new-loop

new-loop is a skill for Claude Code from gabrielmoreira/agent-skills-mirror. It costs 94 tokens per session (1,282 once invoked), scanned A, original, MIT.

A setup guide for creating a recurring workstream in a file-based knowledge base. A loop, also called a domain, has a purpose, schedule, inputs, outputs, and a record of what happened over time.

In plain words
What is it for?
Use it to start recurring work such as weekly SEO monitoring, support triage, or competitor tracking. It creates the workstream's README and records its first run in a timeline and log.
Why use it?
It turns an ongoing responsibility into a documented structure instead of leaving its rules and history scattered across conversations or files. It also requires one real test run to confirm the workstream operates as intended.

Skill for Claude Code

Written for Claude Code: user-invocable in frontmatter. Also seen: mentions CLAUDE.md.

Good fit Use it to start recurring work such as weekly SEO monitoring, support triage, or competitor tracking. It creates the workstream's README and records its first run in a timeline and log.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/gabrielmoreira/agent-skills-mirror/new-loop
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add gabrielmoreira/agent-skills-mirror --skill new-loop
Clone the repo
git clone --depth 1 https://github.com/gabrielmoreira/agent-skills-mirror

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for new-loop

README.md
[![agentmods](https://agentmods.dev/badge/skills/gabrielmoreira/agent-skills-mirror/new-loop/github.svg)](https://agentmods.dev/skills/gabrielmoreira/agent-skills-mirror/new-loop)
Your own site
<a href="https://agentmods.dev/skills/gabrielmoreira/agent-skills-mirror/new-loop"><img src="https://agentmods.dev/badge/skills/gabrielmoreira/agent-skills-mirror/new-loop/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for new-loop

Your own site · 80×15
<a href="https://agentmods.dev/skills/gabrielmoreira/agent-skills-mirror/new-loop"><img src="https://agentmods.dev/badge/skills/gabrielmoreira/agent-skills-mirror/new-loop.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 94 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,282 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00094 $0.01282
Opus 5 $0.00047 $0.00641
Sonnet 5 $0.00019 $0.00256
Haiku 4.5 $0.00009 $0.00128

Measured 9d ago against content hash d4fee50dfc92, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

new-loop scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

mirrors/repos/AI-Builder-Club@skills/skills/new-loop/SKILL.md · 92 lines

How it starts

The opening of the file, as written. The whole thing — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.

new-loop — spin up a 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.

When to use

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.

Inputs to gather (ask only what's missing)

Infer from the request; ask a short clarifying round only for what you can't:

  1. name — kebab-case, the loop's home folder (domains/<name>/). Keep it short.
  2. goal — one line: the outcome this loop drives.
  3. cadencemanual / daily / weekly / a cron expr. Default manual.
  4. what it does — what it consumes (signals? data? an inbox? a URL?) and produces (signals? docs? a report? code changes shipped via /verify?).
  5. tools/data — sources or credentials it needs (point at a setup skill or .env; never inline secrets).

If the request is already specific, infer all five and just confirm in your summary.

Procedure

1. Bootstrap the substrate (one-time; skip if already set up)

Check the knowledge-base repo root for:

  • ARCHITECTURE.md and LOG.md, and
  • a CLAUDE.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.)

2. Scaffold the loop README

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.

Read the full file on GitHub · 92 lines

Files

What ships with it

4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 9d ago First seen · 92 lines · 94 tokens per session scan A d4fee50dfc92

Subscribe to this mod's changes

new-loop is a skill published in the GitHub repository gabrielmoreira/agent-skills-mirror (17 stars, last pushed yesterday), licensed MIT. It adds 94 tokens to every session and 1,282 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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