jinja-expert
SolidAuthor, read, and debug Jinja2 templates across the three places Jinja lives in 2026 — HuggingFace `chat_template.jinja` (rendered by `apply_chat_template` for vLLM / sglang), Ansible playbooks + `.j2` files, and Jinja-adjacent Kubernetes workflows (`values.yaml.j2`, `kubernetes.core.k8s + template`, Helm post-renderers). Companion to the `helm` skill — Helm charts are Go `text/template` + Sprig, not Jinja, and this skill makes that disambiguation explicit.
Install
Quality Score: 79/100
Skill Content
Details
- Author
- air-gapped
- Repository
- air-gapped/skills
- Created
- 3 months ago
- Last Updated
- yesterday
- Language
- Python
- License
- MIT
Integrates with
Bundled in these plugins
Similar Skills
Semantically similar based on skill content — not just same category
vllm-chat-templates
vLLM chat-template (prompt-side Jinja) operator reference. Template resolution precedence (`--chat-template` → AutoProcessor → tokenizer default → bundled fallback), `chat_template_kwargs` allowlist silently dropping `add_generation_prompt`/`enable_thinking`/custom kwargs (PR 27622 fix), 27 shipped `tool_chat_template_*.jinja` files, known template-layer bugs for Qwen3/Qwen3-Coder, DeepSeek-R1/V3/V3.1/V3.2, GPT-OSS, Kimi-K2, Llama-4, Mistral (HF vs mistral mode), Gemma-3/4, Phi-4, GLM. Prompt side only — output parsing lives in sibling skills.
transformers-config-tokenizers-expert
Preflight reference for HuggingFace snapshots — what vLLM, sglang, and transformers.generate see at runtime. Covers config-file precedence (tokenizer.json, tokenizer_config.json, generation_config.json, chat_template.jinja), transformers v5 tokenizer-class taxonomy (TokenizersBackend, PythonBackend, MistralCommonBackend, TikTokenTokenizer), special-token discovery (all_special_ids, added_tokens_decoder, extra_special_tokens, backend_tokenizer.get_added_tokens_decoder), chat-template Jinja contract (ImmutableSandboxedEnvironment, loopcontrols, raise_exception, strftime_now, tojson, add_generation_prompt), and engine knobs (skip_special_tokens, trust_request_chat_template, chat_template_kwargs allowlist, adjust_request, incremental detokenizer, EOS merge). Ships verified 2026 hall-of-shame for Kimi-K2.6, GLM-5.1, Gemma-4, Qwen3, DeepSeek-V3, plus drop-in Python for resolving markers to IDs, detecting turn-primer-as-EOS leaks, and cross-referencing tokenizer.json vs tokenizer_config.json.
baml-expert
BAML (Boundary ML) expert for projects defining LLM calls as typed functions in .baml files with a generated Python client. Use whenever the repo contains baml_src/, baml_client/, baml-cli commands, or imports from baml_py / baml_client. Covers .baml syntax (function, class, enum, client, test, retry_policy, attributes), Python integration (baml_client sync/async, streaming, ClientRegistry, Collector, TypeBuilder), Schema-Aligned Parsing, ctx.output_format, @@assert / @@check tests, @stream.done / @stream.not_null / @stream.with_state streaming, multimodal (image/audio/pdf), and debugging via BAML_LOG plus Boundary Studio. Triggers even unnamed — "add an LLM function", "fix a failing parse", "add a test for the prompt", "stream the response" in a project with baml_src/. Prefer over raw LLM-SDK guidance here; defer to jinja-expert for standalone chat-template / .j2 work.