central-asia-caspian-hybrid-intelligence-skill
OpenClaw skill: central-asia-caspian-hybrid-intelligence-skill
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// RATINGS
Not yet listed on ClawHub or SkillsMP
// README
Central Asia & Caspian Intelligence Skill
A reusable regional reasoning skill for AI agents preparing decision memos on Central Asia and Caspian exposure.
Use it when a general policy memo misses the region's mechanisms: banking and payment routes, beneficial ownership, sanctions and AML exposure, trade corridors, logistics, and energy infrastructure risk.
This is a reasoning skill and decision framework, not a live intelligence feed. The included experimental MCP server is a skeleton: every tool returns not_implemented. It does not retrieve live intelligence, screen counterparties, or issue clearances.
Why this project exists
Regional exposure questions are often reduced to generic geopolitical summaries. This skill gives an agent a more structured way to reason about Central Asia and the Caspian corridor:
- mechanism-first analysis, not just country summaries
- explicit separation of facts, inference, assumptions, and source conflicts
- decision alternatives, escalation conditions, and evidence that would change the recommendation
- a clear handoff for deterministic evidence checks
It is designed to improve the quality of strategic reasoning, not to replace human judgment or legal review.
What this project includes
- a reusable reasoning contract in SKILL.md
- runtime overlays for Claude, Codex, and OpenClaw
- worked examples in examples/README.md
- an evidence handoff to Agenda Intelligence MD
- regional guardrails and explicit uncertainty handling
Choose a starting point
| Your task | Worked example | Evidence to inspect |
|---|---|---|
| Banking or counterparty exposure | Open example | Reasoning-only decision framing |
| A Middle Corridor source pack | Open example | Claims tied to supplied sources |
| Beneficial-ownership uncertainty | Open example | A dated source-backed example |
Start with the skill
- Load the canonical SKILL.md.
- Add the matching runtime overlay: Claude, Codex, or OpenClaw.
- For a recurring practice, follow the cold-start interview to establish decision context and practice profile.
- Give the agent a concrete decision brief.
Example prompt:
Use Central Asia & Caspian Intelligence Skill.
Question: What evidence would justify a limited logistics pilot?
Decision: proceed with a limited pilot, change the plan, or wait.
Audience: risk committee.
Geography: Kazakhstan and the Caspian corridor.
Time horizon: next 90 days.
Evidence mode: reasoning-only; this is a hypothetical case.
Separate facts from assumptions, compare alternatives, and identify
missing evidence and escalation conditions.
For sourced analysis, choose live-source-backed, user-provided sources, or illustrative source packet explicitly. Supply the source packet or authorize collection. Preserve source dates and disclose unresolved source conflicts.
See regional logic, the source guide, and the analysis contract. The currency watch is a supporting regional lens for financial exposure analysis.
Place in the system
| Repository | Responsibility |
|---|---|
| Global Think Tank Analyst | General decision-memo method and executable artifact toolkit |
| This skill | Regional mechanisms, sources, and uncertainty |
| Gulf & Middle East | Complementary reasoning for exposure crossing the regional boundary |
| Agenda Intelligence MD | Deterministic checks on supplied claim/source records |
The companion patterns explain composition. Loading the skills alone does not invoke a verifier or authorize an external action. Evidence-packet checks assess declared support and consistency; they do not validate real-world facts on their own.
Examples
For maritime fleet and port/rail questions, the Caspian capacity reference distinguishes payload, cycle time, interface constraints, and demand constraints. It adds reasoning guidance, not a live data service.
Start with the example guide, then inspect a case that matches your evidence mode:
| Example | What to inspect |
|---|---|
| Banking exposure | Reasoning-only decision framing |
| Middle Corridor packet | Supplied sources and bounded conclusions |
| Ownership opacity | Dated primary-source evidence |
| Conflicting trade estimates | Unresolved disagreement in an illustrative packet |
Evidence-mode counts: reasoning-only=6; illustrative source packet=2; live-source-backed=6; user-provided sources=2.
Source-backed examples are historical snapshots. Recheck current primary sources before reuse. Example counts describe coverage, not validated analytical performance.
What this is not
This project does not:
- provide live regional intelligence feeds
- screen counterparties or sanctions exposure automatically
- certify legal, regulatory, or sanctions compliance
- authorize external action or payment decisions
- substitute for a human reviewer in operational decisions
The skill is a structured reasoning and evidence-handling layer. The actual decision remains with a human or an authorized process.
Validation and limits
The repository has cleared its documented Bar 2 requirements; substantive regional reasoning lift remains unproven. The next specialist-lift evaluation tracks the relevant quality frontier.
There is no public, attributable real-use record. No production-usage, adoption, or benchmark numbers are claimed. STATUS.md preserves the evaluation record and the limits of self-scoring and self-checking.
Human review is required before operational use. The skill does not provide legal advice, sanctions clearance, or payment enforcement. The guardrails define these boundaries.
Documentation and contribution
- SKILL.md: canonical instructions; runtime overlays remain additive
- Evidence-packet handoff: the primary verification contract
- CONTRIBUTING.md and AGENTS.md: contribution workflow and repository constraints
Run the repository validator before submitting changes:
python3 scripts/validate.py
The skill package and examples remain the primary interface. The Python MCP skeleton is retained for development and does not deliver analytical findings.