Дмитрий
40 lines
1.6 KiB
Markdown
40 lines
1.6 KiB
Markdown
# ADR-007: ML / AI tooling (A11)
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- **Status:** Accepted
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- **Date:** 2026-05-17
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- **Deciders:** Дмитрий
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## Context
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The `A11 «ML / AI-разработка»` map section had zero tooling. Лидерра ships no
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ML/AI code; `calc_lead_score` is a deterministic SQL function. A toolset is needed
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for the day AI features (LLM-backed) or a scoring model are scoped.
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## Decision
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A11 adopts a six-position toolset in two subcategories:
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- **LLM integration** — the claude-api skill (build), promptfoo (test prompts),
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Sentry MCP (observe). All reuse or new-light.
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- **Classical ML** — a vendored Data Scientist skill (workflow knowledge). The
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executable part, **Jupyter MCP**, is **deferred**: it needs a Python ML runtime
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the deliberately-minimal native-Windows machine lacks, and there is no model to
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train. Jupyter MCP is a reserved registry slot, installed by a separate task
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when a concrete model is scoped.
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- promptfoo runs manually / CI only — never in a hook (paid LLM calls).
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- A11 tools are non-UI → the `ml-ai-tooling` off-phase category.
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## Consequences
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- Positive: A11 populated; AI features have a build+test+observe toolchain.
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- Risk: the Data Scientist skill is third-party (CC BY 4.0 content) — mitigated by
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vendoring with attribution into `.claude/skills/data-scientist/`.
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- Cost: promptfoo is a heavy devDependency (~1090 transitive packages, one native
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module). Accepted — it is dev-only tooling, not shipped to the app.
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- Deferred: no Python runtime until a model is scoped — accepted, this is the
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decision.
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## Enforcement
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None — A11 tools are advisory; verified by use and code review.
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