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anyjev

Agents & models

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Updated 10-29 04:35

About this skill

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.

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:

python3 .agent-skills/anyjev/scripts/preflight.py --json

From an installed skills root:

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