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affaan-m/ECC

ito-data-atlas-agent

Design background Data Atlas style agents for Itô basket research, market discovery, parameter drafting, and human-in-the-loop editing. Use for architecture and workflow planning, not live order execution.

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See how to use itView GitHub source
npx skills add https://github.com/affaan-m/ECC --skill "skills/ito-data-atlas-agent"
Automated source guide

Source checked Jul 28, 2026·Refresh due Oct 26, 2026

Reorganized from the pinned upstream SKILL.md

Turn ito-data-atlas-agent's source instructions into a guide you can follow

According to the pinned SKILL.md from affaan-m/ECC: Use this skill to design an agent that watches data sources, builds candidate prediction-market baskets, drafts parameter changes, and hands the result to a human for review.

npx skills add https://github.com/affaan-m/ECC --skill "skills/ito-data-atlas-agent"
Check the pinned source

Best fit

  • Design background Data Atlas style agents for Itô basket research, market discovery, parameter drafting, and human-in-the-loop editing. Use for architecture and workflow planning, not live order execution.

Bring this context

  • A concrete task that matches the documented purpose of ito-data-atlas-agent.
  • The files, examples, or context the task depends on.
  • Your constraints, target environment, and definition of done.

Expected outputs

  • data sources
  • access gates
  • agent roles

Key source sections

Read ito-data-atlas-agent through these 5 source sections

Sections are extracted automatically from the pinned SKILL.md and link back to the source.

01

Workflow

1. Define the user objective and excluded actions. 2. List data sources and access requirements. 3. Draft a basket spec with provenance for every underlier. 4. Produce editable parameters rather than executable orders. 5. Store an audit trail: inputs, model output, sources, and…

SKILL.md · Workflow
Define the user objective and excluded actions.List data sources and access requirements.Draft a basket spec with provenance for every underlier.
02

Guardrails

Keep all execution behind explicit human approval.

SKILL.md · Guardrails
Keep all execution behind explicit human approval.Require ITOAPIKEY only for read-only Itô data access unless a separateDo not persist private user data unless the target repo already has a storage
03

Architecture Pattern

1. Research collector: public web, X, GitHub, venue docs, API metadata, and Itô read endpoints when gated access exists. 2. Basket drafter: turns sources into candidate underliers, weights, rules, and questions. 3. Risk reviewer: checks data freshness, venue limits, resolution a…

SKILL.md · Architecture Pattern
Research collector: public web, X, GitHub, venue docs, API metadata, andBasket drafter: turns sources into candidate underliers, weights, rules, andRisk reviewer: checks data freshness, venue limits, resolution ambiguity,
04

Useful Skill Chains

deep-research for source collection.

SKILL.md · Useful Skill Chains
deep-research for source collection.x-api for current social/event signal.ito-market-intelligence for venue and underlier context.

SkillSignal prompt templates

Provide the task, context, and acceptance criteria

These prompts were written by SkillSignal from the source structure; they are not upstream text.

Task-start prompt

Confirm source fit, inputs, and outputs before acting.

Use ito-data-atlas-agent to help me with: [specific task]. Context: [files, data, or background]. Constraints: [environment, scope, and prohibited actions]. Before acting, check the pinned SKILL.md and explain which sections apply, what inputs are still missing, and what you will deliver.

Source-guided execution

Make the Agent explicitly follow the key extracted sections.

Apply the pinned ito-data-atlas-agent source to [task]. Pay particular attention to these source sections: “Workflow”, “Guardrails”, “Architecture Pattern”, “Useful Skill Chains”, “Output Contract”. Preserve the important decision at each step. Mark facts not covered by the source as “needs confirmation” instead of inventing them. Then verify the result against my acceptance criteria: [criteria].

Result-review prompt

Check omissions, permissions, and source drift before delivery.

Review the current ito-data-atlas-agent result: (1) does it satisfy the original task; (2) were any applicable steps or limits in the pinned SKILL.md missed; (3) did it perform any unauthorized file, command, network, or data action; and (4) which conclusions remain unverified? List issues first, then fix only what the source or user authorization supports.

Output checklist

Verify each item before delivery

The task matches the purpose documented in the SKILL.md.

The source section “Workflow” has been checked.

The source section “Guardrails” has been checked.

The source section “Architecture Pattern” has been checked.

The source section “Useful Skill Chains” has been checked.

Inputs, constraints, and acceptance criteria are explicit.

Unverified facts, compatibility, and outcome claims are clearly marked.

Any file, command, network, or data action has been reviewed.

Choose a different workflow

When another Skill is the better fit

design-review

Use when the user says "review the design", "check the UI", or wants a comprehensive UI/UX review. Uses a 7-phase methodology covering interaction, responsiveness, accessibility, and more.

A separate implementation from event4u-app/agent-config; compare its source, maintenance signals, and permission requirements.

Open source detail

neuropixels-analysis

Analyze Neuropixels extracellular recordings end-to-end with SpikeInterface. Covers loading SpikeGLX/Open Ephys/NWB data, preprocessing, drift/motion correction, Kilosort4 (and CPU) spike sorting, quality metrics, and unit curation (threshold-based, model-based UnitRefine, and AI-assisted visual review). Use when working with Neuropixels 1.0/2.0 recordings, spike sorting, or extracellular electrophysiology analysis.

