Distill a real, historical, fictional, or privately known person from mixed source channels into a reusable skill. Combines multi-channel evidence intake with mindset, task-signature, taste-rubric, and persona extraction, then chooses the right output shape: a perspective skill, a persona skill, or a hybrid simulation skill. Use when the user wants to extract how someone thinks, structures work, chooses trade-offs, speaks, decides, and behaves across interviews, writings, chats, documents, social posts, timelines, and third-party commentary, then turn that into a new skill rather than a simple report.
Distill a real, historical, fictional, or privately known person from mixed source channels into a reusable skill. Combines multi-channel evidence intake with mindset, task-signature, taste-rubric, and persona extraction, then chooses the right output shape: a perspective skill, a persona skill, or a hybrid simulation skill. Use when the user wants to extract how someone thinks, structures work, chooses trade-offs, speaks, decides, and behaves across interviews, writings, chats, documents, social posts, timelines, and third-party commentary, then turn that into a new skill rather than a simple report.
Persona Perspective Builder
Fuse the best parts of nuwa-skill and anyone-skill without inheriting their worst constraints.
This skill is for building a new person-derived skill from evidence, not for writing a flattering summary.
It should:
intake mixed evidence from many channels
separate thinking framework, task signature, and taste rubric from surface mimicry
decide whether the result should be perspective, persona, or hybrid
produce a bounded, honest, reusable skill package
Core Thesis
Use nuwa ideas for:
mental models
decision heuristics
anti-patterns
worldview tensions
repeated task structure
repeated taste and critique patterns
Use anyone ideas for:
source intake across chats, documents, archives, and public content
interaction style
memory anchors
values and contradictions
iterative correction and update flow
Do not force every subject into full-person simulation.
Many targets only justify a strong perspective skill.
Many of the most valuable outputs in the agent era are not mimic voice, but reusable task signatures and taste rubrics.
Output Modes
Choose exactly one primary output mode.
1. perspective
Use when the main value is:
how this person thinks
how they make decisions
how they frame trade-offs
how they evaluate new problems
how they tend to structure work
what kind of solutions they consistently reward or reject
Best for:
public thinkers
founders
researchers
historical figures
thin but high-quality public evidence
Default deliverable:
[slug]-perspective/
SKILL.md
agents/openai.yaml
references/evidence_profile.md
2. persona
Use when the main value is:
how this person speaks
how they react
how they relate to others
how they carry memory, tone, and interpersonal style
Best for:
private or user-supplied archives
rich chat logs
letters, diaries, emails
user-known people with strong behavioral traces
Default deliverable:
[slug]-persona/
SKILL.md
agents/openai.yaml
references/persona_profile.md
3. hybrid
Use when both are strong:
the target has stable thinking patterns
the target also has enough evidence for believable interaction style
Best for:
well-documented public figures with long-form interviews
personal archives plus reflective writing
targets where the user explicitly wants both advisor value and character fidelity
Default deliverable:
[slug]-hybrid/
SKILL.md
agents/openai.yaml
references/evidence_profile.md
references/persona_profile.md
Input Contract
Require only:
who the subject is
what the user wants the resulting skill to do
Infer when possible:
subject type
likely source channels
best output mode
whether the goal is advisory, simulation, or both
If the material is really a generic method, evaluator, or playbook with a person wrapper, route to source-to-skill instead of forcing a person-derived build.
Ask follow-up questions only when one of these changes the result:
private vs public use
availability of private archives
whether distribution is intended
whether the user wants a perspective, persona, or hybrid result
the user wants a person-derived advisor, perspective, persona, or hybrid result
the extracted task signatures and taste should stay attached to that person-shaped lens
Route to source-to-skill when:
the bundle is really teaching a generic method, playbook, evaluator, or router
task structure and taste matter more than personhood
the person is mainly a carrier for a reusable workflow
evidence is too weak for a person-derived result but strong enough for operational extraction
When routing, pass forward:
thinking_frameworks
task_signatures
taste_rubrics
operator_rules
exemplars_and_anti_exemplars
Workflow
1. Frame the Subject
Classify the target:
self
someone-known
public-figure
historical-figure
fictional-character
archetype
Then classify the user's intent:
advisor
simulation
both
Do not proceed as if the target is public-safe if the user is supplying private material.
2. Build the Evidence Map
Normalize every source into an evidence ledger with:
source_id
channel
publicness
time_span
subject_distance
confidence_level
perspective_value
task_value
taste_value
persona_value
Recommended channels:
writings and long-form essays
interviews and podcasts
short social posts
chat exports
email or document archives
biographies and external commentary
decision records and major actions
timeline and turning points
Score each source four ways:
perspective_value: how much it reveals thought structure
task_value: how much it reveals preferred task shape and workflow behavior
taste_value: how much it reveals quality bar, trade-off logic, and anti-slop judgment
persona_value: how much it reveals interaction and behavior
Use these rough heuristics:
essays, books, talks -> high perspective
demos, code reviews, design critiques, planning artifacts -> high task and taste
chats, emails, DMs -> high persona
interviews -> medium-high for both
interviews about projects or decisions -> medium-high task and taste
social posts -> medium persona, medium perspective only if repeated patterns appear
third-party commentary -> low-medium, never primary if first-party evidence exists
3. Choose the Build Shape
Use this decision table:
Evidence shape
Best mode
Mostly public, reflective, idea-rich
perspective
Mostly private, conversational, behavior-rich
persona
Strong first-person reflection plus strong interaction traces
hybrid
If the evidence is wide but shallow, degrade gracefully to perspective.
