--- name: anyjev description: > Operate nokia-applied-research/AnyJev, the Apache-2.0 Python library for training-free typed choice, yes/no, and score decisions from next-token probabilities. Use when pinning or inspecting a checkout, selecting raw/L0 Decider readouts, running approved Tacit checkpoints, configuring HF/vLLM/ SGLang/llama.cpp backends, checking tokenizer or calibration limits, or using the local gateway. Triggers on AnyJev, Tacit, L0, permutation debiasing, llama.cpp logprob, SGLang /generate, or comparisons with hosted TypeSafe AI/Jev. Do not use for TypeSafe hosted services or a generic Jev harness. allowed-tools: Bash Read Write Edit Glob Grep compatibility: > Python >=3.10. Model weights and datasets are opt-in downloads. HF/transformers, vLLM, SGLang, and llama.cpp have separate runtime and hardware requirements; remote servers and model providers may require credentials or incur compute cost. license: Apache-2.0 metadata: version: "1.1.0" source: https://github.com/nokia-applied-research/AnyJev upstream_commit: d458d13968eec0e2cfa8c81b64629b96cf5cb4a9 --- # AnyJev operator This skill targets the pinned AnyJev checkout at commit `d458d13968eec0e2cfa8c81b64629b96cf5cb4a9` (package `0.3.0`, audited 2026-10-07). Detailed commands, limits, backend contracts, and citations live in [`references/upstream.md`](references/upstream.md). ## When to use this skill - Inspect or pin an AnyJev checkout without downloading weights or datasets. - Build a training-free `Decider` readout and distinguish `raw` from `L0`. - Run an approved Tacit checkpoint with transformers, vLLM, or a vLLM server. - Configure HF, vLLM, SGLang, or GGUF/llama.cpp backends and tokenizer rules. - Evaluate adaptive rotation calibration or the local `anyjev.serve` gateway. - Record a bounded research readout with model, backend, and source evidence. Use external TypeSafe AI documentation or its own skill for hosted System One/Jev APIs, credentials, billing, or a separate Jev harness. AnyJev is an independent self-hosted open-model library; its manifest has no `typesafe-ai` dependency. ## Instructions ### 1. Choose one route Select exactly one route before changing anything: `inspect`, `decider`, `tacit`, `backend`, `gateway`, or `research`. Read the corresponding pinned details in `references/upstream.md`; do not infer newer `main` behavior. ### 2. Run the offline preflight first From the repository root: ```bash python3 .agent-skills/anyjev/scripts/preflight.py --json ``` From an installed skills root: ```bash python3 "$SKILLS_ROOT/anyjev/scripts/preflight.py" --json ``` Add `--repo PATH --expect-commit d458d13968eec0e2cfa8c81b64629b96cf5cb4a9` to inspect a literal local checkout path. The helper only reads Python and distribution metadata, host/tool presence, public checkout shape, and a local Git commit. `runtime_verified=false` is always honest: detected packages are not working inference, activated servers, loaded models, or quality evidence. Optional backend extras may be absent; base AnyJev still requires Python `>=3.10`, AnyJev metadata, and NumPy `>=1.24`. ### 3. Execute the approved route Install only the selected optional extra and make model, dataset, server, and credential side effects explicit. Preserve the `Question` option/level contracts, require `L0` when it is part of the acceptance condition, and never describe Tacit's one-forward output as an L0-averaged result. For HTTP backends, verify tokenizer availability and required log-probability settings; keep the gateway loopback-bound unless external authentication and network policy are in place. ### 4. Verify and report For no-download API plumbing, the pinned `FakeBackend` smoke path is allowed but is fixture evidence, not model-quality evidence. For real inference report the AnyJev commit, model revision, backend/engine, tokenizer, dtype/device, question layout, prior/adaptive settings, and dataset license. Report package metadata and PATH detections separately from successful inference. ## Examples - **Inspect only:** run the offline preflight with `--repo` and the pinned `--expect-commit`; then read the pinned metadata and limitations. - **Decider:** use a causal-LM backend and `Decider(level="L0")`; inspect the returned `distribution` and explicit `level` before acting. - **Tacit service:** approve the checkpoint, vLLM engine, loopback endpoint, and compute first; then verify `/health`, `/v1/stats`, and one `/v1/decide`. - **No-download smoke:** use `FakeBackend` only to exercise prompt/readout plumbing and state clearly that it says nothing about accuracy. ## Best practices 1. Keep model/dataset downloads, optional installs, remote-server calls, and provider credentials opt-in; local inference still consumes compute. 2. Prefer `Decider(level="L0")` when option-order bias matters and enforce the level; choose `prior="content_free"` or `prior="none"` deliberately. 3. Calibrate adaptive shifts with `canonical_order=True`; the certificate is agreement with full-K AnyJev output, not external accuracy. 4. Treat Decider `answer_mass` as prompt-landed evidence; treat Tacit's `margin` and `route` as diagnostics, not universal confidence; keep its escalation cap enabled for shared services. 5. Treat GGUF/Hugging Face tokenizer disagreement as a safe failure and keep non-loopback gateways behind authentication and network policy. 6. Preserve Apache-2.0 notices and identify benchmark dataset licenses; do not generalize isolated decision evidence into agent-loop claims. ## References - [`references/upstream.md`](references/upstream.md) — pinned commands, backend contracts, tokenizer/calibration limits, gateway boundary, and source index. - [`scripts/preflight.py`](scripts/preflight.py) — offline metadata and checkout shape/commit preflight; it never imports AnyJev or invokes providers. - [AnyJev pinned repository](https://github.com/nokia-applied-research/AnyJev/tree/d458d13968eec0e2cfa8c81b64629b96cf5cb4a9) - [AnyJev Apache-2.0 license](https://github.com/nokia-applied-research/AnyJev/blob/d458d13968eec0e2cfa8c81b64629b96cf5cb4a9/LICENSE)