Agent skill

Reachai Onboarding

by w8123 in w8123/EnterpriseAgentFramework

Integrate Java business systems with ReachAI SDK registration, SDK instance heartbeat, gateway/embed access, and optional API Management handoff.

MITAuto-check passedAI & LLM Engineering

Install Reachai Onboarding

skills CLI
$ npx skills add w8123/EnterpriseAgentFramework --skill reachai-onboarding -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install w8123/EnterpriseAgentFramework reachai-onboarding --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/w8123/EnterpriseAgentFramework.git skills-src && mkdir -p .claude/skills && cp -r skills-src/reachai-control-service/src/main/resources/ai-assist/skills/reachai-onboarding .claude/skills/reachai-onboarding && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
reachai-onboarding
GitHub stars
865
Token cost
~6.1k tokens
SKILL.md length
2,892 words
Files
34 (incl. scripts, references)
Skills in repo
3
Repo updated
First seen
Licence
MIT

At a glance

Integrate Java business systems with ReachAI SDK registration, SDK instance heartbeat, gateway/embed access, and optional API Management handoff.

  • Works in 12 steps: If the prompt is a ReachAI task handoff,… → Download this skill package if it is not… → Detect the project layout → …
  • Asked to connect a Spring Boot service to ReachAI
  • SKILL.md covers Operating Rules, Workflow, References and Output Contract
  • Runs Java scripts from its folder; calls npm and node; needs REACHAI_REGISTRY_APP_SECRET and REACHAI_AI_CODING_KEY

What it does

Reachai Onboarding is an agent skill from w8123/EnterpriseAgentFramework. Integrate Java business systems with ReachAI SDK registration, SDK instance heartbeat, gateway/embed access, and optional API Management handoff. Use when asked to connect a Spring Boot service to ReachAI, add reachai-capability-sdk or reachai-spring-boot2-starter, configure reachai.registry/reachai.project/reachai.capability, prepare @ReachCapability metadata for later manual SDK sync, or verify SDK onboarding from a ReachAI manifest.

Its SKILL.md is about 6.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 41 other files, including scripts and reference files (for example `agents/openai.yaml`, `artifacts/manifest-artifact.json` and `examples/gateway/README.md`).

It sits in AI & LLM Engineering, covering Backend development and Building AI agents. It works with Java, Spring Boot, Model Context Protocol and LangGraph. The repository describes itself as: ReachAI企业级智能体开发平台:快速、安全完成已有业务系统智能化改造,让 AI 在 OA、ERP、CRM 等原系统中查数据、填表单、办业务。ReachAI: Quickly and securely bring AI to existing enterprise systems, enabling AI to query data, fill out… The licence is MIT.

When your agent uses it

  • Asked to connect a Spring Boot service to ReachAI
  • Add reachai-capability-sdk
  • Reachai-spring-boot2-starter
  • Configure reachai.registry/reachai.project/reachai.capability

Example prompts

  • “/reachai-onboarding”

Requirements

  • Node.js
  • A credential in REACHAI_REGISTRY_APP_SECRET

Workflow steps

12 steps, taken from the first numbered list in SKILL.md.

  1. If the prompt is a ReachAI task handoff, activate the one-time code and read GET /context first. Otherwise read the explicitly supplied…
  2. Download this skill package if it is not already installed, then read the reference files only as needed.
  3. Detect the project layout
  4. Resolve and add dependencies using the manifest sdkArtifacts, references/java-sdk-access.md, and templates/pom-dependencies.xml. Platform…
  5. Add configuration using templates/application-reachai.yml. Do not add any capability startup-sync setting. Replace package placeholders…
  6. Do not scan or sync APIs at application startup. Only when the user explicitly asks to prepare API metadata, select one or two low-risk…
  7. Inspect the business gateway boundary before declaring onboarding complete
  8. Add or update the gateway route/token broker
  9. Add or update the business front-end integration
  10. Run the smallest meaningful verification commands for the touched backend, gateway, and front-end modules.
  11. Call the manifest's sdkAccessCheckUrl only after local compile/config succeeds, or explain why a live check cannot run.
  12. For a task handoff, follow protocolGuide.eventStateRules and write real STARTED / PROGRESS events to the current task. While the task is…

What it can do on your machine

Read from SKILL.md and the folder at commit 7179aab. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Java, from the files we listed), which the agent can run.

    Shell commands in SKILL.md call:

    • npm
    • node

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use npm, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • REACHAI_REGISTRY_APP_SECRET
    • REACHAI_AI_CODING_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Reachai Onboarding loads about 6.1k tokens when it runs, and up to ~27k if it reads all its reference files. Until then it costs about 115 tokens; SKILL.md has 2,892 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~115
When it runs · the whole SKILL.md, loaded when a task matches
~6.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~27k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from w8123/EnterpriseAgentFramework at commit 7179aab, republished under its MIT licence (© w8123). 2,892 words, ~6,068 tokens.

