affaan-m/ECC
Research prediction-market events, venues, underliers, liquidity, and news context for Itô basket workflows. Use for read-only market intelligence, API-gated Itô exploration, and source-grounded prediction-market briefings without investment advice or live trading.
npx skills add https://github.com/affaan-m/ECC --skill "skills/ito-market-intelligence"Source checked Jul 28, 2026·Refresh due Oct 26, 2026
Reorganized from the pinned upstream SKILL.md
According to the pinned SKILL.md from affaan-m/ECC: Use this skill when a user wants prediction-market context, event discovery, venue comparison, basket theme exploration, or an Itô API-backed market brief.
npx skills add https://github.com/affaan-m/ECC --skill "skills/ito-market-intelligence"Best fit
Bring this context
Expected outputs
Key source sections
Sections are extracted automatically from the pinned SKILL.md and link back to the source.
1. Clarify the market theme, venue, geography, and time horizon. 2. Gather public market data from venue docs/APIs or source-grounded research. 3. If ITOAPIKEY is present and the user explicitly asks for Itô data, call only read endpoints and state that access is gated. 4. Norma…
Do not provide investment, legal, tax, or trading advice.
Use deep-research or exa-search for source discovery.
Default to a compact brief with source links and a clear caveat:
SkillSignal prompt templates
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-market-intelligence 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-market-intelligence source to [task]. Pay particular attention to these source sections: “Workflow”, “Guardrails”, “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-market-intelligence 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
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 “Useful Skill Chains” has been checked.
The source section “Output Contract” 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
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 detailDistributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.
A separate implementation from K-Dense-AI/scientific-agent-skills; compare its source, maintenance signals, and permission requirements.
Open source detailUse NeuroKit2 to build or audit reproducible research workflows for physiological time-series preprocessing, event/interval analysis, multimodal alignment, variability, and complexity. Trigger when code imports neurokit2 or needs its current APIs, schemas, and method-aware validation—not for diagnosis or device validation.
A separate implementation from K-Dense-AI/scientific-agent-skills; compare its source, maintenance signals, and permission requirements.
Open source detailFAQ
Use this skill when a user wants prediction-market context, event discovery, venue comparison, basket theme exploration, or an Itô API-backed market brief.
The catalog detected this source-specific install command: npx skills add https://github.com/affaan-m/ECC --skill "skills/ito-market-intelligence". Inspect the command and pinned source before running it.
No dedicated Agent platform is declared in the pinned source record.
Quality breakdown
Based on traceable docs and repository signals; stars are not treated as quality.
Compare before choosing
These links are selected from shared tasks, functions, stacks, platforms, and same-name variants. Compare the source owner, documentation, permissions, and maintenance signals.
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.
Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.
Use NeuroKit2 to build or audit reproducible research workflows for physiological time-series preprocessing, event/interval analysis, multimodal alignment, variability, and complexity. Trigger when code imports neurokit2 or needs its current APIs, schemas, and method-aware validation—not for diagnosis or device validation.
Analyze raw prompts, identify intent and gaps, match ECC components (skills/commands/agents/hooks), and output a ready-to-paste optimized prompt. Advisory role only — never executes the task itself. TRIGGER when: user says "optimize prompt", "improve my prompt", "how to write a prompt for", "help me prompt", "rewrite this prompt", or explicitly asks to enhance prompt quality. Also triggers on Chinese equivalents: "优化prompt", "改进prompt", "怎么写prompt", "帮我优化这个指令". DO NOT TRIGGER when: user wants th
Standard single-cell RNA-seq analysis pipeline. Use for QC, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression, visualization, and converting R-friendly single-cell formats such as Seurat or SingleCellExperiment RDS files into h5ad for Scanpy. Best for exploratory scRNA-seq analysis with established workflows. For deep learning models use scvi-tools; for data format questions use anndata.
Use this skill when a user wants prediction-market context, event discovery, venue comparison, basket theme exploration, or an Itô API-backed market brief.
This is a public teaser skill. It can work with public sources by default. Any
Itô-backed data call requires explicit API access through ITO_API_KEY.
ITO_API_KEY is present and the user explicitly asks for Itô data, call
only read endpoints and state that access is gated.deep-research or exa-search for source discovery.x-api for public social signal discovery when X access is configured.market-research for market sizing, competitors, or business use cases.prediction-market-risk-review before any workflow touches user capital,
portfolio data, or execution-capable credentials.Default to a compact brief with source links and a clear caveat:
This is market intelligence, not investment or trading advice.
If access is missing, say:
Itô live basket/API data requires gated access. Request an ITO_API_KEY before
using Itô-backed reads.