Historic Sql Patterns logo

Historic Sql Patterns

OrganizationPopular
Kaelio
historic_sql_patterns

Identify recurring cross-table historic-SQL analytical intents from a bounded pattern shard and emit typed pattern evidence for deterministic wiki projection.

Overview

PublisherKaelio
Repositoryktx
Skill namehistoric_sql_patterns
Stars
1.6K
Forks
104
Bundled files
Instructions only
LicenseApache-2.0
Links
  • 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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Historic Sql Patterns 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.
  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/Kaelio/ktx.git /tmp/ktx
mkdir -p .claude/skills
cp -r /tmp/ktx/packages/cli/src/skills/historic_sql_patterns .claude/skills/historic_sql_patterns
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Historic Sql Patterns 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 Historic Sql Patterns 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 Historic Sql Patterns 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.

Historic SQL Patterns

Use this skill when the WorkUnit raw file is a patterns-input/part-0001.json style shard from the historic-sql adapter. Older staged bundles may still provide root patterns-input.json; when that is the WorkUnit raw file, read it the same way.

Required Workflow

  1. Read the WorkUnit notes first.
  2. Find the single pattern input file listed under the WorkUnit rawFiles section.
  3. Call read_raw_file for that exact raw file path.
  4. Identify recurring analytical intents that span at least two tables and have repeated usage signal.
  5. Emit one pattern evidence object per durable cross-table intent by calling emit_historic_sql_evidence.
  6. Stop after all pattern evidence has been emitted.

Every join column mentioned in pattern descriptions must be verified via entity_details for both sides of the join.

Identifier Verification Protocol

Before writing a wiki page or SL source on any topic:

  1. discover_data({query: "<topic>"}) - see what wikis, SL sources, and raw tables already exist. Prefer updating existing pages over creating new ones.

Before emitting any schema.table or schema.table.column into a wiki body, SL source, tables: frontmatter, sl_refs, or emit_unmapped_fallback:

  1. entity_details({connectionId, targets: [{display: "<identifier>"}]}) - confirm the identifier resolves; inspect native types, FK/PK, and sampleValues.
  2. For literal values from the source, such as status codes or plan tiers, check whether they appear in entity_details sampleValues for the relevant column. If sampleValues is short or the sample may have missed real values, run a sql_execution probe with the same warehouse connection id: sql_execution({connectionId, sql: "SELECT DISTINCT <col> FROM <ref> LIMIT 50"}).
  3. If the candidate identifier still does not resolve, do one of:
    • Use sql_execution({connectionId, sql: "SELECT 1 FROM <ref> LIMIT 0"}). If it errors, the identifier is fictional.
    • Wrap the identifier in [unverified - from <rawPath>] in the wiki body, citing the exact raw path that mentioned it.
    • When recording emit_unmapped_fallback with no_physical_table, include the failing probe error in clarification.
  4. Never copy <schema>.<table> placeholder strings from these instructions into output.

Evidence Shape

Each call to emit_historic_sql_evidence must use this shape:

json
{
  "kind": "pattern",
  "pattern": {
    "slug": "order-lifecycle-analysis",
    "title": "Order Lifecycle Analysis",
    "narrative": "Analysts compare order statuses with customer segments to understand lifecycle movement.",
    "definitionSql": "select o.status, count(*) from public.orders o join public.customers c on c.id = o.customer_id group by o.status",
    "tablesInvolved": ["public.orders", "public.customers"],
    "slRefs": ["orders", "customers"],
    "constituentTemplateIds": ["pg:1", "pg:2"]
  }
}

The pattern object must match patternOutputSchema; multiple calls together must form patternsArraySchema.

Pattern Selection Rules

  • Prefer patterns that involve two or more tables.
  • Prefer templates with executionsBucket at least 10-100 and distinctUsersBucket above solo usage.
  • Merge templates into one pattern only when the business intent is the same.
  • Use a stable kebab-case slug based on intent, not a template id.
  • Set definitionSql to the clearest representative SQL from a constituent template.
  • Set slRefs to source names when the source name is obvious from table names; omit uncertain refs rather than guessing.
  • Treat each pattern shard independently; do not read peer shard files from peerFileIndex.

Boundaries

  • Do not call wiki_write.
  • Do not call sl_write_source.
  • Do not call sl_edit_source.
  • Do not call context_candidate_write.
  • Do not create single-table pattern pages.
  • Do not copy credentials, tokens, user emails, or unredacted literals into evidence.

Frequently asked questions

What does the Historic Sql Patterns AI skill do?

Identify recurring cross-table historic-SQL analytical intents from a bounded pattern shard and emit typed pattern evidence for deterministic wiki projection.

Why use Historic Sql Patterns on TypingMind?

Because you install it once and use it with any model. Historic Sql Patterns 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 Historic Sql Patterns in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Kaelio/ktx/tree/main/packages/cli/src/skills/historic_sql_patterns. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Historic Sql Patterns?

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 Historic Sql Patterns?

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

Is the Historic Sql Patterns AI skill free?

Yes. It is published on GitHub by Kaelio under the Apache-2.0 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