summary line.
3. For each remaining (professional) contact, compute:
   • Total file count
   • First and last file dates (as YYYY-MM)
   • Active months count
   • Peak month (month with most files, with count)
   • Average files per active month
   • Quarterly activity map (for timeline visualisation)
4. Compute contact lifecycle metrics:
   • New contacts by year — when contacts first appeared, with cumulative total
   • Active contacts per quarter — contacts with at least 1 file in the quarter
5. Sort contacts by total file count descending
6. Generate activity timelines — a monospace block showing quarterly engagement density for top 20 contacts using heat notation: · = 0, ░ = 1-2, ▒ = 3-5, ▓ = 6-10, █ = 11+
7. Write to _analytics/YYMMDD-contact-engagement.md

Output format:

# Vault Analytics — Contact Engagement
Generated: YYYY-MM-DD

*N private contacts excluded (N files in aggregate totals only)*

---

## Top Contacts (by file count)

| Contact | Files | Span | Active mo | Peak month | Avg/mo |
|---------|------:|------|----------:|-----------|-------:|
| name | N | YYYY-MM → YYYY-MM | N | YYYY-MM (N) | N.N |

---

## Activity Timelines

Contact Q4 Q1 Q2 Q3 Q4 Q1 Q2 contact-name (N) · ░ ▒ ▓ █ ▒ ·

Legend: · = 0 ░ = 1-2 ▒ = 3-5 ▓ = 6-10 █ = 11+

---

## Network Growth

| Year | New contacts | Cumulative |
|------|------------:|----------:|
| YYYY | N | N |

## Active Contacts Per Quarter

| Quarter | Active contacts |
|---------|----------------:|
| YYYY-QN | N |

---

pipeline -- Input → meeting docs → outcomes over time

Trigger: /analytics pipeline

The document counts alone are not the full picture — a meeting's outcomes live in structured files the other subcommands never touch. This subcommand traces the whole chain: what came in, what was documented, and what it produced.

Data sources (structured fields only — never prose):

Layer	Source	Field scanned
Insights	_insights.yaml (all folders)	date: + preceding type: per entry
Tasks created	_tasks.yaml (v2, all folders)	created: per task
CHANGELOG entries	CHANGELOG.md (all folders)	- **YYMMDD: bullet dates
Outbox packages	_outbox/** file discovery	filename YYMMDD prefix
New contacts	_contacts/<name>/	earliest YYMMDD file per folder

Steps:

1. Run file discovery (same scan as overview)
2. Scan the structured sources above
3. Build the grouped chain table — quarters as columns (metrics as row groups, so the whole chain aligns vertically). Collapse quarters before the first meaningful year into a single ≤YYYY column to keep width manageable. Row groups:
   • INPUT: transcripts, raw text (.txt)
   • MEETING DOCS: summaries/notes, agenda/facilitator, preparations
   • OUTCOMES: insights total + one row per insight type (largest types first, small ones aggregated as "other"), tasks created, CHANGELOG entries, outbox packages, new contact folders
4. Compute derived ratios for quarters where the systems are fully active: insights per meeting+transcript, tasks per meeting+transcript, CHANGELOG entries per meeting+transcript
5. Compute per-day averages by quarter: active days / calendar days, files/day, meetings+transcripts/day, insights/day, tasks/day, files per active day (partial quarters use elapsed days)
6. Write to _analytics/YYMMDD-pipeline-report.md

Report structure — two parts, mandatory:

• Part 1 — Overview: headline totals table, a numbered list of interpreted findings (adoption inflections, ratio shifts, what converged or diverged — written as conclusions, not table prose), and the measurement notes.
• Part 2 — Deep dive: the grouped chain table, derived ratios, per-day averages, and any monthly detail.

Measurement notes (mandatory in every pipeline report):

• Outcome systems have adoption dates: a · before a system existed means "not yet measured", not "nothing happened". State each source's start-of-record explicitly (e.g. insights backfilled with extraction dates, tasks v2 introduction).
• CHANGELOG entries carry the original meeting date and are therefore the best longitudinal outcome proxy.
• Action items inside summary tables are NOT counted (would require prose reads); the task count is the nearest proxy.
• Mark partial quarters/months and use elapsed days for their per-day rates.

Output format (Part 2 core table):

| Group / metric | ≤YYYY | YY-Q1 | YY-Q2 | ... | **Total** |
|---|---:|---:|---:|---:|---:|
| **INPUT** | | | | | |
|   Transcripts | N | N | N | ... | **N** |
|   Raw text (.txt) | N | N | N | ... | **N** |
| **MEETING DOCS** | | | | | |
|   Summaries / notes | N | N | N | ... | **N** |
|   Agenda/facilitator | · | N | N | ... | **N** |
|   Preparations | · | N | N | ... | **N** |
| **OUTCOMES** | | | | | |
|   Insights (total) | · | · | N | ... | **N** |
|     — learning | · | · | N | ... | **N** |
|     — pattern | · | · | N | ... | **N** |
|   Tasks created | · | · | N | ... | **N** |
|   CHANGELOG entries | N | N | N | ... | **N** |
|   Outbox packages | · | · | N | ... | **N** |
|   New contact folders | N | N | N | ... | **N** |

Per-day averages table:

| Metric | YY-Q1 | YY-Q2 | ... |
|---|---:|---:|---:|
| Active days (≥1 file) | N/N | N/N | ... |
| Files/day | N.N | N.N | ... |
| Meetings+transcripts/day | N.N | N.N | ... |
| Insights/day | · | N.N | ... |
| Tasks/day | · | N.N | ... |
| Files per active day | N.N | N.N | ... |

---

backlog -- Unprocessed content detection

Trigger: /analytics backlog

Identifies content that may benefit from processing through existing skills.

