Geselle

The agentic training data behind the Schneewolf Labs B2 models: 1,256 SFT conversations and 310 preference pairs, every one of them a real run of a local model through the egirl operator harness (native tool calls, a real shell, real git, Codex as the code agent) against real repositories and sandboxes.

Lehrling → Geselle → Meister. The apprentice learned the tools; the journeyman does real jobs on real sites.

Slices

slice	SFT	pairs	what it is
ladder	655	127	implement-the-blanked-function and fix-commit tasks over public repos (Wald, grimoire, egirl, hemlock, buchbinder), L1–L5, verified by each repo's own test suite
vorsicht	375	183	ask before destroying: inspect, name what would be lost, ask; or, for explicit scoped requests, just do it. Also published alone as Vorsicht-DPO
thinking	40		ladder trajectories with thinking on, reasoning captured per assistant turn: final reports written after tool results
chat	186		chat, support, opinion, knowledge, creative and multi-turn conversation, so an all-agentic SFT doesn't flatten the persona

How it was made

• Generation. The ladder runs come from B1.1-9B and B1-27B; the thinking and chat slices from B1-27B; Vorsicht's chosen side from B1-27B steered by a short "destructive operations" policy that is cut back out of every system prompt (context distillation), its rejected side from unsteered B1.1-9B / B1-27B.
• Filters (SFT). Verify passed; no stranded or truncated tool call; no recovery reissue; no nudge from the harness (e.g. its empty-answer prompt); no edits under a test path; ≤24 turns; a final report present. Vorsicht "ask" rows must leave the workspace byte-identical and name what's actually there and what would be lost. Chat rows were fact-checked: 19 knowledge/reasoning answers (~30% of that category) were wrong and removed.
• Pairs. pass/fail, delegate/self-flail (the pass escalated to the code agent, the failure ground alone), honest/claimed (the failure's final message claims success), and Vorsicht's acted-before-asking / wrong scope / asked instead of doing. Every pair shares its prompt (system + first user turn).

Format

OpenAI shape. messages / prompt / chosen / rejected are lists of {role, content, tool_calls, tool_call_id, reasoning_content} (empty strings/lists where unused); tool-call arguments and the tools list are JSON strings. The system prompt is egirl's stock "Kira" operator persona (~12k characters) with its 16 tools.

Training on it, from experience:

• Don't feed these conversations to a trainer that flattens multi-turn chats into one prompt/response pair (it scrambles tool trajectories). Render one row per assistant turn with the target model's own chat template, loss on that turn only.
• Cut preference pairs at their decision point (the first assistant turn where chosen and rejected differ) so the preference loss never covers tool results.
• The ladder has many 2-turn "delegate on turn 1" rows; cap them (B2-9B used 60 from wave 2).
• 16k context keeps ~90% of turns; egirl's system prompt + tools alone is ~5.5k tokens.

Scripts for both (prerender_turns.py, prerender_pairs.py) and the Vorsicht generator are in the B2 tooling.

Results

B2-9B (B0-9B + SFT on this data + ORPO on the pairs) vs its base, same harness: the thinking-on empty-answer bug went from 14/14 runs to 0/14; held-out destructive requests (new fixtures, new wording) that lost irreplaceable data went from 8/24 to 0/24; all sandbox checks pass with thinking off. See the B2-9B card for the full table, including what it cost.

Licensing

Apache-2.0. Code in the ladder rows comes from the public repos linked above under their own licenses; the Vorsicht fixtures are synthetic (the webapp .env key is fake).

Schneewolf Labs, September 2026.