Interact with Kleros Curate registries — the decentralized token-curated list protocol for token lists, address tags, and policy-driven curation. Use this skill when the user mentions Curate, Light Curate, LightGeneralizedTCR, LGTCR, LightCurate, Stake Curate, PermanentGTCR, PGTCR, Scout, token-curated registry, TCR, curated list, decentralized registry, registry, token list, address tags, CDN tags, Goldsky, or Solidity functions addItem, removeItem, challengeItem, challengeRequest. Covers all three flavors — Light Curate (LGTCR, optimistic challenge window), Stake Curate (PGTCR, permanent ERC20 stake), and Scout (Gnosis registries for contract/token tagging). Also trigger when the user wants to submit an item to a registry, challenge a submission, remove an item, appeal a dispute on a registry item, deploy a new Curate registry, curate a list, browse registry entries, check whether an address is tagged, add a token to a token list, query a curated list, or fund an appeal round. Even if Curate is not mentioned, trigger when the user describes registry operations — adding entries to a list, checking item status, browsing items, querying a decentralized list — combined with Kleros context signals (Kleros, arbitrator, dispute, juror, PNK). Do NOT trigger for non-Kleros registries or generic IPFS uploads without Curate context — IPFS uploads belong to kleros-ipfs-upload. Exception: if the user explicitly names the kleros-curate skill or asks to test this skill, trigger regardless of context.
npx skills add https://github.com/internet-court/internet-court-skill --skill kleros-curate
Kleros Curate is a decentralized verification system for exclusive, policy-driven registries. Registries are
governed by a policy document stored on IPFS — every submission is judged against that policy, and jurors
consult it if a dispute arises.
The deposit/challenge/arbitration cycle:
registry policy.
submissions during this window.
full — compliant submissions carry no permanent inclusion cost.
Kleros dispute. Impartial Kleros jurors read the policy and render a verdict.
discourages frivolous challenges in both directions.
Why use Curate:
Stake Curate especially, successful challengers can recover their challenge deposit and win the item's stake.
standard, define their own policy, deposits, challenge windows, arbitrator/court, and governance. The result
is a fast, low-overhead registry that can power a public Curate view or a custom frontend.
Why onchain-first matters:
Deposit amounts, arbitration costs, MetaEvidence URIs, and challenge windows are all live onchain state —
they change when registry governors update parameters. Any cached or estimated value is a liability: an agent
that submits the wrong deposit amount will have its transaction revert. Always read live values before acting.
Three contract flavors:
LightGeneralizedTCR: optimistic challenge window, ETH deposits, the mostwidely deployed flavor. Used by the majority of Curate registries across Ethereum and Gnosis.
PermanentGTCR: permanent ERC20 stake (not returned on item removal),Goldsky subgraph as primary data source, different status model (Submitted / Reincluded / Disputed / Absent
+ withdrawal flow). Identified by PGTCR-specific hallmark read calls (see references/stake-curate.md).
token lists, address tags, CDN mappings). Scout IS an overlay on LGTCR — it is not a separate contract
type. Working with Scout always requires both references/scout-registries.md (Scout-specific context)
and references/light-curate.md (LGTCR contract operations).
These rules apply across all Curate flavors. They are always in context; reference files may not repeat them.
item.json.columns must be copied verbatim from MetaEvidence; only values is dynamic.type: "link", not url; validate every metadata.columns[].type before upload.metadata.logoURI.explicitly accepts the review and compatibility risk.
eth_getCode before declaring any address is or isn't a contract.Step 1 — Keyword scan (zero cost)
→ Scout (overlay on Light Curate — LGTCR contracts on Gnosis)
→ Read references/scout-registries.md AND references/light-curate.md (both required; Scout adds context on top of LGTCR operations)
→ Stake Curate (PGTCR)
→ Read references/stake-curate.md
→ Light Curate (LGTCR) (also the default)
→ Read references/light-curate.md
Step 2 — Ambiguous ("Curate" with no flavor hint)
references/scout-registries.md AND references/light-curate.md)references/stake-curate.md for hallmark calls)Step 3 — Contract-type verification (if address provided)
See the flavor reference file for hallmark calls — SKILL.md does not embed function signatures here (contract-type detection belongs in each flavor's reference file).
Submit item to a registry → references/light-curate.md (LGTCR) or references/stake-curate.md (PGTCR)
Challenge / remove an item → flavor reference file
Submit evidence → flavor reference file
Fund an appeal → flavor reference file
Deploy a new registry (factory) → flavor reference file (factory section)
Fetch MetaEvidence (policy + schema) → references/shared-metaevidence.md
Compute deposits → references/shared-deposits.md
Build item.json → references/shared-item-json.md
Verify / make a deployed list visible on the Curate frontend -> references/verify-your-list.md
Upload to IPFS → references/shared-ipfs-upload.md
ABI / function signatures → references/shared-abi-fragments.md
grep: grep -n "function\|event" references/shared-abi-fragments.md
Scout registry addresses + seed templates → references/scout-registries.md
grep: grep -n "0x\|ATQ\|Address Tags\|Tokens\|CDN" references/scout-registries.md
Submit an item (any flavor):
references/shared-metaevidence.md — fetch schema (columns) and policy URIreferences/shared-item-json.md — build the item.json payloadreferences/shared-ipfs-upload.md — upload item.json to IPFS, get CIDreferences/shared-deposits.md — compute exact msg.valuelight-curate.md or stake-curate.md) — send the addItem transactionChallenge or remove an item:
references/shared-metaevidence.md — fetch the applicable policy (clearing policy for removal; registration policy for challenge)references/shared-ipfs-upload.md — upload evidence JSON to IPFSreferences/shared-deposits.md — compute the challenge depositDeploy a new registry:
references/shared-metaevidence.md - prepare valid MetaEvidence JSON (policy URI + column schema + logoURI)references/shared-ipfs-upload.md — upload MetaEvidence JSON to IPFSreferences/verify-your-list.md - submit the new registry to the network's list-of-lists if frontend visibility is requiredFrontend visibility after deployment:
registry for users.
