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Deeplake Dataset Stinger

Organization
legioncodeinc
deeplake-dataset-stinger

Design and maintain Wasp Nestmind Deep Lake datasets. Use for ColumnDef schemas, healing, versioned writes, or hybrid indexing. Read guides/ for the decision path.

Overview

Publisherlegioncodeinc
Repositoryvibe-coding-tools
Skill namedeeplake-dataset-stinger
Stars
94
Forks
44
Bundled files
19
LicenseAGPL-3.0-or-later
Links
Skill sourceRepository
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 19 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by legioncodeinc on GitHub. Read the source before you install it.

Installation

Install the Deeplake Dataset Stinger AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/legioncodeinc/vibe-coding-tools/tree/main/plugins/data-and-ai-tools/skills/deeplake-dataset-stinger
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/legioncodeinc/vibe-coding-tools.git /tmp/vibe-coding-tools
mkdir -p .claude/skills
cp -r /tmp/vibe-coding-tools/plugins/data-and-ai-tools/skills/deeplake-dataset-stinger .claude/skills/deeplake-dataset-stinger
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Deeplake Dataset Stinger in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Deeplake Dataset Stinger on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Deeplake Dataset Stinger is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

deeplake-dataset-stinger

You are equipping deeplake-dataset-wasp-drone - the Army's Deep Lake data architecture authority for Hivemind. This skill encodes the 7-table ColumnDef schema, the USING deeplake table model, FLOAT4[768] embedding layout, additive schema healing, append-only version-bump writes, the indexing decision tree (deeplake_index BM25, <#> vector, hybrid), DeeplakeApi querying discipline, SQL-guard hygiene, dataset versioning, and BYOC storage selection into opinionated, cite-everything guides.

Opinionation is the product. Say "single-source the schema in deeplake-schema.ts, heal additively, never IF NOT EXISTS, version-bump instead of UPDATE" - not "here are options".

First move on every invocation

  1. Read src/deeplake-schema.ts and src/deeplake-api.ts. Capture: the ColumnDef[] for the table(s) in play, which of the SEVEN tables (memory, sessions, skills, rules, goals, kpis, codebase) is touched, NOT NULL + DEFAULT pairings, embedding columns (FLOAT4[], 768-dim, nomic-embed-text-v1.5), JSONB columns, and the DeeplakeApi access pattern (retry set, Semaphore, 402 handling). Everything downstream depends on this.
  2. Classify the invocation - table design / schema review / schema-heal plan / indexing audit / query audit / versioning plan / storage-backend choice. Route to the matching guide(s) per the table below.
  3. Check guides/00-principles.md before writing any finding. The severity rubric, layering, and cross-Drone handoff rules live there.

Routing table

InvocationPrimary guide(s)Output
New table / greenfield schema01-schema-design.md + 02-indexing.mdtemplates/schema-spec.md + starter ColumnDef[] in templates/columndef-table-spec.ts
Schema review01-schema-design.md + 00-principles.mdFindings report at library/qa/deeplake/<date>-schema-review.md (standalone) or library/requirements/features/feature-<###>-<title>/reports/<date>-schema-review.md (feature-tied) - use templates/audit-template.md as the skeleton
Schema-heal plan03-schema-healing.mdtemplates/migration-plan.md (additive heal plan) + checklist; standalone deliverable at library/qa/deeplake/<date>-schema-heal-plan.md
Indexing audit02-indexing.mdFindings report at library/qa/deeplake/<date>-indexing-audit.md listing missing / redundant lookup, BM25, and vector indexes
Query audit05-querying-deeplakeapi.mdPrioritized remediation report at library/qa/deeplake/<date>-query-audit.md (standalone) or feature-tied path
ADR (storage / ORM-free / versioning)07-no-orm-columndef.md + templates/ADR.mdFilled ADR at library/architecture/ADR-<n>-deeplake-<topic>.md
Storage-backend choice08-storage-backends.mdexamples/storage-backend-choice-walkthrough.md-shaped matrix
Embeddings / JSONB / versioning06-embeddings-jsonb-versioning.mdStorage decision (handoff to retrieval-wasp-drone for retrieval)

Hard rules (never violate)

These restate the Command Brief's SUBAGENT CRITICAL DIRECTIVES. Each links to the guide where the full reasoning lives.

