skills/brand-persona-skill/SKILL.md
Distill any commercial entity into a personalized brand agent — a living brand persona with authentic voice, declared service capabilities, and a standard service contract. Every commercial entity has a brand: a name, a style, a way of showing up in the world. This skill exists so that a street vendor, a family clinic, and a global chain can all have their own agent on equal footing. Supports both distillation from existing brand content and declaration from scratch.
npx skillsauth add acnlabs/openpersona brand-persona-skillInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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brand-persona-skill is an orchestration skill for turning any commercial entity into a personalized brand agent. It distills brand identity from existing content (or guides a declaration from scratch), defines the brand's service capabilities as skills, and generates a full OpenPersona pack that external agents can discover and call.
This is not a tool for creating an agent about a brand. It generates the brand itself as an agent.
Dependency chain:
brand-persona-skill → skills/anyone-skill (Phase 1A only — required when distilling from existing content; not needed for Path B declaration)brand-persona-skill → skills/open-persona (Phase 4 — persona pack generation, both paths)persona-knowledge is transparently integrated via anyone-skill when installed — no extra work neededGenerated output: a self-contained {slug}-skill/ persona pack with brand soul, declared service skills, agent-card for A2A discovery, and a service contract for external agents.
/create-brand-agentpresets/commercial-base/persona.jsonskills/brand-persona-skill/assets/brand.persona.template.jsonskills/brand-persona-skill/references/SERVICE-CONTRACT.template.md./{slug}-skill/Ask the user:
Does your brand have any of the following existing content?
[A] Yes → brand guidelines / customer service records / website copy /
founder interviews / marketing materials / social media archives
(any format: .md / .txt / .pdf / chat exports / .json)
[B] No or very little → declare brand parameters directly
If the user selects [A], proceed to Phase 1A. If the user selects [B], proceed to Phase 1B.
Load skills/anyone-skill/SKILL.md and follow its instructions.
When anyone-skill asks "Who do you want to distill?", select [6] Archetype — composite persona with no single real-world subject. This is the correct type for a brand persona.
Brand content → anyone-skill data type mapping (use this to guide the user on what to provide):
| Brand content | anyone-skill data type |
| --------------------------------------------- | ------------------------------------ |
| Brand guidelines / VI spec / brand manual | .md / .pdf → universal adapter |
| Customer service records / sales scripts | Chat export → chat_export adapter |
| Website copy / WeChat public account articles | .txt / .md → universal adapter |
| Founder interviews / brand story | .txt → universal adapter |
| Social media archives (X/Instagram) | Archive directory → social adapter |
| Product catalog / FAQ document | .md / .pdf → universal adapter |
Note: if skills/persona-knowledge/SKILL.md is present, anyone-skill will automatically route brand knowledge into the MemPalace persistent store. No extra steps needed.
Output: a structured brand persona draft with four dimensions extracted:
Collect this output and proceed to Phase 2.
Collect the following from the user, one question at a time:
jinguyuan-dumplings)soul.identity.bio)Optional: use WebSearch to research competitors or industry tone references if the user needs inspiration.
Output: structured brand soul fields. Proceed to Phase 2.
Services are skills. Every service capability the brand agent offers maps directly to a skills[] entry in persona.json.
Ask the user the following questions (Questions 1–3 are required; Question 4 is only asked if any service in Question 2 uses A2A delegate):
List every service the agent can handle without human involvement. Think in two categories:
For each service declared above, the implementation method is transparent to this skill — the brand chooses based on what they have:
| Implementation | When to use | Example | | ------------------ | ---------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------- | | Knowledge response | Information queries, no system integration | Business hours, product descriptions, policies | | Local script | Specific business operations | Queue number generation, appointment booking | | MCP tool | Has own service endpoint | Order lookup, inventory check | | A2A protocol | Agent-to-agent collaboration | Cross-system business flows | | Webhook / API | Has existing ERP / CRM / payment system | Order management, customer data | | Human handoff | High-risk or complex situations | Complaints, contracts, disputes | | A2A delegate | Operation belongs to a third-party platform that has its own brand agent | F&B: queue via Meituan agent; Hotel: booking via Ctrip agent; Fitness: class sign-up via booking platform agent | | Mixed | Most real businesses | Auto-answer queries + escalate transactions |
Record the implementation method in the skill's description field.
