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Tender & Ecosystem Intelligence Commands

These commands support the competitive analysis and ecosystem gap-finding workflow. They operate on the concurrentie-analyse/intelligence.db SQLite database and require the database to exist before running.

/tender-scan​

Phase: Intelligence Gathering

Scrape TenderNed for new tenders, import them into SQLite, and classify unclassified tenders by software category using a local Qwen model.

Usage:

/tender-scan

What it does:

  1. Runs concurrentie-analyse/tenders/scrape_tenderned.py to fetch fresh data
  2. Imports new tenders into the intelligence database
  3. Classifies unclassified tenders using Qwen via localhost:11434
  4. Reports new tenders found, classified, and any new gaps detected

Requires: Local Qwen model running on Ollama (http://localhost:11434)

/tender-status​

Phase: Intelligence Monitoring

Show a dashboard of the tender intelligence database — totals by source, category, status, gaps, and recent activity.

Usage:

/tender-status

What it does:

  • Queries concurrentie-analyse/intelligence.db for live stats
  • Shows tenders by source, status, and category (top 15)
  • Highlights categories with Conduction coverage vs gaps
  • Shows top integration systems and ecosystem gaps

Model: Checked at run time when invoked standalone — stops if on Opus (no reasoning needed, wastes quota), warns if on Sonnet and offers to switch. Haiku is the right fit for this task. Model check is skipped when this skill is called from within another skill.

/tender-gap-report​

Phase: Gap Analysis

Generate a gap analysis report — software categories that appear in government tenders but have no Conduction product.

Usage:

/tender-gap-report

What it does:

  1. Queries the database for categories with tenders but no conduction_product
  2. Generates a markdown report at concurrentie-analyse/reports/gap-report-{date}.md
  3. Includes top 5 gaps with tender details, organisations, and key requirements
  4. Cross-references with application-roadmap.md to flag already-tracked gaps
  5. Recommends which gaps to investigate first

/ecosystem-investigate <category>​

Phase: Competitive Research

Deep-dive research into a software category — find and analyze open-source competitors using GitHub, G2, Capterra, AlternativeTo, and TEC.

Usage:

/ecosystem-investigate bookkeeping

What it does:

  1. Loads category context and related tenders from the intelligence database
  2. Uses the browser pool (browser-1 through browser-5) to scrape 5-10 competitors from multiple source types
  3. Creates competitor profiles in concurrentie-analyse/{category}/{competitor-slug}/
  4. Inserts competitors and feature data into the database with provenance tracking
  5. Presents a comparison table and recommendation for Nextcloud ecosystem fit

Model: Checked at run time — stops if on Haiku. Asks which model to use and explains how to switch if the choice differs from the active model. Sonnet for most categories. Opus for high-stakes or complex categories where strategic depth matters.

/ecosystem-propose-app <category>​

Phase: Product Planning

Generate a full app proposal for a software category gap, using tender requirements and competitor research as input.

Usage:

/ecosystem-propose-app bookkeeping

What it does:

  1. Gathers all tenders, requirements, competitors, and integrations for the category
  2. Generates a structured proposal following the template in concurrentie-analyse/application-roadmap.md
  3. Appends the proposal to application-roadmap.md
  4. Inserts the proposal into the app_proposals database table
  5. Optionally bootstraps the app with /app-create

Model: Checked at run time — stops if on Haiku. Asks which model to use and explains how to switch if the choice differs from the active model. Sonnet for most proposals. Opus for high-stakes proposals where architectural fit and market analysis need extra depth.

/intelligence-update [source]​

Phase: Intelligence Maintenance

Pull latest data from external sources into the intelligence database. Syncs sources that are past their scheduled interval.

Usage:

/intelligence-update              # sync all sources that are due
/intelligence-update all          # force sync every source
/intelligence-update wikidata-software  # sync one specific source

Sources and intervals:

SourceInterval
tenderned24h
wikidata-software7 days
wikipedia-comparisons7 days
awesome-selfhosted7 days
github-issues7 days
dpg-registry7 days
developers-italia7 days
gemma-releaseyearly

What it does:

  1. Checks source_syncs table for overdue sources
  2. Runs concurrentie-analyse/scripts/sync/sync_{source}.py for each
  3. Updates sync status, records count, and error messages
  4. Displays a summary table of all sources with their sync status

Model: Checked at run time when invoked standalone — stops if on Opus (no reasoning needed, wastes quota), warns if on Sonnet and offers to switch. Haiku is the right fit for this task. Model check is skipped when this skill is called from within another skill.

Tender Intelligence Workflow​

/tender-scan              (fetch & classify new tenders)
       │
       ▼
/tender-status            (review dashboard)
       │
       ▼
/tender-gap-report        (identify gaps)
       │
       ▼
/ecosystem-investigate    (research competitors for top gap)
       │
       ▼
/ecosystem-propose-app    (generate app proposal)
       │
       ▼
/app-design          (design the new app)

Keep data fresh: Run /intelligence-update weekly and /tender-scan daily to keep the database current.