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name: project-workflows description: "Route Align-Anything satellite project workflows and decide runnable versus extension versus reference-only use." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

Project Workflows

Use this sub-skill when a task mentions an Align-Anything satellite project under projects/, project-local scripts, Janus workflows, InterMT, language-feedback generation, text-image-to-text-image Chameleon workflows, any-to-text model initialization, or the bundled Eval-Anything project. This sub-skill helps decide whether the project material is immediately runnable, an extension pattern for core Align-Anything trainers, or reference-only evidence.

Do not use this sub-skill as the primary guide for ordinary Align-Anything trainer modules, core package APIs, or generic installation. Route those through the root skill or the more specific training/evaluation sub-skills, then return here only for project-folder evidence.

First Decision

1. Identify the project signal in the user request: any_to_text, janus, intermt, lang_feedback, text_image_to_text_image, or eval-anything.

2. If working inside an Align-Anything checkout and the user asks what is available, run the bundled discovery script. It parses files and shell snippets but does not import or execute project code:

   python scripts/list_project_entrypoints.py --root <repository-root>
   

   Use --json for machine-readable output.

3. Read references/project-map.md for routing status, prerequisites, data-shape expectations, and runnable/extension/reference-only decisions.

4. For eval-anything, also read references/eval-anything-notes.md; treat it as a separate package surface unless a dedicated runtime has been prepared.

5. When something fails or appears inconsistent with the project README, read references/troubleshooting.md before assuming the core package is broken.

Routing Matrix

Project signal	Default treatment	Runnable only when	Use as extension/reference when
projects/any_to_text	Runnable builder scripts plus training-pattern evidence.	Align-Anything imports work, Transformers/Torch can load the requested base LLM, CLIP vision tower, and optional CLAP audio tower, and the output directory is intentionally chosen.	Designing custom multimodal initializers or interpreting staged any-to-text training flags.
projects/janus and scripts/janus	Optional Janus workflow.	The separate Janus-compatible package is installed, Janus model weights are available, GPU capacity is planned, and tokenized .pt data conventions match the target trainer.	Understanding how Align-Anything wires Janus SFT/DPO generation and understanding trainer shell patterns.
projects/intermt	Reference-only by default.	Only after a separate InterMT-Bench/data runtime is intentionally prepared.	Explaining InterMT dataset/benchmark intent and selecting multi-turn multimodal preference-alignment evidence.
projects/lang_feedback	Internal/development workflow; cautious runnable pattern.	vLLM, a multimodal model, GPU tensor parallel plan, images, and the expected JSON fields are available.	Reusing the base → critique → refine data-generation pattern without claiming stable public support.
projects/text_image_to_text_image	Chameleon preprocessing/training pattern with optional runtime.	A Chameleon-capable Transformers fork/model and GPU memory are available, and the dataset schema matches the selected tokenizer script.	Planning text-image interleaved SFT/DPO/RM/PPO workflows, especially pre-tokenization before core trainers.
projects/eval-anything	Separate package/CLI/pipeline reference unless a heavy runtime is prepared.	A Python 3.11-compatible Eval-Anything environment with vLLM/HF/API backend dependencies, model weights or API credentials, datasets, and GPU or backend resources is intentionally prepared.	Mapping safety benchmark configuration, package entry points, model backends, and pipeline extension points.

Operating Rules

• Treat project README commands as examples, not proof that a runtime is ready. Confirm optional packages, model identifiers, dataset schema, device count, and output directories first.
• Do not run repository project scripts merely to discover capabilities. Use the bundled discovery script or inspect files statically.
• Do not preserve placeholder path tokens in runnable commands; replace them with user-supplied locations or stop and ask.
• Keep eval-anything separate from the main align_anything package. It has its own package metadata, CLI, configs, benchmark registry, model backend registry, and optional VLA dependency group.
• If a project depends on another forked repository, external datasets, large model downloads, or API credentials, mark it optional or blocked until those are explicitly prepared.

Handoff Checklist

When handing a project-workflow decision to another sub-skill or to execution, include:

• Project folder and entrypoint name.
• Decision: runnable, extension pattern, or reference-only.
• Required package/runtime deltas beyond the base Align-Anything install.
• Expected input schema and output artifact type.
• Device/backend assumptions and whether CPU substitution is unsupported, partial, or unknown.
• Any known script caveat from references/troubleshooting.md.