A2A delegate requires collecting three extra fields:
tbd."shop_id: 4211342 for a restaurant branch; hotel_id: bj-sanlitun-001 for a hotel property; clinic_id: 88 + doctor_id: 204 for a clinic) — they will be written into the trigger mapping table in Step 4c.meituan-queue for Meituan queue operations, ctrip-booking for Ctrip hotel reservations, wechat-health-booking for healthcare appointments." This enables a three-tier execution model written into the service contract:capability_needed skill installed → execute directly using the routing parameters provided by this brand agentRecord all three fields. They will be written into both the trigger mapping table (Step 4c) and the SERVICE-CONTRACT routing table (Step 4d).
List hard limits and escalation triggers:
Summarise all A2A delegate entries collected above into a routing table for confirmation before proceeding. For each delegated service, verify the three fields collected in Question 2 are complete:
| Service | Routing parameters | Client-side capability | Third-party agent ACN | Fallback | | ------------------------- | ------------------------ | ---------------------------- | --------------------- | -------------------------------- | | {service collected in Q2} | {params collected in Q2} | {capability collected in Q2} | {ACN slug or "tbd"} | {platform link or human channel} |
Ask the user to confirm or correct any row. Once confirmed, this table is written verbatim into SERVICE-CONTRACT.md in Phase 4.
Output mapping:
skills[] with name, description, and optional triggersoul.character.boundariesMerge Phase 1 brand soul and Phase 2 service skills into a complete persona.json using presets/commercial-base as the base. Apply this field mapping:
| Source | Target field |
| ------------------------------------- | ------------------------------------------------------------------------------------------ |
| Phase 1A/1B brand name | soul.identity.personaName |
| Phase 1A/1B slug | soul.identity.slug |
| Phase 1A/1B one-line bio | soul.identity.bio |
| Phase 1A/1B personality keywords | soul.character.personality |
| Phase 1A/1B speaking style | soul.character.speakingStyle |
| Phase 1A/1B core values + hard limits | soul.character.boundaries (combined as a single string; list items separated by newline) |
| Phase 1A/1B background / brand story | soul.character.background (if available from distillation) |
| Phase 2 service skills list | skills[] (each entry: name, description, optional trigger) |
| Phase 2 additional hard limits | append to soul.character.boundaries |
| Base fields | all other fields from presets/commercial-base/persona.json |
Use skills/brand-persona-skill/assets/brand.persona.template.json as a writing guide. Do not copy the template file directly — it contains _comment fields that will cause schema validation errors. Write a new persona.json based on its structure, with all _comment keys removed.
Gate — Soul Gate: before proceeding, verify:
soul.identity.personaName is non-emptysoul.identity.slug matches ^[a-z0-9-]+$soul.identity.bio is non-emptysoul.character.personality is non-emptyIf any field is missing, return to Phase 1 to collect it.
Gate — Service Gate: verify:
skills[] contains at least one entry with a non-empty namesoul.character.boundaries is non-emptyIf either check fails, return to Phase 2.
Load skills/open-persona/SKILL.md and follow its generation instructions.
npx openpersona create --config persona.json --output ./{slug}-skill
Gate — Generate Gate: if openpersona create exits non-zero, read the validation error, fix persona.json, and retry.
Fill skills/brand-persona-skill/assets/behavior-guide.template.md with brand-specific values and write the result to ./{slug}-skill/soul/behavior-guide.md, overwriting the framework default. Apply this mapping:
| Placeholder | Source |
| ------------------------------ | ---------------------------------------------------------------------------------------------------------------- |
| {{brandName}} | Phase 1A/1B brand name |
| {{oneLinerBio}} | Phase 1A/1B one-line bio |
| {{onboardingQuestion_1/2/3}} | 3 realistic first questions a customer would ask this brand |
| {{blindspotRedirect}} | Escalation channels declared in Phase 2 (human handoff channel, official website, customer service number, etc.) |
| {{confirmationRequiredOps}} | Action services from Phase 2 that require confirmation (list each by name) |
| {{brandVibe}} | Phase 1A/1B vibe / personality keywords |
| {{speakingStyleNote}} | Phase 1A/1B speaking style, condensed to one sentence |
| {{brandBoundaries}} | soul.character.boundaries from Phase 3 |
From the Phase 2 skills[] list, write a trigger mapping table and append it to ./{slug}-skill/SKILL.md under a new section ## Trigger Scenarios. For each declared service skill, produce one or more rows:
## Trigger Scenarios
| What the customer might ask | Corresponding operation |
|---|---|
| {realistic user phrasing for skill_1} | {skill_1 name and implementation method} |
| {realistic user phrasing for skill_2} | {skill_2 name and implementation method} |
...
