From agentops
Distill context (research, recon, learnings) into evidence-anchored rules routed to automation shapes. Use when a finished artifact should become skills, gates, or beads.
How this skill is triggered — by the user, by Claude, or both
Slash command
/agentops:operationalizeThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
> **Loop position:** move 7 (capture + ratchet) of the [operating loop](../../docs/architecture/operating-loop.md) — routes promoted learnings to their weakest durable enforcement (skill, gate, or bead).
Loop position: move 7 (capture + ratchet) of the operating loop — routes promoted learnings to their weakest durable enforcement (skill, gate, or bead).
Rich context dies in the artifact that gathered it. A deep-research report, a codebase-recon sweep, or a painful learning is read once, agreed with, and never changes behavior again. This skill is the bridge: distill the artifact into a handful of evidence-anchored rules, then route each rule to the automation shape that will actually fire next time — skill, workflow, hook, gate, beads, or playbook.
Use when: "I gathered rich context — operationalize it." The input is a finished artifact; the output is rules with anchors and a handoff per rule.
Name the source artifacts in place — absolute or repo-relative paths plus a one-line provenance note each (who produced it, when, method). Confirm each source has citable anchors (section IDs, finding IDs, line ranges); if not, add anchor IDs to your notes about the source, never by editing the source.
Checkpoint: every source is a named path with a provenance line. No corpus dirs were created.
Extract candidate rules in the canonical form — "When X, do Y because Z" — where Z cites at least one anchor. Work source by source, then reconcile:
Checkpoint: every rule line carries ≥1 anchor; every conflict became a DISPUTED entry, not a blended rule.
Hand each rule to /automation-shape-routing and extend its decision with this target table:
| Route | Pick when the rule… | Emit target |
|---|---|---|
| skill | needs judgment at execution time | /skill-builder |
| workflow | is a deterministic multi-step sequence | /workflow-builder |
| hook | must fire mechanically on a runtime event | /cc-hooks |
| gate | should check outputs — start warn-only | a validation gate spec (warn-only first) |
| beads | is unsettled work or a DISPUTED investigation | /beads-workflow |
| playbook | guides a human/operator decision, not an agent | .agents/playbooks/ entry |
Checkpoint: every rule has exactly one route; every DISPUTED entry routed to beads.
Write the rule packet (Output Specification below), then create one handoff stub per routed rule: the rule text, its anchors, the chosen route, and the target skill invocation. The downstream builder owns the artifact; this skill owns the rule and its evidence trail.
For each rule, run the counter-example check: actively search the sources (and your own experience) for one case where following the rule would be wrong. A found counter-example narrows the rule's "When X" or demotes it to DISPUTED. Then request a /validate verdict on the packet before handing off — verify before any downstream builder consumes it.
Input: fixtures/research-excerpt.md — a fake deep-research excerpt on worker-lane retry behavior, anchors RX-1…RX-5.
Distilled packet:
Note what did NOT happen: rules 1–3 were not averaged into "rotate fairly quickly"; RX-3 (re-dispatch to a warm lane) was held back at Step 5 because its own source records a 9% duplicate-work counter-example.
Format: markdown rule packet — sources-in-place list, numbered rules in
"When X, do Y because Z" form with anchors, DISPUTED section, route table, and
the validate verdict reference.
Path: written to .agents/operationalize/YYYY-MM-DD-<slug>.md; handoff
stubs accompany it as a ## Handoffs section (one block per routed rule).
Exit signal: packet path + per-rule route summary reported to the caller.
npx claudepluginhub boshu2/agentops --plugin agentopsReconciles session learnings into .claude/rules/ entries on demand via /reconcile. Uses same engine as automatic session-end phase. Proposes rules for operator approval before writing.
Scans installed skills to extract cross-cutting principles and distills them into rules by appending, revising, or creating rule files. Useful for periodic rules maintenance after skill changes.
Scans installed skills to extract principles shared across 2+ skills and distills them into rules by appending, revising, or creating rule files.