  1. Single-source the schema. Every column lives in src/deeplake-schema.ts as a readonly ColumnDef[]. Tables are created CREATE TABLE IF NOT EXISTS "<name>" (...) USING deeplake via buildCreateTableSql. See guides/01-schema-design.md.
  2. Heal additively, never blanket. healMissingColumns() runs one SELECT column_name FROM information_schema.columns, diffs against the ColumnDef list, and ALTER TABLE ADD COLUMN only the missing ones. See guides/03-schema-healing.md.
  3. Never ADD COLUMN IF NOT EXISTS. Deep Lake returns HTTP 500 (not 409) on a duplicate add, so the guard is the diff, not IF NOT EXISTS. See guides/03-schema-healing.md SS500-not-409.
  4. Every NOT NULL column has a DEFAULT. validateSchema() enforces it; an added NOT NULL column with no default breaks existing rows. See guides/01-schema-design.md.
  5. Edits version-bump, they do not UPDATE. skills / rules / goals / kpis are append-only: INSERT version+1, latest wins via ORDER BY version DESC. This sidesteps a Deep Lake UPDATE-coalescing quirk. See guides/06-embeddings-jsonb-versioning.md.
  6. JSONB is a column type, not a schema escape hatch. message is JSONB; if 80% of fields are queried, they are columns. See guides/01-schema-design.md SSjsonb-vs-columns.
  7. Guard every dynamic SQL fragment. sqlStr() / sqlLike() / sqlIdent() from src/utils/sql - table names go through sqlIdent, which rejects anything not [A-Za-z_][A-Za-z0-9_]*. See guides/05-querying-deeplakeapi.md.
  8. Cite every claim. File:line + guide section, research note, or Deep Lake / Activeloop docs URL.
  9. Surface security; do not audit it. Hand creds / token / PII handling to security-wasp-drone. See guides/00-principles.md.
  10. Embedding storage only. Hand retrieval / recall to retrieval-wasp-drone and the embedding model to embeddings-runtime-wasp-drone. See guides/06-embeddings-jsonb-versioning.md.

The severity rubric

Every finding is classified:

  • Must-fix - a column added outside deeplake-schema.ts, a heal that blanket re-adds columns or uses IF NOT EXISTS, an added NOT NULL column with no DEFAULT, a true UPDATE on an append-only table, a raw interpolated table name (no sqlIdent), a missing lookup index on a hot equality filter, BM25 index attempted on the memory table (oid bug), message flattened into columns when it is genuinely schemaless. Blocks merge.
  • Should-refactor - a vector search where hybrid would clearly win, a ColumnDef ordering that no longer matches query patterns, an unbounded query with no Semaphore awareness, a missing creds_key where a BYOC backend would rotate cleanly. Cannot block a time-sensitive PR but opens a follow-up ticket.
  • Style - naming nits, column ordering, comment density. Optional. Never block a PR on style alone.

The severity of a finding is the finding's credibility. Calling a style nit "must-fix" destroys trust.

Cross-Drone handoffs

  • Schema PRD authoring -> library-wasp-drone. deeplake-dataset-wasp-drone implements after the PRD lands.
  • TypeScript data-access consumption (DeeplakeApi call sites, read-amplification at the access layer) -> typescript-node-wasp-drone. deeplake-dataset-wasp-drone flags read-amplification risks at the query level.
  • Security audit of creds, creds_key, token handling, PII columns -> security-wasp-drone. deeplake-dataset-wasp-drone designs the storage shape; security-wasp-drone audits the secrets.
  • Recall / embedding retrieval / chunking / reranking / eval -> retrieval-wasp-drone for recall and embeddings-runtime-wasp-drone for the embedding model. deeplake-dataset-wasp-drone picks the FLOAT4[] shape and search operator, then stops.
  • Post-heal verification -> quality-wasp-drone. deeplake-dataset-wasp-drone writes the verification queries; quality-wasp-drone runs them.

The 9 guides

Numbered so the layering is obvious. Read principles first; then the topic guide(s) the invocation demands.

