| name | ticket-dataset-knowledge-bootstrap |
| description | Build a real ticket knowledge base from a large ticket export or record list by deriving a source profile, promoted taxonomies, representative examples, and reusable projections instead of stopping at counts or flat summaries. |
| metadata | {"short-description":"Build a reusable ticket knowledge base from a ticket dataset"} |
| cluster | knowledge_bootstrap |
Ticket Dataset Knowledge Bootstrap
CTOX Runtime Contract
- Task spawning is allowed only for real bounded work steps that add mission progress, external waiting, recovery, or explicit decomposition. Do not spawn work merely because review feedback exists.
- The Review Gate is a quality checkpoint, not a control loop. After review feedback, continue the same main work item whenever possible and incorporate the feedback there.
- Do not create review-driven internal work cascades. If more work is needed, reuse or requeue the existing parent work item; create a new task only when it is a distinct bounded work step with a stable parent pointer.
- Every durable follow-up, queue item, plan emission, or internal work item must have a clear parent/anchor: message key, work id, thread key, ticket/case id, or plan step. Missing ancestry is a harness bug, not acceptable ambiguity.
- Rewording-only feedback means revise wording on the same artifact. Substantive feedback means add new evidence or implementation progress. Stale feedback means refresh or consolidate current runtime state before drafting again.
- Before adding follow-up work, check for existing matching internal work, queue, plan, or ticket state and consolidate rather than duplicating.
Use this skill when CTOX has a large ticket export, workbook, CSV, JSON list, or API record dump and needs to turn it into a reusable knowledge base.
This work is only durable when the resulting source profile, taxonomies, glossary, and projections are imported or reflected into CTOX ticket and discovery knowledge. Generated files alone do not count as completed knowledge.
This is not normal ticket handling.
This is a dataset-to-knowledge bootstrap skill.
The target is:
- a source profile
- promoted taxonomy dimensions
- taxonomy buckets with representative examples
- a glossary
- downstream projection hints for ticket onboarding and ticket knowledge
Read these first:
Output Contract
The run is only acceptable if it produces:
source_profile.json
taxonomies.json
glossary.json
semantic_clusters.json
knowledgebase.md
The output must contain:
- at least one promoted taxonomy dimension
- buckets under that dimension
- canonical and edge examples for those buckets
- projection guidance for later ticket onboarding
Runtime Approach
Use embeddings for semantic pattern discovery.
Use a small LLM for taxonomy and cluster naming.
The intended exemplar path is:
- local embedding model on the host for row semantics
gpt-5.4-nano for cluster naming, bucket descriptions, glossary cleanup, and projection notes
If the host or batch runner cannot sustain long mixed embedding-plus-LLM jobs, run the same pipeline in staged mode:
- keep embeddings on-host
- use
--semantic-naming-mode deterministic and/or --glossary-mode deterministic for the first durable pass
- then refine selected buckets later with small separate
gpt-5.4-nano micro-jobs
Canonical Run Shape
- inspect the workbook or dataset shape
- build the source profile
- promote strong categorical taxonomies from structured columns
- build semantic issue-pattern clusters from embeddings
- use the small LLM to name and describe those semantic buckets
- extract glossary candidates and clean them
- write the final knowledge artifacts
Commands
Use CTOX ticket commands as the execution boundary:
ctox ticket history-export --system <system> --output <path>
ctox ticket knowledge-bootstrap --system <system>
ctox ticket source-skill-import-bundle --system <system> --bundle-dir <dir>
Inspect and query the resulting knowledge:
ctox ticket knowledge-list --system <system>
ctox ticket source-skill-query --system <system> --query "<ticket text>" --top-k 8
Do not execute embedded Python helpers from skills/system. If a required dataset-to-bundle transform is not yet exposed by ctox ticket, implement or request that CLI/API path first.
Guardrails
- Do not claim “knowledge base” if the run only produced counts.
- Do not stop at deterministic value groupings.
- Do not promote every column to a taxonomy.
- Do not let semantic clusters exist without human-readable names and examples.
- Do not write vague prose instead of durable artifacts.