---
name: freshie-inventory
description: "Manage the freshie ecosystem inventory database \u2014 a CMDB tracking\
  \ all plugins,\nskills, packs, and compliance grades across 50 SQLite tables. Use\
  \ when checking\necosystem health, running discovery scans, validating compliance,\
  \ remediating\nissues, querying inventory data, comparing runs, exporting data,\
  \ or generating\nstatus reports. Trigger with \"freshie status\", \"inventory scan\"\
  , \"ecosystem audit\",\n\"grade report\", \"compliance check\", \"remediate skills\"\
  , \"query freshie\",\n\"compare runs\", \"export grades\", or \"freshie report\"\
  .\n"
allowed-tools: Read, Write, Edit, Bash(sqlite3:*), Bash(python3:*), Bash(node:*),
  Bash(mkdir:*), Bash(wc:*), Glob, Grep, AskUserQuestion, Skill, Task
version: 1.0.0
author: Jeremy Longshore <jeremy@intentsolutions.io>
license: MIT
tags:
- database
- inventory
- freshie
- compliance
- ecosystem
- sqlite
compatibility: Designed for Claude Code, also compatible with Codex and OpenClaw
---
# Freshie Inventory Manager

Interactive command center for the freshie ecosystem inventory database.

## Current DB Status

!`sqlite3 freshie/inventory.sqlite "SELECT 'Run #' || id || ' — ' || run_date || ' | Plugins: ' || total_plugins || ' | Skills: ' || total_skills || ' | Packs: ' || COALESCE(total_packs, 0) FROM discovery_runs ORDER BY id DESC LIMIT 3;" 2>/dev/null || echo "DB not found at freshie/inventory.sqlite"`

!`sqlite3 freshie/inventory.sqlite "SELECT grade || ': ' || COUNT(*) FROM skill_compliance GROUP BY grade ORDER BY grade;" 2>/dev/null`

## Overview

The freshie database is the single source of truth for ecosystem-wide metrics — plugin counts,
skill compliance grades, pack coverage, anomaly detection, and historical trends across versioned
discovery runs. This skill is an **interactive wizard** — it always asks what you want to do,
then delegates heavy operations to specialized subagents.

**Database location:** `freshie/inventory.sqlite` (50 tables, versioned by `run_id`)

**Key scripts:**
- `freshie/scripts/rebuild-inventory.py` — full repo scan, creates new discovery run
- `freshie/scripts/batch-remediate.py` — auto-fix compliance issues
- `scripts/validate-skills-schema.py` — enterprise validation with DB population

## Prerequisites

- `sqlite3` CLI available on PATH
- `python3` with `pyyaml` installed
- Working directory is the repo root (`claude-code-plugins/`)
- Database exists at `freshie/inventory.sqlite`
- `/email` skill installed (for PDF report emailing)

## Instructions

### Step 1: Present Main Menu

When invoked, ALWAYS start by presenting this menu using AskUserQuestion:

```
FRESHIE INVENTORY COMMAND CENTER
================================================================

What would you like to do?

 1. Dashboard        — Current status, grades, staleness
 2. Discovery Scan   — Full repo scan, create new run
 3. Compliance Check — Enterprise validation + DB population
 4. Remediation      — Batch fix compliance issues
 5. Query            — Ad-hoc SQLite queries
 6. Compare Runs     — Delta analysis between runs
 7. Export Data      — CSV exports to freshie/exports/
 8. Anomaly Scan     — Data quality + outlier detection
 9. Pack Coverage    — SaaS pack completeness metrics
10. Full Audit       — Scan + validate + report (end-to-end)
11. Report Only      — Generate summary from existing data
```

Use AskUserQuestion with these options. If the user's initial prompt already contains
a clear intent (e.g., "freshie status"), skip the menu and route directly.

### Step 2: Execute Chosen Workflow

Based on selection, follow the matching workflow below. Every workflow ends with
Step 3 (Email Report).

---

## Workflow A: Dashboard

Run these queries and present as a formatted dashboard:

```bash
sqlite3 freshie/inventory.sqlite "SELECT id, run_date, total_plugins, total_skills, COALESCE(total_packs,0) FROM discovery_runs ORDER BY id DESC LIMIT 1;"
sqlite3 freshie/inventory.sqlite "SELECT grade, COUNT(*) FROM skill_compliance WHERE run_id=(SELECT MAX(id) FROM discovery_runs) GROUP BY grade ORDER BY grade;"
sqlite3 freshie/inventory.sqlite "SELECT CAST(julianday('now') - julianday(run_date) AS INTEGER) FROM discovery_runs ORDER BY id DESC LIMIT 1;"
sqlite3 freshie/inventory.sqlite "SELECT 'plugins', COUNT(*) FROM plugins WHERE run_id=(SELECT MAX(id) FROM discovery_runs) UNION ALL SELECT 'skills', COUNT(*) FROM skills WHERE run_id=(SELECT MAX(id) FROM discovery_runs) UNION ALL SELECT 'packs', COUNT(*) FROM packs WHERE run_id=(SELECT MAX(id) FROM discovery_runs) UNION ALL SELECT 'anomalies', COUNT(*) FROM anomalies WHERE run_id=(SELECT MAX(id) FROM discovery_runs);"
# Core vs SaaS pack breakdown
sqlite3 freshie/inventory.sqlite "SELECT CASE WHEN path LIKE '%saas-packs%' THEN 'saas-pack-skills' ELSE 'core-skills' END as type, COUNT(*) FROM skills WHERE run_id=(SELECT MAX(id) FROM discovery_runs) GROUP BY type;"
```

