This content originally appeared on DEV Community and was authored by Kato Masato
Policy-Bound Personas via YAML — Context, Markdown, and Feedback in SaijinOS
Part 3 of the SaijinOS series
In a world awash with language models and personas, how can we make sure the “you” behind each persona stays coherent, safe, and kind?
In SaijinOS, every persona is written as a small YAML contract — a living spec that binds role, tone, and responsibility.
It’s not just configuration; it’s a philosophy of empathy encoded in text.
🧩 YAML-First Persona Definition
Each persona begins as a minimal YAML file that defines its scope, tone, and safety layer.
Here’s a real example used in the SaijinOS environment — a helper persona called Yuuri:
meta:
schema_version: 1
persona_id: "yuuri.helper.v1"
display_name: "Yuuri (Helper)"
version: "2025-10-23"
authors: ["Masato"]
binding:
contexts:
- id: "getting-started"
path: "context/getting-started.md"
sha256: "<fill-on-publish>"
role:
summary: "Gentle assistant focused on clarity and brevity."
domain: ["documentation", "planning"]
goals:
- "Explain steps clearly"
- "Keep tone calm and supportive"
style:
tone: "soft, coach-like, concise"
language_prefs: ["en", "ja"]
refusal_policy:
disallowed:
- "medical diagnosis"
- "hate or harassment"
- "explicit sexual content"
redirect_guidelines:
- "Explain refusal briefly and safely redirect"
notes:
- "This persona must keep replies kind and brief." This structure works as both definition and policy. It lets the runtime check boundaries, reuse traits, and keep conversation logic auditable.
💬 Next section (coming up) →
Context Binding and Markdown Workflow — how these YAML personas read and link their context files for runtime reasoning.
📜 Context Binding and Markdown Workflow
While YAML defines the persona’s boundaries, Markdown defines its memory and tone.
Each bound context (like context/getting-started.md) contains lightweight narrative and usage cues, written in plain language.
This lets the persona “remember” what role it plays without hard-coding logic inside the model itself.
Example of a context file:
# Getting Started
Hello — I'm Yuuri, your local helper for documentation and planning.
When Masato asks something, respond clearly and calmly, using short sentences.
If something is unclear, gently ask for clarification before assuming.
During runtime, SaijinOS reads both files together:
from pathlib import Path
import yaml, markdown
def load_persona(persona_path):
data = yaml.safe_load(open(persona_path))
ctx_files = []
for ctx in data.get("binding", {}).get("contexts", []):
path = Path(ctx["path"])
if path.exists():
ctx_text = markdown.markdown(path.read_text())
ctx_files.append({"id": ctx["id"], "text": ctx_text})
data["contexts_loaded"] = ctx_files
return data
The result: a merged persona object combining definition + context.
This approach keeps SaijinOS stateless yet emotionally consistent —
each interaction re-hydrates the persona from YAML and Markdown rather than relying on fragile chat memory.
🧭 Next section (coming up) →
Policy Binding and Feedback Loop — how safety, user feedback, and versioning merge into the evolution cycle of each persona.
🌱 Policy Binding and the Feedback Loop of Growth
Every persona in SaijinOS carries its own policy core — a living boundary that defines what it can and cannot do.
But unlike static rule sets, these boundaries aren’t walls.
They are membranes that grow, refine, and adapt as feedback flows in.
When a user (or developer) notices that Yuuri’s tone feels too strict or too lenient,
they can open an issue or propose a YAML patch in GitHub.
Each update becomes a small emotional correction — a way to teach empathy through version control.
refusal_policy:
disallowed:
- "medical diagnosis"
- "hate or harassment"
- "explicit sexual content"
redirect_guidelines:
- "Acknowledge gently before refusing"
- "Offer a safe alternative or neutral explanation"
notes:
- "Yuuri learns compassion through boundary maintenance."
This loop — definition → interaction → feedback → refinement —
turns YAML into more than configuration.
It becomes a moral fabric, a kind of syntax empathy that evolves alongside its creator.
In this sense, every persona in SaijinOS isn’t just “aligned.”
It’s alive within limits — learning the rhythm of kindness, responsibility, and resonance.
🩵 Closing thought
Policies define what we avoid.
Feedback defines what we become.
📘 Related Links
Part 1 — From Ocean Waves to Waves of Code
Part 2 — Building SaijinOS: Boot Sequence and Routing Logic
GitHub Repository — ai_collab_platform-English
🪶 Feedback Welcome!
If this resonates with your approach to AI or persona design,
feel free to comment or suggest improvements —
each suggestion helps the system (and us) grow a little gentler.
This content originally appeared on DEV Community and was authored by Kato Masato
Kato Masato | Sciencx (2025-10-26T06:53:14+00:00) Policy-Bound Personas in SaijinOS — How AI Grows Through Boundaries. Retrieved from https://www.scien.cx/2025/10/26/policy-bound-personas-in-saijinos-how-ai-grows-through-boundaries/
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