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Ingest Triage

OrganizationPopular
Kaelio
ingest_triage

Classify and resolve conflicts detected during bundle ingest (structural duplicates, definitional contradictions, near-duplicate clusters, re-ingest changes, evictions).

Overview

PublisherKaelio
Repositoryktx
Skill nameingest_triage
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 Ingest Triage 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/ingest_triage .claude/skills/ingest_triage
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ingest Triage 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 Ingest Triage 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 Ingest Triage 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.

Ingest Triage - conflict classification and resolution

This skill is loaded in two contexts:

  • By a Stage 3 WorkUnit agent when sl_discover, deterministic projection output, existing project memory, or prior provenance overlaps with what the current WorkUnit is about to write.
  • By the Stage 4 reconciliation agent for cross-WorkUnit sweeps, accepted patch overlap, and eviction decisions.

Apply the rules below before every write that could collide with an existing artifact.

Decision tree

  1. Is this the same artifact I'm producing now, or a different one with the same name? Read both. If names match and content matches (modulo whitespace): no conflict - skip the write, the prior one stands.

  2. If content differs, is it an expression-only change (e.g. a different sql: body for the same measure name, same grain, same columns)? Re-ingest change (expression-only): silently replace via sl_edit_source. No flag.

  3. If the difference is structural - grain, columns, filter, join shape - is the current bundle the re-ingest of a previously-ingested bundle (i.e. priorProvenance has a row for this raw file and artifact)? Re-ingest change (semantic break): replace + flag. Record in the IngestReport's conflicts_resolved list with flagged_for_human: true.

  4. If reconciliation sees accepted patches from this same job with no prior-sync row, check for same-ingest contradictions:

    KindDetectionResolution
    Structural duplicateSame name, near-identical expressionElect canonical by: (a) highest inbound-ref count from other sources; tiebreak: (b) lexicographically first unit key; (c) lexicographically first source name. Subsume losers into <canonical>-variants.md wiki page. Do NOT flag unless ambiguous.
    Near-duplicate clusterDifferent names, overlapping shape (same table, similar formulas)Same as structural; one canonical, others subsumed. Flag only if no canonical emerges.
    Definitional contradictionSame name, substantively different formulas (different aggregation, different filters, different columns)Rename + capture: disambiguate ALL variants with suffix derived from the domain (churn_risk_engagement_based, churn_risk_billing_based) and write a unified wiki page listing every variant with provenance. The contested name does NOT land in the SL. Always flag.
  5. Eviction (Stage 4 only): for each entry in eviction_list():

    • Remove the artifact (sl_write_source or sl_edit_source with delete: true for SL sources, wiki_remove for wiki pages).
    • Record the removal with emit_eviction_decision and action: "removed".

Why same-ingest vs re-ingest differs

Within ONE bundle there's no user signal telling us which duplicate wins - we capture all variants and flag. Across bundles, re-uploading IS the signal that the new state is intended - we replace silently for expression changes and flag for semantic breaks.

Naming disambiguation hints

When you rename to disambiguate, prefer domain suffixes that match the containing view/table/collection name: customers.churn_risk_scorecustomers.churn_risk_engagement_based (if the customer_churn view computes it from engagement); billing.churn_risk_scorebilling.churn_risk_billing_based. Avoid numeric suffixes (churn_risk_1, churn_risk_2) - they disclose nothing.

Applying canonical pins

When the Stage 4 system prompt includes a <canonical_pins> block, treat each pin as a prior user decision for that contestedKey.

  • If the pinned canonicalArtifactKey is present in the Stage Index or already exists in SL, keep it as the canonical artifact for that contested key.
  • Disambiguate competing artifacts instead of using the contested name for them.
  • Do not flag the pinned contested key solely because the variants disagree; the user has already chosen the canonical artifact.
  • If the pinned artifact cannot be found and no current WU can recreate it, emit emit_conflict_resolution with flaggedForHuman: true and explain that the pin references a missing canonical artifact.

When a pin applies cleanly, call emit_conflict_resolution with kind: "definitional_contradiction", artifactKey set to the pinned canonicalArtifactKey, detail describing the pinned election, and flaggedForHuman: false.

What to write in the unified wiki page

When you perform rename + capture, also write one page named <canonical-concept>-definitions.md under the wiki GLOBAL scope. Structure:

  • One heading per variant, referencing the disambiguated SL name.
  • One paragraph per variant: what it computes, where it came from (raw file + line range), when to use it.
  • A closing "Choosing between these" paragraph if the variants are legitimately domain-specific.

Do not attempt to rank variants or pick a "best" - that's user-override territory.

Silence rules

Flag for human review when:

  • You did rename + capture for a definitional contradiction (kind 3 above).
  • You performed an eviction retention (kind 5, second row).
  • An override constraint (from a Stage 4 re-run) conflicts with current inbound refs.

Do NOT flag:

  • Same-content duplicate skip (trivial).
  • Structural duplicate with clear canonical election.
  • Expression-only re-ingest replace.

Frequently asked questions

What does the Ingest Triage AI skill do?

Classify and resolve conflicts detected during bundle ingest (structural duplicates, definitional contradictions, near-duplicate clusters, re-ingest changes, evictions).

Why use Ingest Triage on TypingMind?

Because you install it once and use it with any model. Ingest Triage 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 Ingest Triage in TypingMind?

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

Which AI models can use Ingest Triage?

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 Ingest Triage?

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

Is the Ingest Triage 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.

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