Send proofs, planning questions, or difficult debugging tasks to DeepSeek's reasoning model. Receive the final answer and model-provided reasoning text in separate fields, with token usage reported for reviewing responses and costs.
Reasoning-mode completion against DeepSeek's `deepseek-reasoner` model (R1) via /v1/chat/completions. Surfaces the model's chain-of-thought (`reasoning_content`) separately from the final answer (`content`), so callers can display or discard the CoT without re-parsing. Reads DEEPSEEK_API_KEY; degrades gracefully (exit 0 with status:degraded envelope) when unset or the API is unreachable. Ignores temperature/top_p per DeepSeek's spec for reasoner models.
Review the instructions the agent will receive before you install.
Wraps DeepSeek's reasoning model so ruflo callers can get an explicit
chain-of-thought back with the final answer, without having to parse it
out of the message content. Same subprocess-invocation shape as the sibling
deepseek-chat skill.
deepseek-chat.The prompt and any --system text are sent over the network to api.deepseek.com, a third party. Do not put secrets, credentials, personal data or proprietary code you are not allowed to share into them. The API key is read from DEEPSEEK_API_KEY in your shell only; never paste it into a prompt, a file or a command line.
Implementation: scripts/reason.mjs.
DEEPSEEK_API_KEY from env. Degrade gracefully when missing./v1/chat/completions with { model: 'deepseek-reasoner', messages, max_tokens? }.
Per DeepSeek's docs, temperature/top_p are ignored for reasoner
models — this script does not forward them.choices[0].message.content AND choices[0].message.reasoning_content.reasoning; table mode omits it unless
--show-reasoning is passed.reasoningTokens (from usage.completion_tokens_details.reasoning_tokens)
is surfaced separately so callers can attribute cost.node plugins/ruflo-deepseek-harness/scripts/reason.mjs \
--prompt "Prove that sqrt(2) is irrational." \
--format table --show-reasoning
Table output shows the reasoning block, then the answer. JSON mode returns:
{
"status": "ok",
"model": "deepseek-reasoner",
"content": "sqrt(2) is irrational because …",
"reasoning": "Assume for contradiction that sqrt(2) = p/q in lowest terms …",
"finishReason": "stop",
"usage": {
"promptTokens": 14,
"completionTokens": 812,
"reasoningTokens": 640,
"totalTokens": 826
}
}
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Compare models available through the HuggingFace router with chat-ui configuration, research missing models, and write short English descriptions of their architecture and capabilities. Add entries to both production and development configuration files and commit the changes.
Create an Architecture Decision Record, a document for recording important design choices, with the next sequential number. It generates a Markdown template, registers the record in AgentDB, and links related decisions found through search.
Build a searchable index and relationship graph from ADRs, records of architectural decisions. Import two supported document formats, refresh existing metadata without duplicate relationships, and review summaries of statuses, missing references, and inconsistencies.
Rebuild the index of ADRs, records of architectural decisions, after files or relationship lines are deleted. It clears stored records and relationships, indexes the files still present, and checks that the final record count matches.
Review code changes against accepted Architecture Decision Records, documents that capture agreed design choices. Report violations, outdated references, compliant changes, and areas without ADR coverage, then suggest code fixes or a new decision record where appropriate.
Check saved architecture decision records (ADRs) for broken references, circular replacement relationships, and inconsistent statuses. Use the results to verify imported records or stop a CI check when selected integrity rules fail.