Do not claim full-person simulation without strong persona evidence.
4. Run Multi-Lane Extraction
Run all lanes even if one will later be downweighted.
Pass A: Cognitive Extraction
Extract:
3-7 mental models
5-10 decision heuristics
recurring trade-off logic
anti-patterns and refusal rules
contradictions worth preserving
This is the nuwa-style core.
Pass B: Task Extraction
Extract:
recurring task shapes
preferred task grain
favored starting point
done conditions
delegation or escalation rules
favored interfaces, tools, or surfaces
recurring transformation from vague ask to executable unit
This is the tasky lane.
Pass C: Taste Extraction
Extract:
what this person consistently rewards
what they consistently reject
repeated trade-off preferences
signals of elegance, durability, or overbuilding
examples and anti-examples they use to teach judgment
likely operator rules another agent could reuse
This is the taste lane.
Pass D: Persona Extraction
Extract:
vocabulary and sentence habits
emotional temperature
conflict style
humor and softness/harshness
memory anchors
pride, fear, fixation, avoidance
core values and prohibitions
This is the anyone-style core.
5. Grade the Evidence
Every non-trivial claim should be tagged as:
L1 direct quote or direct artifact
L2 strong paraphrase or well-sourced report
L3 inference from multiple signals
L4 inspired extrapolation
Higher-stakes traits need stronger evidence:
voice style may tolerate some L3
non-negotiable values should prefer L1-L2
hard prohibitions should almost never rely only on L4
task signatures should prefer repeated observed choices, not one project
taste rubrics should prefer repeated critiques, comparisons, or trade-off decisions
If evidence conflicts, preserve the tension.
Do not smooth contradictions into fake coherence.
6. Build the Skill Packet
Produce a packet with:
subject_frame
source_inventory
chosen_mode
durable_units
honesty_boundaries
target_skill_shape
durable_units should separate:
thinking_frameworks
task_signatures
taste_rubrics
operator_rules
interaction_patterns
memory_anchors
values_and_red_lines
failure_modes
7. Generate the New Skill
Create the generated skill directory for the subject.
Default shape:
SKILL.md
agents/openai.yaml
references/*.md only when needed
The generated SKILL.md should include:
clear trigger text
what the skill is for
what it must not pretend to know
how it should answer new questions
how to express uncertainty
For perspective mode:
optimize for advice, analysis, decision framing, task shaping, and critique
do not over-index on mimic voice
expose stable operator rules when they are genuinely evidenced
For persona mode:
optimize for interaction fidelity and boundaries
do not overclaim worldview depth when evidence is mostly conversational
For hybrid mode:
make thinking core primary
make voice and behavior a controlled overlay
if the two conflict, preserve the cognitive core over surface mimicry
8. Validate Before Delivery
Run at least these checks:
known-stance test
Compare 3 generated answers against known subject positions.
new-problem test
Ask one novel question and confirm the skill answers from extracted principles, task signatures, and taste rubrics rather than generic filler.
boundary test
Ask beyond the evidence and confirm the skill says what is unknown.
mode-fit test
Confirm the chosen perspective/persona/hybrid mode matches the actual evidence mix.
task-fit test
Ask the skill to structure a new task and verify the output reflects the extracted task grain, starting point, and done condition.
taste-fit test
Ask the skill to compare two options and verify the judgment uses repeated trade-off signals rather than vague aesthetics.
If the validation fails:
reduce scope
downgrade hybrid -> perspective or persona -> perspective
mark weak areas explicitly
Guardrails
do not confuse voice cloning with person understanding
do not grant hybrid status unless both lanes are genuinely evidenced
do not use sparse social posts as the whole person
do not treat biography as behavior
do not build deception tools for impersonation, harassment, or manipulation
do not present public-figure simulations as the real person
do not turn weak inference into strong canon
Honesty Boundaries
Always include these in the generated skill when relevant:
this is evidence-shaped, not the real person
public evidence has blind spots
private archives still do not expose the whole interior self
changed positions over time should be preserved with dates when possible
Recommended Defaults
If building for public thinkers:
prioritize perspective
use long-form writing, interviews, decisions, and criticism
pay special attention to task-shaping and taste signals in project choices, reviews, and demos
If building for people known personally:
prioritize persona or hybrid
use chat logs, emails, notes, and user-supplied memories
If building for fictional characters:
use persona
switch to inspired-by mode when distribution is intended
Example Requests
Use $persona-perspective-builder to turn this founder's interviews, essays, and tweets into a perspective skill.
Use $persona-perspective-builder to build a hybrid skill from these chats, emails, and voice notes about my former collaborator.
Use $persona-perspective-builder to decide whether this target deserves a perspective skill or a full persona skill before generating anything.
Use $persona-perspective-builder to fuse public research and private archives, then create the best-fit person-derived skill.
Use $persona-perspective-builder to extract how this person structures work and what kinds of solutions they consistently reward or reject.
Use $persona-perspective-builder to decide whether this bundle should stay person-derived or route to $source-to-skill because the reusable method matters more than the person.
