Idmp Ai

ai

Read [../idmp-shared/SKILL.md](../idmp-shared/SKILL.md) first.

What this skill covers

• Prove AI availability, AI-visible data, chat-session visibility, and generation boundaries before claiming success.
• Keep generation grounded in a real element scope and hand off to non-AI workflows when the backend is empty or unavailable.

Recommended reference

• [references/ai-read-flows.md](references/ai-read-flows.md)

Missing context to resolve first

• AI backend availability.
• Record policy.
• Whether the operator wants availability only, recommendations, a draft object, or a recorded chat.
• The target element scope for recommend or create requests.
• Whether record:true is acceptable.

Constrained live behaviors

• Treat datasource available list=false as a hard stop.
• idmp-cli datasource available list is the hard availability gate.
• recommend can return an empty array.
• idmp-cli ai anydata list measures AI-visible data, not chat history.
• idmp-cli ai chat sessions is the safest proof that recorded chat is visible.
• ai recommend create and ai recommend create-post still need questionType values from the backend QuestionType enum. Use GENERAL_QUESTION, GEN_PANEL_QUESTION, or GEN_ANALYSIS_QUESTION; do not send intuitive aliases such as PANEL or ANALYSIS.
• Keep the endpoint path in the evidence: ai recommend create calls /api/v1/ai/panels/recommend, while ai recommend create-post calls /api/v1/ai/prompts/recommend.
• Recommendation endpoints can return []; classify that as empty output instead of inventing advice.
• If the backend returns a structured error, surface that error verbatim.
• If recommend fails with Can't parse the value: PANEL to AiGenPromptType, classify it as a backend contract mismatch instead of datasource or auth unavailability.
• If recorded chat creation complains about an invalid session, retry with sessionId:null.
• AI analysis draft creation can time out even when datasource available list and ai anydata list are healthy; classify context deadline exceeded as backend AI/API latency and hand off to structured analysis workflows instead of mutating the request semantics blindly.

Execution flow

1. Start with idmp-cli datasource available list and stop immediately if availability is false.
2. Use idmp-cli ai anydata list to prove the AI backend can see usable data instead of assuming data visibility from availability alone.
3. Read idmp-cli ai chat sessions before any recorded chat or follow-up AI write so the session state is grounded in a real reread.
4. Use idmp-cli ai recommend create-post --ack-risk --data with the final questionType locked to GENERAL_QUESTION, GEN_PANEL_QUESTION, or GEN_ANALYSIS_QUESTION, then classify the result as populated, empty, structured failure, or backend contract mismatch.
5. Use idmp-cli ai create create --ack-risk --data or idmp-cli ai create create-post --ack-risk --data only when a draft object is requested, then prove the response actually contains a generated object before claiming success. If analysis draft creation times out, stop and hand off to the structured analysis workflow instead of claiming AI unavailability.

Evidence of completion

• Availability is only complete when datasource available list and ai anydata list agree on the backend state.
• Recommendation work is only complete when the returned array, empty result, or structured error is preserved exactly; do not fabricate advice.
• AI create work is only complete when the response proves a real generated draft or clearly classifies the failure boundary.

Key commands

1. idmp-cli datasource available list to prove the backend is reachable.
2. idmp-cli ai anydata list to prove the AI service can see usable data.
3. idmp-cli ai chat sessions to inspect visible chat history before new writes.
4. idmp-cli ai recommend create-post --ack-risk --data '{"elementId":123,"questionType":"GENERAL_QUESTION","async":true}' for generic prompt suggestions.
5. idmp-cli ai recommend create-post --ack-risk --data '{"elementId":123,"questionType":"GEN_PANEL_QUESTION","async":true}' or ...GEN_ANALYSIS_QUESTION... for panel or analysis-oriented recommendations.
6. idmp-cli ai create create --ack-risk --data '{"elementId":123,"prompt":"Create a 1-minute average-current analysis","record":true,"deepThinking":false,"deviceDocument":false}' for analysis drafts.
7. idmp-cli ai create create-post --ack-risk --data '{"elementId":123,"prompt":"Create a maximum-current trend panel","record":true,"deepThinking":false,"deviceDocument":false}' for panel drafts.

Exception paths

• Stop if availability is false; hand off to datasource or asset bootstrap workflows instead of forcing AI generation.
• Keep empty recommendation results separate from backend failures.
• Do not claim a panel or analysis exists unless the response payload proves it and the draft is visible through a follow-up read.
• If create fails, stop and report the backend-unavailable state.

Validation scenarios

1. Availability gate

Run idmp-cli datasource available list and idmp-cli ai anydata list first. Success here only means the environment can support later AI work.

2. Data readiness

Use idmp-cli ai anydata list after availability passes. If the list is empty, treat that as weak AI context rather than a fabricated success.

3. Session visibility

Read idmp-cli ai chat sessions before recorded chat creation. Only use session detail reads if the list result gives you a trusted session ID.

4. Recommendation path after availability passes

Use idmp-cli ai recommend create-post --ack-risk --data with a locked element scope and an explicit backend enum such as GENERAL_QUESTION, GEN_PANEL_QUESTION, or GEN_ANALYSIS_QUESTION. Empty arrays, structured backend errors, and enum-contract errors are all valid, different outcomes.

5. Explicit unavailable-backend failure branch

If idmp-cli ai create create --ack-risk --data or idmp-cli ai create create-post --ack-risk --data fails, classify the failure and stop. Distinguish backend timeouts from true availability failures, and never report a generated object unless the response proves it.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: taosdata
• Source: taosdata/agent-skills
• License: MIT

Install and usage instructions live in the source repository linked above.