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.
npx skills add manishiitg/coding-agent-loop --skill storefront-opportunity-to-verified-changegit clone --depth 1 https://github.com/manishiitg/coding-agent-loopWrote 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.
[](https://agentmods.dev/skills/manishiitg/coding-agent-loop/storefront-opportunity-to-verified-change)<a href="https://agentmods.dev/skills/manishiitg/coding-agent-loop/storefront-opportunity-to-verified-change"><img src="https://agentmods.dev/badge/skills/manishiitg/coding-agent-loop/storefront-opportunity-to-verified-change/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.
<a href="https://agentmods.dev/skills/manishiitg/coding-agent-loop/storefront-opportunity-to-verified-change"><img src="https://agentmods.dev/badge/skills/manishiitg/coding-agent-loop/storefront-opportunity-to-verified-change.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00031 | $0.00707 |
| Opus 5.5 | $0.00012 | $0.00283 |
| Sonnet 5.5 | $0.00006 | $0.00141 |
| Haiku 4.5 | $0.00003 | $0.00071 |
Grade A, and why
storefront-opportunity-to-verified-change 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 19d 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.
How it starts
The opening of the file, as written. The whole thing — 48 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Storefront Opportunity to Verified Change
Outcome
Turn one observed shopper problem into a precise product or variant correction, a merchant decision, a verified publish state, and a comparable result or an explicit inconclusive finding.
When to use
Use for a Shopify product-page or variant issue in one store and market. A public-page observation supports a hypothesis; measured conversion claims need an authorized report.
Discovery and user direction
Inspect existing Crews, storefront, catalog and inventory authority, analytics access, merchant goal, and publish owners. Propose reuse or creation and show the plan before Builder applies it. Installation copies guidance and pending SETUP.json; it activates nothing.
Required inputs
Bind store, market, currency, language, buyer task, URL, product and variant IDs, owner, and observation time. For a measured goal, agree on source, event definition, segment, numerator, denominator, baseline window, and follow-up window. Qualitative-only is valid.
Plan and AgentWorks tools
Reuse or propose Shopify Growth Analyst and Catalog & Merchandising Analyst. Builder may use create_crew with stable keys for missing Crews. Growth writes shopify-growth-opportunity/v1; run scripts/validate_handoff.py --opportunity-only <opportunity.json> before Catalog reads it. Catalog rechecks the exact ProductVariant, inventory authority, and market storefront, then writes catalog-change-review/v1 with current and proposed values. Validate both artifacts. Show the exact diff to the merchant, then use a separately authorized publish route only if approved. Growth checks what shipped before comparing a follow-up report.
Knowledge and persistence
Save store, market, product, variant, opportunity, action, approval, publish, retest, and report IDs. Preserve before and proposed values, source times, metric rules, owner decisions, and prior results. Re-read the variant before any repeat edit.
Validation and reporting
What ships with it
8 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.
- examples/catalog-change-review.json 1.1 KB
- examples/invalid-catalog-change-review.json 665 B
- examples/shopify-growth-opportunity.json 825 B
- playbook.json 4.5 KB
- references/team-and-handoffs.md 2.4 KB
- scripts/test_validate_handoff.py 4.7 KB runs code
- scripts/validate_handoff.py 5.3 KB runs code
- SETUP.json 3.0 KB
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.
- 19d ago First seen · 48 lines · 31 tokens per session scan A 1852f291cd33
storefront-opportunity-to-verified-change is a skill published in the GitHub repository manishiitg/coding-agent-loop (13 stars, last pushed 18d ago), licensed MIT. It adds 31 tokens to every session and 707 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-27.
Other skills, from other repositories
gpui-mockup
Explore visual directions for a herdr-gpui UI element as real GPUI code. Write 3–4 variants in one Rust file, show them side by side in a native mockup window in the user's own theme and fonts, capture the window to check your work, and iterate on the picks and notes the user sends back. Use instead of the HTML mockup…
anti-slop-frontend
A mechanical, countable anti-slop checklist for AI-generated frontend. Catches the specific signatures an undirected model defaults to: AI-purple glows, Inter-everywhere, em-dashes, div-based fake screenshots, eyebrow-on-every-section, beige+brass "premium" palettes, generic Jane Doe / Acme data. Advisory layer that…
accessibility-a11y
Semantic HTML, keyboard navigation, focus states, ARIA labels, skip links, and WCAG contrast requirements. Use when ensuring accessibility compliance, implementing keyboard navigation, or adding screen reader support.
anti-slop-image-direction
Opt-in, paid reference images for marketing or new-site work: plan one image per section, confirm count, backend and cost, generate with pi-nano-banana, then distill a written direction brief for the mockup loop. Images are direction, never source. Never for product UI. Triggers: "generate design references", "image…
cadcraft
Headless 2D CAD with CADCraft (DXF/DWG, AutoCAD-style commands, layers, text, dimensions, export to PDF/PNG/SVG, DXF DWG conversion), provisioned on demand by pi-craft. Use for "draw a rectangle and a circle and export a pdf", "convert this dxf to a pdf", "add a layer and a text label to this drawing", "turn this dwg…
craft-studio
Router for the ArtCraft craft apps (PhotoCraft, VectorCraft, FilmCraft, LightCraft, PdfCraft, EffectCraft, DesignCraft, FigCraft, WordCraft, SoundCraft, DeckCraft, CADCraft, GridCraft) driven headless via pi-craft. Use to choose which app fits a job and run the discover-execute-save-verify loop - "which craft app…