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prediction-market-oracle-research

Research prediction markets as data sources or oracle signals for products, agents, dashboards, and corporate decision intelligence. Use for source-grounded analysis of market-implied probabilities, caveats, and integration patterns without investment advice. Use when evaluating prediction markets as a data source or oracle signal for a product, agent, or dashboard.

Quality

88%

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SecuritybySnyk

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SKILL.md
Quality
Evals
Security

Quality

Content

86%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A tight, well-structured research skill: lean prose, a concrete quality checklist, a defined recommendation scale, and an explicit output contract with a mandatory closing disclaimer. Its only weaknesses are the absence of a worked example (sample market source, sample output) and explicit validation steps such as a staleness re-check before recommending a signal.

Suggestions

Add a brief worked example to the Output Contract section — one real market snapshot (question, venue, implied probability, timestamp, link) and a sample 'usable/weak/unsuitable' verdict — so the abstract sections become concrete.

Add an explicit validation step before step 6 (e.g., re-check that probabilities and liquidity figures are current and sources resolve before issuing the recommendation) to close the workflow's missing feedback loop.

Name a few concrete starting venues or source types (e.g., established CFTC-regulated exchanges, aggregator APIs, on-chain venues) so 'find relevant markets, events, tags, and venues' has concrete execution detail.

DimensionReasoningScore

Conciseness

Lean and efficient throughout: it never explains what prediction markets are or other concepts Claude already knows, and every section (Guardrails, Workflow, Integration Patterns, Output Contract) earns its place, matching anchor 5.

5 / 5

Actionability

Mostly executable guidance for an instruction-only skill: a concrete signal-quality checklist (liquidity, spread, market age, resolution authority), a defined recommendation scale ('usable, weak, or unsuitable'), and exact output sections plus a verbatim disclaimer line. Minor gaps — no worked example or concrete venue/API pointers keep it below anchor 5.

4 / 5

Workflow Clarity

A clear six-step sequence with an embedded quality-evaluation checkpoint in step 4, but no explicit validation/feedback loop (e.g., re-checking staleness or liquidity before finalizing the recommendation), matching anchor 4 rather than anchor 5's explicit validation with error recovery. The destructive/batch cap of 3 does not apply since the workflow is read-only research.

4 / 5

Progressive Disclosure

No bundle files exist and none are needed for this short, single-purpose skill; the body is organized into clear sections (Guardrails, Research Workflow, Integration Patterns, Output Contract) and its one cross-skill reference to llm-trading-agent-security is clearly signaled, so the simple-skill exception applies.

5 / 5

Total

18

/

20

Passed

Description

87%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong description: explicit third-person what and when clauses, concrete trigger phrasing, and a well-defined niche that avoids conflicts with general research skills. The only gaps are research-abstract action verbs and missing a few natural synonyms or venue names that users might say.

DimensionReasoningScore

Specificity

Names the domain and several specific actions ('Research prediction markets as data sources or oracle signals', 'source-grounded analysis of market-implied probabilities, caveats, and integration patterns') in third person, but the actions stay research-abstract rather than fully concrete, matching 'several specific actions; minor gaps' rather than the comprehensive anchor 5.

4 / 5

Completeness

Clearly answers both what ('Research prediction markets as data sources or oracle signals for products, agents, dashboards, and corporate decision intelligence') and when with concrete trigger phrases ('Use when evaluating prediction markets as a data source or oracle signal for a product, agent, or dashboard'), matching anchor 5 exactly.

5 / 5

Trigger Term Quality

Good natural keyword coverage ('prediction markets', 'oracle signals', 'data source', 'dashboards', 'agents', 'market-implied probabilities'), but common variations users might say are missing (e.g., 'forecasting', venue names like Polymarket/Metaculus/Kalshi), matching anchor 4 rather than the comprehensive-synonyms anchor 5.

4 / 5

Distinctiveness Conflict Risk

'Prediction market' is a clear niche trigger with distinct phrasing; overlap with general research or forecasting skills is minimal, matching anchor 5 (clear niche with distinct triggers) rather than anchor 4's 'minor overlap risk with closely related skills'.

5 / 5

Total

18

/

20

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_version

'metadata.version' is missing

Warning

Total

15

/

16

Passed

Repository

affaan-m/ECC

Path

skills/prediction-market-oracle-research/SKILL.md

Reviewed

Table of Contents

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