trace-to-skill-inducer

name	trace-to-skill-inducer
description	Use when you have captured session evidence — session-retro / session- observatory-live traces in .planning/patterns/, tool logs, correction records — and want to induce a reusable skill from it. Segments the traces into candidate skill units (an LLM judgment, not a deterministic parse) and decomposes each candidate into a four-part structured spec: workflow structure, execution semantics, and runtime attachments (verification, safety, rollback, state). It emits a spec object, NOT a finished SKILL.md, and hands that spec to skill-forge. It sits between skill-integration (upstream frequency detector) and skill-forge (downstream author). Backed by Agent-Trace-to-Skill Induction (arxiv 2606.06893v1). Triggers on inducing a skill from captured traces, turning a repeated pattern into a skill spec, and preparing evidence for skill-forge.
description-frequency	on-demand
user-invocable	true
version	1.0.0
format	"2025-10-02T00:00:00.000Z"
triggers	["induce a skill from these captured session traces","turn this repeated pattern in .planning/patterns/ into a skill spec","prepare a structured spec from trace evidence for skill-forge"]
updated	"2026-07-18T00:00:00.000Z"
status	ACTIVE
source	arxiv 2606.06893v1 (Agent-Trace-to-Skill Induction)

Trace-to-Skill Inducer

Turn captured interaction traces into a structured skill spec the skill-forge loop can author from. Segment the traces into candidate skill units, decompose each candidate into workflow structure + execution semantics + runtime attachments, scrub sensitive data, and hand the spec downstream. This is the induction step of the skill lifecycle on this system: it converts raw session evidence into a design contract, and stops there.

Why

skill-integration frequency-detects that a tool sequence recurs, but a raw recurrence count is not a skill — it has no declared preconditions, verification, rollback, or state model, so authoring straight from it produces under-specified skills that pass validate and then misbehave in skill-counterfactual-audit. The failure this prevents is scope collision: if induction emits a finished SKILL.md, it overlaps skill-forge and two authors fight over the same file. Draw the boundary so induction feeds authoring — spec out, not skill out.

Data classes touched

Session traces from .planning/patterns/ are project-internal. A trace can incidentally capture a credential value (a token echoed into a tool arg) or Fox Companies IP / a MEMORY.md "never surface" record (private origins, Center Camp trust rules). Boundary rule: the induced spec may reference such a value by name (e.g. RH_POSTGRES_URL, Fox-IP:<slug>) but must never embed the secret value itself. A spec carrying a live credential or a never-surface record is fail-closed: do not emit it — escalate to the security-hygiene gate.

How

1. Gather evidence. Read the trace set for the target pattern from .planning/patterns/ (session-retro / session-observatory-live JSONL). Only proceed on a pattern skill-integration already flagged, or one you can confirm recurs in ≥ 3 distinct sessions. Fewer than 3 → skip (§When to skip).
2. Segment into candidate skill units. A candidate is a goal-directed span with a stable entry precondition and a stable exit postcondition. This is an LLM judgment — do not treat tool-sequence equality as the segment boundary; two traces reaching the same goal via different tool order are one candidate (§Robustness rule).
3. Decompose each candidate into the four-part spec (this is the induction payload, not a SKILL.md):
   • Workflow structure — ordered steps, branch points, loop/iteration.
   • Execution semantics — tools invoked, arg schema, side effects, and which steps touch shared repo state (git, worktrees, refinery-merge queue).
   • Runtime attachments — the verification check that proves the step worked, the safety gate (ProcessContext/LoaderContext chokepoints, PreToolUse commit hook), the rollback action, and any state the skill must persist (Grove content-addressed store / MEMORY.md).
4. Scrub. Apply the §Data-classes boundary rule — replace any credential or never-surface value with a named reference before the spec leaves this skill.
5. Emit the spec, hand to skill-forge. Output the structured spec object and route it to skill-forge; do not scaffold or write SKILL.md frontmatter here.
6. Low-confidence segmentation → defer. If step 2 cannot draw a stable boundary (candidate spans overlap, or entry/exit conditions are unclear), emit no spec and hand the raw evidence to skill-forge's HITL / a human, rather than guessing a unit.

Robustness rule

Judge candidates by effect, not surface phrasing. Cluster traces by the goal they achieve and the pre/post-conditions they satisfy, not by identical tool calls or wording. A candidate that recurs only because the same literal command string appears is a weaker unit than one whose outcome recurs.

Confidence / failure model

Segmentation wraps an LLM judgment — it is semi-decidable, not a deterministic check, so it can over- or under-segment. This skill reduces the chance of authoring an under-specified skill; it does not guarantee a correct unit. Fail-closed default: on any uncertainty about a candidate that touches shared repo state, sensitive memory, or self-modification, escalate (to skill-forge HITL / mayor-coordinator) rather than silently emit a spec. The refinery-merge queue never auto-resolves conflicts; induction inherits that posture — never auto-emit past an unresolved boundary.

When to skip

• The pattern recurs in fewer than 3 sessions — collect more traces first.
• skill-integration has not surfaced it and you cannot confirm frequency — it may be a one-off, not a skill.
• A finished SKILL.md already exists for this behaviour — route to skill-causal-curation (keep/repair/retire) instead of re-inducing.
• The only available trace is a single session with no repetition — there is no reusable unit to induce.

Integration

• skill-integration (upstream) — its frequency detection is the trigger; this skill consumes what it flags.
• session-retro / session-observatory-live (evidence source) — write the .planning/patterns/ traces this skill segments.
• skill-forge (downstream) — receives the structured spec and does the authoring/validate/critique/ship; this skill never writes the SKILL.md.
• security-hygiene — the scrub + never-surface escalation runs under it.