when the list is intentionally stealth/private.
GeneralizedTCR, not Light Curate. Usereferences/verify-your-list.md.
These 9 files are loaded on demand — only when needed for the current task. The action index above points
to the right file for each operation. Loading an unnecessary reference file wastes context.
references/light-curate.md
Light Curate (LightGeneralizedTCR) operations end-to-end: minimum inputs to ask the user, registry
discovery via eth_getCode and hallmark reads, MetaEvidence retrieval, item.json construction, submit
item, challenge / remove item, submit evidence, fund an appeal, deploy a new registry via factory.
Read this file for any LGTCR contract interaction — and always alongside references/scout-registries.md
when working on Scout.
references/stake-curate.md
Stake Curate (PermanentGTCR) operations end-to-end: PGTCR hallmark detection (distinguishes PGTCR from
LGTCR), ERC20 approval + stake deposit flow, Goldsky subgraph as primary MetaEvidence source with onchain
fallback, PGTCR status model (Submitted / Reincluded / Disputed / Absent), item withdrawal flow, deploy
factory. Read this file for any PGTCR contract interaction.
references/scout-registries.md
Scout-specific overlay for the 4 known Gnosis registries (Tokens / Address Tags / Contract Domain Names /
CDN). Contains: the 4 registry contract addresses used for address-based routing, seed-first submission
pattern (fill from existing seed template, then submit), item.json templates per registry, image guidance,
incentives information. Always read alongside references/light-curate.md — Scout IS LGTCR at the
contract layer; this file adds Scout-only context on top.
references/verify-your-list.md
Narrow workflow for making a deployed registry visible and verified in the Curate frontend. Contains the
known list-of-lists addresses, explains why verification matters, and documents the simple Classic Curate /
GeneralizedTCR.addItem(bytes) path. Read this file for frontend visibility submissions; do not use Light
Curate addItem(string) mechanics for the known list-of-lists.
references/shared-metaevidence.md
Shared MetaEvidence retrieval applicable to all Curate flavors: eth_getLogs method with the correct
topic0, latest applicable MetaEvidence selection, LGTCR registration-vs-clearing classification, Goldsky
subgraph path for PGTCR, MetaEvidence JSON structure, policy URI extraction, and MetaEvidence authoring
guardrails.
Read this file before fetching MetaEvidence for any registry, regardless of flavor.
references/shared-deposits.md
Shared deposit computation covering all Curate flavors: submission deposit formula for LGTCR, challenge
deposit formula for LGTCR, PGTCR stake vs arbitration deposit distinction (ERC20 stake is separate from
the native-token arbitration cost), arbitrationCost() read pattern, msg.value assembly rule.
Read this file before computing any deposit or constructing any transaction value.
references/shared-item-json.md
Strict item.json construction rules: the columns + values schema format, verbatim-copy rule for columns
(copy from MetaEvidence without modification - values is the only dynamic part), GTCR field type allowlist,
forbidden aliases, placeholder rejection, pre-upload validation, and NewItem event sampling to verify
field order before submitting. Read this file before building any item payload for any Curate registry.
references/shared-abi-fragments.md
Shared ABI fragments for all Curate contracts: LightGeneralizedTCR read and write function signatures,
PermanentGTCR read and write function signatures, IArbitrator interface ABI, key event signatures
(MetaEvidence, ItemStatusChange, RequestSubmitted, etc.). Use grep -n "function\|event" to
navigate this file. Read when you need function selectors, calldata encoding, or event topic hashes.
references/shared-ipfs-upload.md
Shared IPFS upload guidance for Curate workflows: durability rationale (external pins can disappear after
onchain anchoring), required recommended path via the kleros-ipfs-upload skill and Kleros x402 endpoint,
/ipfs/<CID> format rule (avoid double-slash when building URLs), and explicit risk warning for any
user-approved external pinning source.
Read before any IPFS upload step inside a Curate workflow.
Something broken or confusing in this skill? Report it: fetch feedback/SKILL.md — helps maintainers fix what agents silently trip over.
Automate creating a GitHub repository and deploying a web project to Vercel. Use when the user asks to deploy a website/app to production, publish a project, or set up GitHub + Vercel deployment.
Deploy applications and websites to Vercel. Use this skill when the user requests deployment actions such as "Deploy my app", "Deploy this to production", "Create a preview deployment", "Deploy and give me the link", or "Push this live". No authentication required - returns preview URL and claimable deployment link.
Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Take internet-court/kleros-curate 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.