A separate implementation from K-Dense-AI/scientific-agent-skills; compare its source, maintenance signals, and permission requirements.

Open source detail

scientific-brainstorming

Facilitates evidence-aware scientific ideation with independent generation, structured discussion, explicit assumptions, transparent evaluation, adversarial review, and decision logs. Use for early-stage research brainstorming or prioritizing candidate directions; hand off empirical validation, study design, ethics or regulatory review, and clinical questions to appropriate experts or skills.

A separate implementation from K-Dense-AI/scientific-agent-skills; compare its source, maintenance signals, and permission requirements.

Open source detail

FAQ

What does ito-data-atlas-agent do?

Use this skill to design an agent that watches data sources, builds candidate prediction-market baskets, drafts parameter changes, and hands the result to a human for review.

How do I start using ito-data-atlas-agent?

The catalog detected this source-specific install command: npx skills add https://github.com/affaan-m/ECC --skill "skills/ito-data-atlas-agent". Inspect the command and pinned source before running it.

Which Agent platforms does it declare?

No dedicated Agent platform is declared in the pinned source record.

Repository stars
234,327
Repository forks
35,711
Quality
81/100
Source repository last pushed

Quality breakdown

Based on traceable docs and repository signals; stars are not treated as quality.

81/100
Documentation22/30
Specificity17/25
Maintenance20/20
Trust signals22/25

Compare before choosing

Related Agent Skills and source variants

These links are selected from shared tasks, functions, stacks, platforms, and same-name variants. Compare the source owner, documentation, permissions, and maintenance signals.

design-review by event4u-app

Use when the user says "review the design", "check the UI", or wants a comprehensive UI/UX review. Uses a 7-phase methodology covering interaction, responsiveness, accessibility, and more.

neuropixels-analysis by k-dense-ai

Analyze Neuropixels extracellular recordings end-to-end with SpikeInterface. Covers loading SpikeGLX/Open Ephys/NWB data, preprocessing, drift/motion correction, Kilosort4 (and CPU) spike sorting, quality metrics, and unit curation (threshold-based, model-based UnitRefine, and AI-assisted visual review). Use when working with Neuropixels 1.0/2.0 recordings, spike sorting, or extracellular electrophysiology analysis.

scientific-brainstorming by k-dense-ai

Facilitates evidence-aware scientific ideation with independent generation, structured discussion, explicit assumptions, transparent evaluation, adversarial review, and decision logs. Use for early-stage research brainstorming or prioritizing candidate directions; hand off empirical validation, study design, ethics or regulatory review, and clinical questions to appropriate experts or skills.

citation-management by k-dense-ai

Comprehensive citation management for academic research. Search Google Scholar and PubMed for papers, extract accurate metadata, validate citations, and generate properly formatted BibTeX entries. This skill should be used when you need to find papers, verify citation information, convert DOIs to BibTeX, or ensure reference accuracy in scientific writing.

doc-and-modernize by github

Two related workflows for a locally-cloned codebase, in one skill. Documentation mode produces a single, comprehensive, verifiable architecture document primarily by reading files on disk (local-first) — use it whenever the user wants to understand, map, document, research, or onboard onto a codebase ("research this repo", "write up the architecture", "do an architecture deep dive", "document how this codebase works", "map the system design", "create an onboarding doc"). Modernization mode gener

View original Skill.mdThis page is parsed directly from the repository SKILL.md without editorial rewriting. Collected: Jul 28, 2026 · about 2 min

Itô Data Atlas Agent

Use this skill to design an agent that watches data sources, builds candidate prediction-market baskets, drafts parameter changes, and hands the result to a human for review.

This skill describes architecture and workflow. It does not run live trading.

Guardrails

  • Keep all execution behind explicit human approval.
  • Require ITO_API_KEY only for read-only Itô data access unless a separate private implementation explicitly adds execution controls.
  • Do not persist private user data unless the target repo already has a storage contract and the user asks for it.
  • Do not expose private strategy logic, venue credentials, or local paths in public docs.

Architecture Pattern

Use four lanes:

  1. Research collector: public web, X, GitHub, venue docs, API metadata, and Itô read endpoints when gated access exists.
  2. Basket drafter: turns sources into candidate underliers, weights, rules, and questions.
  3. Risk reviewer: checks data freshness, venue limits, resolution ambiguity, compliance notes, and prompt-injection exposure.
  4. Human editor: opens a chat or UI state where the user can approve, reject, adjust, or ask for more research.

Workflow

  1. Define the user objective and excluded actions.
  2. List data sources and access requirements.
  3. Draft a basket spec with provenance for every underlier.
  4. Produce editable parameters rather than executable orders.
  5. Store an audit trail: inputs, model output, sources, and human decision.

Useful Skill Chains

  • deep-research for source collection.
  • x-api for current social/event signal.
  • ito-market-intelligence for venue and underlier context.
  • ito-basket-compare for user knowledge-base matching.
  • prediction-market-risk-review before any execution-capable integration.

Output Contract

Return an implementation-ready workflow spec with:

  • data sources
  • access gates
  • agent roles
  • human approval points
  • storage/audit boundary
  • non-goals
Source repo
affaan-m/ECC
Skill path
skills/ito-data-atlas-agent/SKILL.md
Commit SHA
4e973d3eaf92
Repository license
MIT
Data collected