Freshie Inventory Manager

Interactive command center for the freshie ecosystem inventory database.

Current DB Status

!sqlite3 freshie/inventory.sqlite "SELECT 'Run #' || id || ' — ' || rundate || ' | Plugins: ' || totalplugins || ' | Skills: ' || totalskills || ' | Packs: ' || COALESCE(totalpacks, 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:

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:

python3 freshie/scripts/batch-remediate.py --dry-run

1. Present the changes that would be made.

1. Use AskUserQuestion:

REMEDIATION PREVIEW
================================================================
{summary of proposed changes}

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

1. Only if user selects "Execute":

python3 freshie/scripts/batch-remediate.py --all --execute

1. 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.

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:

Always filter to latest run: WHERE runid = (SELECT MAX(id) FROM discoveryruns)

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

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 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

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:

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

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.

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:

python3 ~/.claude/skills/email/scripts/md-to-pdf.py /tmp/freshie-report-{date}.md /tmp/freshie-report-{date}.pdf --style professional

1. 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

Examples

See 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 — 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