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stack-detect

stack-detect

github.com/hainrixzData & databases2170 installsUpdated 3d ago
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About stack-detect

A deterministic database-stack detector used as the entry point for database audits. It identifies paradigms, engines, ORMs, and sources of truth, then routes each detected store to the applicable audit modules and scoring profile.

Key features

  1. 01Detects database stack components
  2. 02Returns all detected stores
  3. 03Ranks schema sources of truth
  4. 04Routes stacks to audit modules
  5. 05Reports confidence and scanned files

Use cases

  1. 01Selecting audit modules for a project
  2. 02Routing multi-store database audits
  3. 03Determining the authoritative schema source

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/hainrixz/claude-db --skill stack-detect

After installing, type/stack-detectin Claude Code to confirm it loaded.

Details

From the project README

stack-detect (Phase 1 — detect)

Classifies the project into one or more { paradigm, engine, orm, platform, source_of_truth, confidence, files } stacks. This is the deterministic front door of every audit: it picks the paradigm profile the scorer re-normalizes over (scripts/score.mjs → PROFILES) and the module set each auditor runs.

What to do

  1. Resolve the target directory ($ARGUMENTS path, else the project root / cwd).

  2. Run the detector and read its JSON stdout:

    node scripts/detect-stack.mjs --dir "<dir>"

    It returns { stacks: [...], files_scanned }. Never edit or re-implement it — it is tested foundation.

  3. Empty stacks → there is nothing to audit offline. Do not guess an engine. Route the user to /claude-db:start (the guided wizard) or invite a plain-language description of the intended database. Surface the detector's hint verbatim.

Source-of-truth precedence (see references/detection-signals.md)

When several sources describe one database, authority is, in order:

  1. Live Tier-1 introspection (via the introspect skill) — beats any file.
  2. Declarative / generated artifact — schema.prisma, Drizzle *_snapshot.json, structure.sql, schema.rb, generated migration SQL → confidence: established.
  3. Migration SQL over ORM program source.
  4. ORM program source (schema.ts, models.py, Mongoose/CDK) → confidence: directional.

directional stacks parsed from program source never raise a severity-5 cap — they nudge toward a generated artifact or Tier-1. When a declarative artifact and ORM source disagree, that is schema drift, owned by db-migration-safety (M22).

Routing emitted per stack

For each detected stack, emit the routing the orchestrator needs:

  • paradigm → selects the score.mjs profile and --paradigm flag (relational | document | key-value | wide-column | vector | time-series | graph).
  • module set → M0 engine-selection (recommendation, unscored) + M1..M22 filtered to the paradigm's profile modules. Categories whose modules emit no scored finding go inactive — e.g. a document store is never penalised for missing foreign keys.
  • source_of_truth + confidence → the parse reliability ceiling carried into every finding (Tier 0(a) reliable vs 0(b) best-effort).

Multi-store

Return all stacks (e.g. Postgres primary + Redis cache). The orchestrator audits each, scores each with its own --paradigm, and rolls up worst-of-across-stores per axis (see references/scoring-model.md → Multi-store rollup), naming the flooring store.

Output

Emit the raw stacks array, the per-stack routing (paradigm profile + module set), and the source_of_truth precedence note. Keep narration tight. Respond in the user's language (EN/ES).

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