Polysona models a person not as a single monolithic identity, but as a structured set of machine-readable files. Every persona lives in its own directory underDocumentation Index
Fetch the complete documentation index at: https://mintlify.com/LilMGenius/polysona/llms.txt
Use this file to discover all available pages before exploring further.
personas/{id}/, composed of exactly three Markdown files in PLOON format. Each file captures a different dimension of identity: the deep psychological core, the voice and platform expression layer, and the external reference models used for calibration. Together they form the complete input set for every Polysona agent — from interview extraction to content generation to QA simulation.
Three-File Overview
persona.md
Core identity structure: unconscious patterns, decision logic, energy profile, and blind spots. The primary target for the Profiler agent’s interview log.
nuance.md
Voice register and platform-specific expression patterns. Defines tone, style, emoji density, and phrase preferences per channel.
accounts.md
Role models and virtual audience profiles. Benchmarks concrete figures and simulates target reader responses for QA.
Directory Structure
Active Persona Resolution
Polysona readspersonas/_active.md to determine which persona is currently loaded. If the file is absent or empty, it falls back to personas/default/. This allows you to switch between multiple persona datasets without changing any agent configuration.
persona.md — Core Identity File
persona.md is the structural heart of a persona. It contains five sections, each with a specific psychological purpose.
Sections
## core — Identity Architecture
## core — Identity Architecture
The
These three rows form the primary contradiction surface — the Profiler agent compares them to detect GAP signals.
core section holds three ego-layer rows in a typed PLOON table:| Layer | What it captures |
|---|---|
unconscious-self | Deep narrative identity, meaning-making patterns (sourced from McAdams, Laddering, Zen Koan) |
conscious-ideal | The self-image the person explicitly endorses (sourced from direct input, Laddering) |
others-see-me | How external observers describe the person (sourced from Johari Window) |
## decide — Decision Logic
## decide — Decision Logic
The
decide section stores a ranked priority list with approach descriptions. Each row maps to a framework source (typically Laddering or Repertory Grid). It answers: when this person must choose, what do they optimize for, and how do they move?## energy — Activation Profile
## energy — Activation Profile
The
energy section records what activates versus drains the person, with context modifiers. It is used during content generation to gauge when the persona’s voice is likely to be most engaged or most guarded.## blind — Blind Spots and Defense Patterns
## blind — Blind Spots and Defense Patterns
The
blind section has two row types:johari— gaps between self-perception and observer feedback (sourced from Johari Window, 五倫)defense— protective IFS parts and stress-triggered coping behaviors (sourced from IFS, Projective Technique)
## interview-log — Chronological Extraction Record
## interview-log — Chronological Extraction Record
The A session closure also appends a
interview-log section stores every insight the Profiler agent appended, in strict chronological order using date-stamped PLOON entries. It is append-only — previous entries are never overwritten or reordered.Each line follows one of two formats:Summary: line consolidating the session’s top signals.Real Example: personas/default/persona.md
View full persona.md content
View full persona.md content
nuance.md — Voice and Platform Layer
nuance.md stores expression patterns that sit above raw identity. While persona.md answers who this person is, nuance.md answers how they sound.
## voice Table
The voice section uses a three-column table: register, style, and avoid. Each row defines a speech register (e.g., casual conversational, semi-formal) alongside what distinguishes it and what to avoid contaminating it with. These registers are extracted by the Profiler via the Clean Language framework, which surfaces the persona’s own metaphor vocabulary.
## platform Table
The platform section maps each publishing channel to its tone, hook pattern, and emoji density. Polysona’s five MVP platforms (X, Threads, LinkedIn, Naver Blog, Brunch) each carry distinct audience expectations and formatting norms.
## phrasing Table
The phrasing section marks specific phrases as prefer or avoid. This allows content-generation agents to stay within the persona’s authentic voice rather than defaulting to generic filler.
Real Example: personas/default/nuance.md
View full nuance.md content
View full nuance.md content
accounts.md — Role Models and Virtual Audience
accounts.md provides external calibration references. It serves two distinct agent use cases: trend detection (which looks at rolemodel) and QA simulation (which looks at virtual).
## rolemodel Table
Each rolemodel row captures a real-world figure, their platform, why they serve as a benchmark, and their characteristic signal. During trend detection, the system watches for content patterns that align with these benchmarks.
## virtual Table
Virtual audience rows describe simulated reader profiles with their primary concern and expectation. QA agents use these profiles to stress-test drafted content against the personas’ target readers before publishing.
Real Example: personas/default/accounts.md
View full accounts.md content
View full accounts.md content
Context Loading by Task
Different agent tasks load different subsets of the three files. The rules are defined inCLAUDE.md and enforced at runtime:
| Task | Files Loaded |
|---|---|
| Interview session | persona.md + interview skill references + latest interview-log |
| Persona introduction | All three files |
| Trend detection | accounts.md rolemodel + domain references |
| Content generation | persona.md core/decide/energy/blind + nuance.md voice/platform |
| QA simulation | Generated draft + accounts.md rolemodel/virtual |
| Publishing and tracking | Final draft + account mapping + performance records |
Every persona dataset is stored as plain Markdown files. Git is the database and history ledger — all changes are version-controlled automatically.