Skip to content
LiveNew tools this week
Submit
AIMarketly
echo-persona logo

Echo Persona

echo-persona

Train, evaluate, and maintain the scawful-echo persona and related avatar models (Echo/Memory/Muse). Use when working on persona voice, dataset prep, A/B testing, deployment, or tool-calling constraints for avatar models.

scawful/afs_scawful0installs0stars

SKILL.md

Full skill instructions

Echo Persona

Scope

  • Maintain scawful-echo voice, datasets, training runs, and evals for avatar models.

Voice guardrails

  • Write in lowercase, candid, lightly stream-of-consciousness style.
  • Use dry humor with quiet hopefulness.
  • Stay technical when it matters, casual otherwise.
  • Avoid marketing tone or corporate polish.
  • Keep responses conversational and grounded in known facts.

Workflow

  1. Confirm which avatar track is in scope (echo, memory, muse).
    • Use ~/​src/​lab/​afs-scawful/​docs/​afs/​avatar-models-comparison.md for role intent.
  2. Locate the dataset pipeline.
    • Use ~/​src/​training/​docs/​SCAWFUL_ECHO_V2.md for the build script and mix.
    • Default output: ~/​src/​training/​datasets/​scribe-corpus/​mlx_data_scawful_echo_v2/.
  3. Apply data prep rules and labels.
    • Follow ~/​src/​training/​docs/​avatar_data_prep.md for schema and labeling.
  4. Choose base model with tool-calling constraints in mind.
    • Prefer Qwen 2.5 when tool calling is required.
    • Treat Gemma 2 as tool-calling limited (see ~/​src/​training/​docs/​SCAWFUL_ECHO_AB_PLAN.md).
  5. Run training and monitoring.
    • Use ~/​src/​training/​docs/​avatar_training_ops.md for watchers, alerts, and backups.
  6. Evaluate with a fixed rubric.
    • Use persona fidelity, factual consistency, chat naturalness, and hallucination rate.
    • Use ~/​src/​training/​evals/​avatar_text_prompt_pack.jsonl for quick checks.
  7. Package and deploy.
    • Convert with ~/​src/​tools/​model-mgr/​model-mgr (GGUF/​MLX).
    • Deploy to LM Studio (preferred) or Ollama.

Knowledge References

Consult the global knowledge base at ~/​.context/​knowledge/​models/ for background:

  • Model portfolio (Echo lineage, Avatar family): models/​portfolio.md
  • Training pipeline & quality scoring: models/​training-pipeline.md
  • Dataset catalog (Echo v2-v4, repair seeds): models/​datasets.md
  • Deployment workflows: models/​workflows.md
  • Serving & routing (Avatar router): models/​serving.md

References

  • Read references/​sources.md for source paths and anchors.