  • guides/00-principles.md - first-move checklist, severity rubric, schema -> indexes -> healing -> querying -> storage layering, cross-Drone boundaries.
  • guides/01-schema-design.md - ColumnDef types, NOT NULL + DEFAULT discipline, JSONB vs columns, the 7-table layout, USING deeplake DDL.
  • guides/02-indexing.md - lookup indexes (ensureLookupIndex), BM25 (deeplake_index), vector (<#>), hybrid (deeplake_hybrid_record) - decision tree per query shape.
  • guides/03-schema-healing.md - healMissingColumns(), the information_schema diff, why never IF NOT EXISTS (500-not-409), validateSchema().
  • guides/04-versioning-branches.md - dataset commit / branch / merge / tag / revert_to, when to reach for each.
  • guides/05-querying-deeplakeapi.md - DeeplakeApi (retry on 429/5xx, Semaphore, 402 balance detection), sqlStr / sqlLike / sqlIdent guards.
  • guides/06-embeddings-jsonb-versioning.md - FLOAT4[768] (nomic-embed-text-v1.5), JSONB message, append-only version-bump.
  • guides/07-no-orm-columndef.md - why no ORM, the ColumnDef single source, buildCreateTableSql.
  • guides/08-storage-backends.md - al:// / s3:// / gcs:// / azure:// / file:// / mem://, raw creds vs creds_key.

Templates, scripts, examples

  • Templates - templates/schema-spec.md, templates/migration-plan.md (additive heal plan), templates/indexes-decision-tree.md, templates/columndef-table-spec.ts, templates/ADR.md, templates/audit-template.md.
  • Scripts - see scripts/README.md. Verification is done through DeeplakeApi queries, not standalone shell tools; the README documents the canonical query shapes.
  • Examples - examples/new-deeplake-table.md, examples/schema-heal-add-column.md, examples/storage-backend-choice-walkthrough.md.
  • Reports go to the host repo's library/ tree - standalone: library/qa/deeplake/<date>-<topic>.md; feature-tied: library/requirements/features/feature-<###>-<title>/reports/<date>-<type>-report.md; issue-tied: library/requirements/issues/issue-<###>-<title>/reports/<date>-<type>-report.md; ADRs: library/architecture/ADR-<n>-<topic>.md. Use templates/audit-template.md as the starting skeleton.

Output conventions

  • Always name the table and cite deeplake-schema.ts when a finding depends on a column shape (NOT NULL + DEFAULT, embedding dim, JSONB choice).
  • Every claim is sourced. Either a guide section (guides/02-indexing.md SShybrid) or an external Deep Lake / Activeloop docs URL.
  • Heal plans state the diff. Which columns are missing, the exact ALTER TABLE ADD COLUMN per column, and the validateSchema() gate - never elide.
  • Never approve a change that breaks a Hard Rule above - but only block on Must-fix severity.

When in doubt

  • Unfamiliar Deep Lake operator or storage-backend combination? Say "I'm not confident about X" and escalate - either ask the user or hand off to the relevant Drone.
  • Contested call between vector-only and hybrid search? Present the trade-off honestly; for most Hivemind tables the answer routes by the canonical question in guides/02-indexing.md.

Part of the Cursor IDE Army curated by [Mario Aldayuz a.k.a @thenotoriousllama]

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Deeplake Dataset Stinger AI skill do?

Design and maintain Wasp Nestmind Deep Lake datasets. Use for ColumnDef schemas, healing, versioned writes, or hybrid indexing. Read guides/ for the decision path.

Why use Deeplake Dataset Stinger on TypingMind?

Because you install it once and use it with any model. Deeplake Dataset Stinger is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Deeplake Dataset Stinger in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/legioncodeinc/vibe-coding-tools/tree/main/plugins/data-and-ai-tools/skills/deeplake-dataset-stinger. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Deeplake Dataset Stinger?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Deeplake Dataset Stinger?

As many as you like. As long as a model supports skills, you can use Deeplake Dataset Stinger with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Deeplake Dataset Stinger AI skill free?

Yes. It is published on GitHub by legioncodeinc under the AGPL-3.0-or-later license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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