Present as:
```
FRESHIE INVENTORY DASHBOARD
============================
Last Scan:     Run #{id} — {date} ({days} days ago)
Plugins:       {n}
Skills:        {n} total
  Core:        {n} (hand-crafted plugin skills)
  SaaS Packs:  {n} (auto-generated pack skills)
Packs:         {n}

Grade Distribution:
  A: {n}  B: {n}  C: {n}  D: {n}  F: {n}

Staleness: {Fresh (<3d) | Stale (3-7d) | CRITICAL (>7d)}
```

If Critical (>7 days), recommend a discovery scan.

---

## Workflow B: Discovery Scan

**Delegate to the discovery-scanner subagent** via the Agent tool:

```
Launch Agent: discovery-scanner
Prompt: "Run a full freshie discovery scan. Show current state first, execute
rebuild-inventory.py, then report the delta (plugin/skill count changes)
compared to the previous run."
```

The subagent handles the long-running scan in isolation and returns the delta report.

---

## Workflow C: Compliance Check

**Delegate to the compliance-validator subagent** via the Agent tool:

```
Launch Agent: compliance-validator
Prompt: "Run enterprise compliance validation against the freshie DB.
Execute: python3 scripts/validate-skills-schema.py --enterprise --populate-db freshie/inventory.sqlite --verbose
Then summarize: grade distribution with percentages, and list all D/F grade skills."
```

The subagent runs the full validation pipeline and returns a structured summary.

---

## Workflow D: Remediation

**CRITICAL: Always dry-run first, then confirm before executing.**

1. Run dry-run:
```bash
python3 freshie/scripts/batch-remediate.py --dry-run
```

2. Present the changes that would be made.

3. Use AskUserQuestion:
```
REMEDIATION PREVIEW
================================================================
{summary of proposed changes}

Proceed?
  - Execute — Apply all fixes
  - Cancel  — Abort, no changes made
```

4. Only if user selects "Execute":
```bash
python3 freshie/scripts/batch-remediate.py --all --execute
```

5. After execution, run Workflow C (Compliance Check) to measure improvement.

---

## Workflow E: Query

For ad-hoc queries, load the pre-built query library from [common-queries.md](references/common-queries.md).

Match the user's question to the closest pre-built query. If no match, construct a custom
query against the freshie schema using these key tables:

| Table | Contents |
|-------|----------|
| `plugins` | name, category, version, path |
| `skills` | name, plugin_path, has_references, has_scripts |
| `packs` | name, skill_count, category |
| `skill_compliance` | score, grade, error_count, warning_count, is_stub |
| `plugin_compliance` | plugin-level roll-up scores |
| `content_signals` | word_count, code_block_count |
| `anomalies` | detected data quality issues |
| `discovery_runs` | run history with timestamps |

Always filter to latest run: `WHERE run_id = (SELECT MAX(id) FROM discovery_runs)`

After showing results, use AskUserQuestion to offer follow-up:
```
Results shown. What next?
  - Refine query  — Modify or drill deeper
  - Export to CSV — Save results to file
  - Back to menu  — Return to main menu
```

---

## Workflow F: Compare Runs

```bash
sqlite3 freshie/inventory.sqlite "SELECT id, run_date, total_plugins, total_skills, COALESCE(total_packs,0) FROM discovery_runs ORDER BY id;"
```

If more than 2 runs exist, use AskUserQuestion to let user pick which two to compare.
Default to the two most recent.

Use the "Historical Trends" queries from [common-queries.md](references/common-queries.md) for:
- Grade distribution comparison between runs
- Skills that changed grade (upgrades/downgrades with score delta)
- New skills added since previous run
- Skills removed since previous run

---

## Workflow G: Export Data

```bash
mkdir -p freshie/exports
```

Use AskUserQuestion to let user pick what to export:
```
EXPORT OPTIONS
================================================================
What should I export?

  - Skill Grades    — All skill compliance scores + grades
  - Plugin Inventory — All plugins with category and version
  - Pack Coverage   — Pack names, skill counts, categories
  - Full Dump       — All three exports
  - Custom Query    — Export any query result to CSV
```

Then run the appropriate export:
```bash
sqlite3 -header -csv freshie/inventory.sqlite "{query}" > freshie/exports/{filename}.csv
```

Report file paths and row counts.

