Okfy — AI skill for Claude Code

OKFy

Tooling that eases context assembly for models. When a full knowledge base is too large for any context window — or actively harmful, diluting the model's attention with task-irrelevant material — OKFy extracts knowledge from corpora (codebases, docs, research collections) into purpose-shaped **OKF Bundles** and gives models precise access to just the slice a task needs. An OKF Bundle is a self-contained directory of Markdown concepts with YAML frontmatter, shaped by a declared **Purpose** and consumable by any agent — even one without OKFy installed: raw file traversal works, the CLI makes it fast, MCP makes it a tool surface.

The pipeline is split by responsibility. Everything deterministic — validation, indexing, BM25 retrieval, lexicon-driven query expansion, segmentation, eval records, write gates, packaging — lives in an agent-neutral Python core (`core/`, PyYAML is its only dependency, zero LLM calls). The LLM-driven work — the Purpose Interview, schema design, blind parallel extraction, consolidation, and the judgment inside a lexicon or an eval verdict — is an agent workflow driven by Claude Code plugin commands that call that core CLI. The core is the machine; the agents supply judgment on top of it. Retrieval is deliberately **vector-free**: BM25 plus a reviewed, machine-readable lexicon closes the cross-language gap deterministically, so the same query returns the same answer, every run, auditable.

**Extraction quality is not self-certified.** OKFy makes no claim that a bundle is good because the model that built it said so. Acceptance is a replayable artifact: `okfy eval run` replays the bundle's own test queries deterministically, an LLM-judge may *propose* verdicts, but only an **owner** verdict counts toward release — a bundle grading its own output is a closed loop this design refuses to trust. The verdicts, the query-expansion contract, and every "this concept is no longer current" flag are recorded inside the bundle, not in a chat transc