Spin up a new loop (domain) in a file-based knowledge base — bootstrap the substrate if it's missing, gather the loop's charter, scaffold domains/<loop>/README.md, then do ONE real test run and record it in the loop's Timeline and LOG.md. Use when the user says "set up a new loop", "create a domain", "start a new beat/workstream", or names a recurring job they want the agent to own.
npx skills add https://github.com/AI-Builder-Club/skills --skill new-loop
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.docs? a report? code changes shipped via /verify?).
.env; neverinline 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.
accrete via its Timeline.
existing loop, add it there (a backlog line + a domain: tag) instead of a near-duplicate.
the /verify skill (proof + PR), giving each parallel agent its own isolated stack via
crabbox-setup (sibling harness skills in this plugin).
Point the README's Backlog at them.
Creating interactive data visualisations using d3.js. This skill should be used when creating custom charts, graphs, network diagrams, geographic visualisations, or any complex SVG-based data visualisation that requires fine-grained control over visual elements, transitions, or interactions. Use this for bespoke visualisations beyond standard charting libraries, whether in React, Vue, Svelte, vanilla JavaScript, or any other environment.
Comprehensive Python library for astronomy and astrophysics. This skill should be used when working with astronomical data including celestial coordinates, physical units, FITS files, cosmological calculations, time systems, tables, world coordinate systems (WCS), and astronomical data analysis. Use when tasks involve coordinate transformations, unit conversions, FITS file manipulation, cosmological distance calculations, time scale conversions, or astronomical data processing.
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full ASM JSON, flattened CSV for easy import, and exportable Python code for data engineers. Common triggers include converting instrument files, standardizing lab data, preparing data for upload to LIMS/ELN systems, or generating parser code for production pipelines.
Quantum mechanics simulations and analysis using QuTiP (Quantum Toolbox in Python). Use when working with quantum systems including: (1) quantum states (kets, bras, density matrices), (2) quantum operators and gates, (3) time evolution and dynamics (Schrödinger, master equations, Monte Carlo), (4) open quantum systems with dissipation, (5) quantum measurements and entanglement, (6) visualization (Bloch sphere, Wigner functions), (7) steady states and correlation functions, or (8) advanced methods (Floquet theory, HEOM, stochastic solvers). Handles both closed and open quantum systems across various domains including quantum optics, quantum computing, and condensed matter physics.
Retrieve and display GitHub Copilot usage metrics for organizations and enterprises using the GitHub CLI and REST API.
Socratic mentoring for junior developers and AI newcomers. Guides through questions, never answers. Triggers: "help me understand", "explain this code", "I''m stuck", "Im stuck", "I''m confused", "Im confused", "I don''t understand", "I dont understand", "can you teach me", "teach me", "mentor me", "guide me", "what does this error mean", "why doesn''t this work", "why does not this work", "I''m a beginner", "Im a beginner", "I''m learning", "Im learning", "I''m new to this", "Im new to this", "walk me through", "how does this work", "what''s wrong with my code", "what''s wrong", "can you break this down", "ELI5", "step by step", "where do I start", "what am I missing", "newbie here", "junior dev", "first time using", "how do I", "what is", "is this right", "not sure", "need help", "struggling", "show me", "help me debug", "best practice", "too complex", "overwhelmed", "lost", "debug this", "/socratic", "/hint", "/concept", "/pseudocode". Progressive clue systems, teaching techniques, and success metrics.
Core Python library for astronomy and astrophysics workflows that need Astropy APIs, including units/quantities, coordinates, FITS I/O, tables, time systems, WCS, and cosmology. Use when implementing or debugging astronomical data analysis code with Astropy.
High-performance DataFrame library for Python ETL, analytics, and pandas migration. Use for expression-based data manipulation with lazy query optimization, parallel execution, streaming out-of-core processing, Arrow interoperability, and optional GPU execution.
Take ai-builder-club/new-loop from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.