---

## Workflow H: Anomaly Scan

**Delegate to the anomaly-detector subagent** via the Agent tool:

```
Launch Agent: anomaly-detector
Prompt: "Run anomaly detection on the freshie inventory DB. Check:
1. Stored anomalies from the latest discovery run
2. Skills with word count < 50 (likely stubs)
3. Plugins with no skills
4. Skills with high template-text density (>10%)
5. Duplicate files
Report all findings grouped by severity."
```

---

## Workflow I: Pack Coverage

```bash
sqlite3 freshie/inventory.sqlite "SELECT name, skill_count, category FROM packs WHERE run_id=(SELECT MAX(id) FROM discovery_runs) ORDER BY skill_count DESC;"
```

Also flag packs below minimum viable (< 3 skills) and show grade distribution within packs.
Use pack coverage queries from [common-queries.md](references/common-queries.md).

---

## Workflow J: Full Audit

This is the power workflow — runs everything end-to-end:

1. **Discovery Scan** (Workflow B) — via subagent
2. **Compliance Check** (Workflow C) — via subagent
3. **Anomaly Scan** (Workflow H) — via subagent
4. **Report Generation** (Workflow K) — compile all results

Launch steps 1-3 as parallel subagents, then compile the report when all complete.

---

## Workflow K: Report Only

Generate a summary report from existing data (no new scans). Gather dashboard data
(Workflow A queries) and compile:

```
FRESHIE ECOSYSTEM REPORT — {date}
================================================================

Discovery: Run #{id} ({date})
  Plugins: {n} | Skills: {n} | Packs: {n}

Compliance (enterprise tier):
  A: {n} ({pct}%) | B: {n} ({pct}%) | C: {n} ({pct}%) | D: {n} ({pct}%)

  Average score: {avg}/100

Since last run:
  Plugins: {+/-delta} | Skills: {+/-delta}
  Grade upgrades: {n} | Downgrades: {n}

Top Issues:
  1. {issue}
  2. {issue}
  3. {issue}

Recommendations:
  - {action}
  - {action}
================================================================
```

---

### Step 3: Email PDF Report

After ANY workflow completes, use AskUserQuestion to offer the report:

```
WORKFLOW COMPLETE
================================================================
{Brief summary of what was done}

Would you like a PDF report emailed?
  - Yes, email me      — Generate PDF + send to jeremy@intentsolutions.io
  - Yes, email someone — Specify recipient
  - Save PDF only      — Generate PDF, no email
  - No thanks          — Done
```

If the user wants a report:

1. **Generate markdown report** — write the workflow results to `/tmp/freshie-report-{date}.md`
2. **Convert to PDF** using the email skill's converter:
```bash
python3 ~/.claude/skills/email/scripts/md-to-pdf.py /tmp/freshie-report-{date}.md /tmp/freshie-report-{date}.pdf --style professional
```
3. **Send via /email skill** — invoke the Skill tool with `skill: "email"` and args describing:
   - To: recipient (default: jeremy@intentsolutions.io)
   - Subject: "Freshie Ecosystem Report — {date}"
   - Body: brief summary
   - Attachment: the generated PDF

## Output

All operations produce structured text output. Dashboards use fixed-width formatting.
Query results use table format. Deltas show +/- indicators. CSV exports write to
`freshie/exports/`. PDF reports write to `/tmp/` and optionally email.

## Error Handling

| Error | Cause | Solution |
|-------|-------|----------|
| "DB not found" | Missing `freshie/inventory.sqlite` | Run `python3 freshie/scripts/rebuild-inventory.py` to create |
| "no such table" | DB schema outdated or empty | Run a fresh discovery scan (Workflow B) |
| Empty grades | Compliance not yet populated | Run compliance validation (Workflow C) |
| `rebuild-inventory.py` fails | Missing `pyyaml` | `pip install pyyaml` |
| Stale data (>7 days) | No recent scans | Run discovery scan, then compliance |
| PDF generation fails | Missing `weasyprint` | `pip install weasyprint` |
| Email send fails | Missing env vars | Check `~/.env` for GMAIL_APP_PASSWORD |

## Examples

See [examples.md](references/examples.md) for detailed input/output examples covering all workflows:
- Quick status check (direct intent, skips menu)
- Full audit with email PDF report (parallel subagents)
- Ad-hoc query with CSV export follow-up
- Remediation cycle (dry-run, confirm, re-validate)
- Compare discovery runs (delta analysis)
- Pack coverage analysis

## Resources

- [Common Queries](references/common-queries.md) — pre-built SQLite query library: grades, stubs, plugins, packs, content quality, trends, anomalies, field analysis, cross-references
- `freshie/scripts/rebuild-inventory.py` — full repo scanner, versioned discovery runs
- `freshie/scripts/batch-remediate.py` — compliance fix engine (`--dry-run`, `--all --execute`)
- `scripts/validate-skills-schema.py` — universal validator (`--enterprise --populate-db`)
- `freshie/inventory.sqlite` — the database (50 tables, versioned by `run_id`)
- `~/.claude/skills/email/scripts/md-to-pdf.py` — markdown to PDF converter
- `/email` skill — email sending with attachments