Use natural customer language in the left column (questions a real customer would type, not technical names). Use the skill name + implementation method in the right column.
For A2A delegate skills, expand the right column to show all three execution tiers on separate lines. The table row format for an A2A delegate entry looks like this:
| "How long is the wait? Can you add me to the queue?" | **queue-waitlist** (A2A delegate)<br>Tier 1: install `meituan-queue` → params: shop_id `4211342`<br>Tier 2: A2A → `acn://meituan-queue-agent` (tbd)<br>Tier 3: fallback → https://meituan.com/restaurant/4211342 |
More examples by industry (same table row format):
| "I'd like to book a room for two nights" | **room-booking** (A2A delegate)<br>Tier 1: install `ctrip-booking` → params: hotel_id `bj-sanlitun-001`<br>Tier 2: A2A → `acn://ctrip-booking-agent` (tbd)<br>Tier 3: fallback → https://ctrip.com/hotels/bj-sanlitun-001 |
| "Can I book an appointment with Dr. Li?" | **appointment-booking** (A2A delegate)<br>Tier 1: install `wechat-health-booking` → params: clinic_id `88`, doctor_id `204`<br>Tier 2: A2A → `acn://wechat-health-agent` (tbd)<br>Tier 3: fallback → hospital registration portal |
The customer agent reads the three tiers and chooses the best available execution path at runtime.
Copy skills/brand-persona-skill/references/SERVICE-CONTRACT.template.md
→ ./{slug}-skill/references/SERVICE-CONTRACT.md
Fill in all {{placeholder}} values with the information collected in Phase 2.
For the Third-Party Agent Routing section, use the routing table collected in Phase 2 Question 4:
acn://{slug} and set status to activelink-fallbacktbd and fill in a human-readable fallback instructionThe routing table is the most important part of the contract for customer agents — it tells them the complete service map of this brand, including what this agent delegates and where.
Gate — Contract Gate: verify that ./{slug}-skill/agent-card.json has a non-empty skills[] array. agent-card.json is auto-generated by openpersona create — its skills[] maps directly from persona.json's skills[]. If it is empty, the root cause is an empty skills[] in Phase 3; return to Phase 2 to declare at least one service skill and regenerate.
Confirm the generated pack structure with the user:
{slug}-skill/
├── SKILL.md ← brand agent behavior rules
├── persona.json ← brand declaration
├── agent-card.json ← A2A discovery credential
├── acn-config.json ← ACN registration config
├── soul/
│ ├── injection.md ← brand soul injection
│ ├── constitution.md ← ethical foundation (inherited)
│ └── behavior-guide.md ← brand behavior guide
├── scripts/
│ └── state-sync.js ← cross-session state management
└── references/
├── SERVICE-CONTRACT.md ← service capabilities and boundaries
└── SIGNAL-PROTOCOL.md ← host integration guide
Optional — Register on ACN:
npx openpersona acn-register {slug} --endpoint https://your-agent-endpoint.example.com
This publishes the brand agent's agent-card.json to the Agent Communication Network so other agents can discover and call it without installing any brand-specific skill. The --endpoint flag is the only value the brand must supply — everything else (agent-card.json, acn-config.json) was auto-generated by openpersona create.
| Gate | On failure | | ------------- | --------------------------------------------------------------- | | Soul Gate | Return to Phase 1, collect missing fields | | Service Gate | Return to Phase 2, add at least one service skill | | Generate Gate | Read openpersona validate error, fix persona.json fields, retry | | Contract Gate | Return to Phase 2, verify skills[] is populated, regenerate |
Always ask for explicit confirmation before:
take_number, place_order, cancel_order, or any state-changing operationtools
Audit any OpenPersona (or peer LLM-agent) persona in three complementary modes: structural (CLI, deterministic, CI-friendly: 4 Layers × 5 Systemic Concepts × Constitution gate with role-aware severity), semantic white-box (LLM reads pack-content JSON and scores Soul-narrative quality via rubrics), and semantic black-box (LLM evaluates a remote agent it cannot read on disk, via A2A handshake / consent-probe / passive observation, with confidence caps). Produces quality reports with dimension scores, strengths, and actionable improvements. Use when asked to evaluate, audit, score, review, self-review, peer-review, or black-box review an agent.
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