From skills
Spin up a new loop (domain) in a file-based knowledge base with charter, scaffold README, and test run. Use for recurring workstreams like weekly SEO or support triage.
How this skill is triggered — by the user, by Claude, or both
Slash command
/skills:new-loopThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
A **loop** (a `domain`) is a recurring thread of work the agent owns: a charter, a cadence, and
A loop (a domain) is a recurring thread of work the agent owns: a charter, a cadence, and
the artifacts it produces. This skill creates one, proves it works with a single real run, and
leaves behind a domains/<loop>/README.md that is the loop's live state.
The user wants to stand up a new workstream/beat/job (e.g. "a weekly SEO loop", "a support
triage loop", "a competitor-watch loop"). Don't use this for a one-off task — that's a backlog
line in an existing domain, or a doc/signal.
Infer from the request; ask a short clarifying round only for what you can't:
domains/<name>/). Keep it short.manual / daily / weekly / a cron expr. Default manual.pr/ship-change?)..env; never
inline secrets).If the request is already specific, infer all five and just confirm in your summary.
Check the knowledge-base repo root for:
ARCHITECTURE.md and LOG.md, andCLAUDE.md that has a "Knowledge base" section.All present → the substrate exists; skip to Step 2.
Anything missing → read references/KNOWLEDGE_SETUP.md and follow it — it copies in
ARCHITECTURE.md + LOG.md, creates signals/ docs/ domains/ with their README schemas, and
injects the knowledge-base section into CLAUDE.md (or scaffolds one from
references/CLAUDE.template.md). It's idempotent: it only creates what's missing.
(Read references/ARCHITECTURE.md once if you haven't — it's the model this skill instantiates.)
Create domains/<name>/README.md from the domain template (in domains/README.md, also
quoted in references/KNOWLEDGE_SETUP.md), filled with the gathered inputs. Required sections:
frontmatter (kind: domain, domain, status: active, goal, cadence), a 2–4 line
description, ## Current focus, ## Backlog (to-dos inline — they stay in the README until they
earn a task kind), and an empty ## Timeline. Add ## Evidence & analysis / ## Metrics
placeholders if relevant.
Collision check: if domains/<name>/ already exists, stop and ask whether to update it instead
of overwriting.
The point of the skill: prove the loop actually runs, not just that the folder exists.
Actually run the loop once, at small scale — do whatever it's meant to do (triage a few real tickets, pull one real SERP, fetch the inbox, draft one comment, run one analysis query, scope one code change…). Use real tools/data where you can; if a credential is missing, do the furthest-reachable dry run and note the gap.
Producing an artifact is optional — a legit run may surface nothing worth filing. Only create
a signal/doc if the run genuinely produced one.
Two required outputs regardless:
## Timeline:
YYYY-MM-DD | test run — <what you did and found / "nothing actionable yet">.LOG.md (its grammar):
## YYYY-MM-DD · <loop-name> loop created + first run · #ops
What: <one line — what the loop is and what the first run did/found>.
Refs: domains/<name>/README.md (new)[, any artifact created].
Summarize: the loop's charter (the five inputs), what the test run did/found, any artifacts created (or "none — nothing actionable this run"), missing tools/credentials to wire up, and how to run it again (cadence + entry point). Keep it tight.
domain: tag) instead of a near-duplicate.pr skill / ship-change and gets each
agent its own isolated stack via crabbox-setup (sibling harness skills in this plugin).
Point the README's Backlog at them.npx claudepluginhub ai-builder-club/skills --plugin skillsDesigns, configures, and hardens autonomous agent loops with verification gates, persistent state, and stop conditions. Helps scope, build, and debug self-iterating agents.
Designs and orchestrates scheduled, multi-step agent loops (discover→triage→verify→land/escalate) with risk tiers, budget tracking, and a kill switch. Composes inner loops and native scheduling.
Find, compare, adapt, or design bounded AI-agent feedback loops with explicit checks, stop rules, guardrails, and handoffs. Use for recurring agent workflows, automation cadences, or iterative improvement processes.