Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/PicsArt/gen-ai-skillsnpx agentmods add skills/picsart/gen-ai-skills/enterprise-pinned-registryWrote 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/picsart/gen-ai-skills/enterprise-pinned-registry)<a href="https://agentmods.dev/skills/picsart/gen-ai-skills/enterprise-pinned-registry"><img src="https://agentmods.dev/badge/skills/picsart/gen-ai-skills/enterprise-pinned-registry/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/picsart/gen-ai-skills/enterprise-pinned-registry"><img src="https://agentmods.dev/badge/skills/picsart/gen-ai-skills/enterprise-pinned-registry.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 | $0.00015 | $0.02349 |
| Opus 5 | $0.00008 | $0.01175 |
| Sonnet 5 | $0.00003 | $0.00470 |
| Haiku 4.5 | $0.00002 | $0.00235 |
Grade B, and why
enterprise-pinned-registry scanned grade B with 1 finding 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 12d 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.
Asks the agent to reveal its instructionsmediumSystem prompt leakage
Directions to print, repeat or translate the system prompt extract configuration the operator did not intend to expose.
- **Fingerprint prompt templates.** Any string change to a template is a versioned bump, not a silent edit. How it starts
The opening of the file, as written. The whole thing — 190 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Enterprise Pinned Registry
A lockfile concept for AI generation. Model versions, prompts, brand tokens, and seeds are pinned to a manifest that regenerates the same campaign identically six months, a year, or three years later. Built for enterprises that need reproducibility for legal holds, brand-campaign continuity, regulatory defense, and year-over-year comparability.
When to Use
- Multi-team deployment: 20+ designers / PMs / agencies generating on the same brand, need identical output across all of them
- Year-over-year campaign (annual report, quarterly earnings graphics) must match the prior year's look exactly
- Regulated industry where "reproducible" is a compliance requirement, not a preference
- Legal hold — a specific asset may need to be regenerated identically for litigation support
- Model-provider change management — new versions ship weekly; you want explicit, reviewed upgrades, not drift
- Audit trail: every shipped asset must trace back to a specific model version + prompt + brand token set
Do not use for: one-off exploration, draft/ideation phases, consumer projects.
Prerequisites
Before authoring the registry:
- Scope — which models, prompts, and brand tokens are in scope? Start narrow (heroes only) then expand.
- Bump cadence — quarterly? On-demand only? Who approves a bump?
- Regression-test corpus — what's the set of prompts that must be re-run before a bump is approved? (Typically 10–20 representative prompts.)
- Storage — registry in the campaign repo, a dedicated
brand-registryrepo, or a central governance repo? - Rollback plan — if a bump produces unacceptable drift, how do we revert? (Git revert is usually enough if the registry is in git.)
- Audit retention — how long must the registry + generated outputs be retained? 7 years for SOX-adjacent, 10 for pharma, indefinite for trademark defense.
How to Run
Five steps. The registry is the contract; everything generates against it.
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.
- 12d ago First seen · 190 lines · 15 tokens per session scan B 5604ff92dfc6
enterprise-pinned-registry is a skill published in the GitHub repository PicsArt/gen-ai-skills (4 stars, last pushed 15d ago), licensed MIT. It adds 15 tokens to every session and 2,349 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it B with 1 finding (asks the agent to reveal its instructions). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
prompt-craft
Translate natural language image descriptions into detailed, structured DALL-E prompts with subject, style, composition, lighting, and mood specifications.
dbt-setup
Scaffolds a complete dbt-fabric project for a Fabric Data Warehouse: generates profiles.yml, dbtproject.yml, a column-rich sources.yml (with data types for every table column, ready for dbt contracts), requirements.txt, and .gitignore, then writes them to the local filesystem. Use when the user asks to "set up dbt"…
nosql-database-design
Designs a NoSQL data model by leading with access pattern analysis. Covers DynamoDB single-table design (PK/SK/GSI) and MongoDB embedding vs referencing, consistency models, and capacity planning. Invoked when the user asks to design a DynamoDB schema, MongoDB data model, or NoSQL data model.
json-to-toon
Transforms JSON input into Token-Oriented Object Notation (TOON) to reduce token consumption in LLM prompts and context windows. Applies the full TOON spec: inline primitive arrays, tabular format for uniform object arrays, and list format for heterogeneous or nested structures. Invoked when the user asks to compress…
relational-database-design
Designs or reviews a relational database schema for a given domain. Covers table structure, normalization, indexes, constraints, and migration strategy. Invoked when the user asks to design a schema, review a database structure, or optimize a data model.
rudder-profiles-debug
Diagnoses RudderStack Profiles compile failures, run failures, and output-quality problems. Use when pb compile fails, pb run fails, identity stitching looks wrong, output quality regresses, or Profiles errors need structured recovery.