Steps:

1. Run file discovery
2. Detect unprocessed transcriptions:
   • Find .txt files with YYMMDD prefix (classified as raw-text)
   • These are likely raw transcriptions that haven't been processed through /transcript
   • Group by directory, sorted by count descending, show count and date range
3. Detect orphaned content:
   • Find directories containing YYMMDD-prefixed files but no CHANGELOG.md
   • Only include directories with 2+ files (single files are likely intentional one-offs)
   • Show top 15 by file count, with *(+ N more)* if truncated
4. Detect stale inbox items:
   • Read _inbox/_inbox.yaml if it exists
   • Count items with status: pending
   • Report age of oldest pending item
5. Detect insight gaps:
   • Find folders with CHANGELOG.md but no _insights.yaml
   • Count YYMMDD-prefixed files in each (potential insight yield)
   • Only include folders with 1+ transcript files
   • Show top 15 by transcript count, with *(+ N more)* if truncated
6. Write summary table at top with all four categories and suggested actions
7. Write to _analytics/YYMMDD-backlog-report.md

Output format:

# Vault Analytics — Backlog Report
Generated: YYYY-MM-DD

---

## Summary

| Category | Items | Potential action |
|----------|------:|-----------------|
| Raw text files (.txt) | N | `/transcript` or `/inbox` |
| Folders without CHANGELOG | N dirs, N files | Manual triage |
| Pending inbox items | N | `/inbox` process |
| Folders missing _insights.yaml | N dirs, ~N transcripts | `/insights reprocess` |

---

## Raw Text Files (Likely Unprocessed Transcriptions)

| Directory | Count | Date range |
|-----------|------:|-----------|
| _contacts/name | N | YYMMDD → YYMMDD |
| _projects/name | N | YYMMDD → YYMMDD |

---

## Folders Without CHANGELOG

| Folder | YYMMDD files |
|--------|-------------:|
| org/meetings/area | N |
| _contacts/name/subfolder | N |
| *(+ N more)* | |

---

## Pending Inbox Items

N items pending. Oldest: YYYY-MM-DD
(or: No pending items.)

---

## Insight Reprocessing Opportunities

| Folder | Transcripts | Has _insights.yaml |
|--------|------------:|-------------------:|
| _contacts/name | N | No |
| org/_projects/name | N | No |
| *(+ N more)* | | |

---

help -- Usage guide

Trigger: /analytics help

Output:

/analytics -- Vault Content Analytics
======================================

Analyse the vault as a dataset -- file metadata, not contents.

Usage:
  /analytics                      Vault overview (default)
  /analytics overview             Same as above
  /analytics skills               Skill adoption over time
  /analytics contacts             Contact engagement timelines
  /analytics pipeline             Input -> meeting docs -> outcomes chain
  /analytics backlog              Unprocessed content detection
  /analytics help                 This guide

Output: _analytics/ folder in vault root (one snapshot per run).

Data flow:
  YYMMDD-*.* files  ──>  /analytics  ──>  _analytics/YYMMDD-*.md
  (read filenames,        (classify,       (markdown snapshots,
   paths, dates)           aggregate)       one per subcommand)

Related skills:
  /insights          Extract knowledge FROM file contents
  /insights status   Count _insights.yaml coverage
  /daily-dashboard   Daily view (today's meetings + tasks)
  /analytics         Longitudinal view (trends over time)

---

Language

Output language follows the same resolution as other standalone skills:

1. If vault root CLAUDE.md specifies a language policy, follow it
2. Default: Swedish for section headers and labels, English for technical terms
3. Swedish text MUST use correct å, ä, ö characters

---

Notes

• The _analytics/ folder is created automatically on first run
• Old snapshots are archived to _analytics/.archive/ — never deleted
• This skill reads _insights.yaml/_tasks.yaml/CHANGELOG.md at field level only (dates and type enums for the pipeline subcommand) — entry contents (summaries, rationale, notes) remain the visualisation app's domain
• This skill does NOT read file prose (except optional H1 heading for display)
• Contact privacy is respected via _meta.yaml private: true
• The skill classification algorithm uses path first, keywords second — this avoids the ~17% miscount that pure keyword matching produces (e.g., acme/meetings/ files with descriptive names)
• Quarterly comparisons handle partial quarters gracefully — the current quarter is annualised for trend comparison