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Place a new task against historical task anchors or rank a task set using Bradley-Terry pairwise comparisons before factory routing.
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Based on SOC occupation classification
Selects the best-matching Kamino agent template for a given task, fills its template variables from the provided task and context, verifies no placeholders remain, and writes the completed agent file into the per-task folder .kamino/tasks/<task_id>/. Use when the user provides a task and wants a Kamino agent chosen and instantiated, mentions selecting or creating an agent from .kamino/agents, references the kamino agent index, or asks to run template-replace on an agent template.
Executes an assembled pipeline of instantiated Kamino agents in sequence using subagents, verifying each agent's output before proceeding to the next. Reads the run order from a dispatch-queue's execution-graph.md, prints the sequence to the terminal, and runs each step. Use when the user has a dispatch-queue (from taskgraph) or instantiated agent files and wants them executed, or asks to run/execute the assembled agents and confirm each step worked.
Copies this Kamino factory into a new folder in virgin state by running copy-factory.sh — installing both the .claude/ control plane (afac plugin manifest, skills, judge and classifier agents) and the .kamino/ data plane (blueprints, deterministic scripts, eval schemas), with an empty ledger and no inherited run data. Use when the user wants to install, clone, bootstrap, or set up a fresh agent factory in another repository or directory, asks to copy the factory somewhere, or invokes /copy-factory.
Deterministic read-only search over completed task outcome ledger records to produce score-free Agent Factory candidates.
Validates the Kamino agent library by running template-variable-checks.sh over the .kamino/agents directory — confirming every agent's required_inputs match the {{variables}} used in its body (both directions) and that its hardcoded_properties declaration is present and accurate. Use when the user wants to check or validate the agents, verify template-variable consistency, lint the agent blueprints, or invokes /check.
Ingests a source of problems + solutions from disk (a crawl, a dataset, a folder of problem files) into a standard-shape evaluation corpus the factory can sweep. Because the factory engine stays 100% corpus-agnostic, this skill does NOT hardcode any source format — it interviews the user, AUTHORS a corpus-specific builder script on demand, snapshots the ground-truth answers, runs the builder, validates the result against the corpus integrity gates, and records provenance. The authored builder and its input data live with the ingestion record (git-tracked), never in the generic engine. Use when the user wants to turn a problem set / dataset / answer list into a corpus, add a new corpus, ingest LeetCode/Project-Euler-style problems, or "make a corpus out of this folder".
| name | rank-task-difficulty |
| description | Place a new task against historical task anchors or rank a task set using Bradley-Terry pairwise comparisons before factory routing. |
Use this skill after task-evaluate and before task-outcome-lookup. It produces pairwise difficulty placement evidence for factory routing.
For ranking mode:
<tasks_file>
.kamino/evals/tasks/sample-difficulty-tasks.json
</tasks_file>
<comparisons_file>
.kamino/evals/tasks/sample-difficulty-comparisons.json
</comparisons_file>
For similar-difficulty placement mode:
<task_evaluation_json>
path/to/task-evaluation.json
</task_evaluation_json>
<ranking_json>
path/to/ranking-output.json
</ranking_json>
<target_task_json>
path/to/target-task.json
</target_task_json>
<comparisons_file>
path/to/target-comparisons.json
</comparisons_file>
bradley-terry-pairwise-rankingpairwise-difficulty-judge.kamino/evals/scripts/bradley_terry_pairwise_ranking.pyRun the script only through uv run:
uv run .kamino/evals/scripts/bradley_terry_pairwise_ranking.py rank --tasks "<tasks-file>" --comparisons "<comparisons-file>" --format json
uv run .kamino/evals/scripts/bradley_terry_pairwise_ranking.py similar --ranking "<ranking-json>" --target-task "<target-task-json>" --comparisons "<comparisons-file>" --neighbors "3" --format json
Ranking mode returns:
Similar-difficulty mode returns:
estimated_insertion_rankestimated_difficulty_scorePreferred placement artifact path:
.kamino/evals/tasks/difficulty/<task_id>.json
status: needs_comparison, invoke pairwise-difficulty-judge for the requested pair only.status: complete.pairwise-difficulty-judge only for missing comparisons requested by the deterministic script.uv run.Fail clearly if:
harder_task, confidence, reasoning, or key_factors.