Download SKILL.mdSave it as .claude/skills/reachai-onboarding/SKILL.md (or your agent's skills folder). This skill also uses 33 other files; get the full folder from GitHub.
name
reachai-onboarding
description
Integrate Java business systems with ReachAI SDK registration, SDK instance heartbeat, gateway/embed access, and optional API Management handoff. Use when asked to connect a Spring Boot service to ReachAI, add reachai-capability-sdk or reachai-spring-boot2-starter, configure reachai.registry/reachai.project/reachai.capability, prepare @ReachCapability metadata for later manual SDK sync, or verify SDK onboarding from a ReachAI manifest.

ReachAI Onboarding

Operating Rules

Treat the current business repository as the source of truth. Inspect its Maven modules, Java version, Spring Boot version, configuration files, existing controller/service boundaries, and test commands before editing.

凡是写入 ReachAI 或展示给业务用户的名称、标题、描述、说明、System Prompt、节点名称、审计原因、进度和结果,默认使用清晰的简体中文。不要仅因 API、Schema 或字段名为英文就生成英文业务文案。Token、MCP、AI、Agent、Supervisor、Workflow、Tool、API、SDK 等熟知专业术语,以及 keySlug、toolName、代码、路径、枚举值、协议字段和技术标识可保留英文;必要时使用“中文名称(英文术语)”。不要翻译或改写技术标识。

Never paste, print, or commit the registry app secret. Use the environment variable named by the manifest, normally REACHAI_REGISTRY_APP_SECRET.

ReachAI task handoffs use a one-time activation code. Activate it once, keep the returned short-lived task token only in the current process, and call /api/ai-coding/tasks/{taskId}/** with Authorization: Bearer <taskToken>. Never reuse a project-level aiCodingKey on task protocol routes.

Separate project/Workflow AI Coding APIs under /api/ai-coding/projects/** and /api/workflows/**/ai-coding/** can still use the explicit project aiCodingKey when the user independently supplies one. Send it as X-ReachAI-AiCoding-Key; never put it in a URL, browser bundle, task artifact, or progress event.

Prefer minimal, reviewable changes:

  • Add ReachAI dependencies only to the modules that need them.
  • Put reachai-spring-boot2-starter in the runnable Spring Boot application module.
  • Put reachai-capability-sdk in modules that declare @ReachCapability methods or DTO field metadata.
  • @ReachCapability is method-level, @ReachParam is parameter/field-level, and @ReachOutput is field-only on response DTO fields. Do not put @ReachOutput on methods.
  • Do not use the ReachAI platform base URL as a Maven repository or npm registry. Manifest/skill/self-check URLs are not Maven/npm repositories.
  • Unique recommended Java SDK install (no ReachAI source checkout): read the absolute Java entries in the onboarding manifest's sdkArtifacts, expand {skillExtractDir} in each installCommandTemplate, and run reachai-capability-sdk before reachai-spring-boot2-starter. The bundled scripts/install-java-sdk.ps1 downloads the declared JAR and standalone consumer POM, verifies both declared SHA-256 values, and installs that exact coordinate into the business system's Maven local repository. Fail if a URL or hash is absent or mismatched; do not guess another URL and do not require access to the ReachAI repository.
  • Unique recommended Embed SDK install (no ReachAI source checkout): read sdkArtifacts for @reachai/embed-chat, extract this Skill zip anywhere, then run the expanded installCommandTemplate from the business frontend directory that contains package.json. The bundled scripts/install-embed-chat.mjs verifies integritySha256, copies the tgz to the stable repo-local vendor/reachai/ directory, replaces the exact installed package directory, and records .reachai-artifact-sha256. Re-run this installer whenever a SNAPSHOT artifact checksum changes; npm install --force alone does not prove that a same-version file dependency was refreshed. reachai-doctor --mode static reports EMBED_SDK_ARTIFACT_MATCH. Never run npm install directly against a temporary Skill extract path, and never leave %TEMP%, .cursor, .trae or another machine-specific absolute path in package.json / lockfiles. Authenticated downloadUrl needs auth headers that npm cannot send, so prefer this Skill-bundled installer.
  • Do not invent dependency download paths such as /repository/**, /maven/**, /repository/maven/**, /api/embed/sdk, or /npm/**. Do not use cd ai-admin-front && npm run build:sdk as the business-project install path.
  • Gateway checklist is a top-level gatewayChecklist object list on the onboarding manifest (id, description, required, verificationHint, failureImpact). See references/java-sdk-access.md.
  • Avoid changing unrelated business logic, package structure, formatting, or dependency versions.
  • SDK onboarding must not scan or sync business APIs on application startup. After compile, registration and heartbeat succeed, an active ReachAI PROJECT_ONBOARDING task may explicitly trigger exactly one audited SDK sync with POST <taskRoot>/verifications/SDK_SYNC; the task token scopes that operation to its own project. The equivalent console action remains API Management(API 管理)手动触发的 SDK 同步. Restrict both paths to business-owned packages and never include framework, platform, third-party, starter, or shared infrastructure controllers as business APIs.

Workflow

  1. If the prompt is a ReachAI task handoff, activate the one-time code and read GET <taskRoot>/context first. Otherwise read the explicitly supplied onboarding manifest URL.
  2. Download this skill package if it is not already installed, then read the reference files only as needed.
  3. Detect the project layout:
    • Maven root and child modules.
    • Java source level.
    • Spring Boot version.
    • Runnable application module.
    • Business-owned Java base packages from application classes, controllers, services, and module names, as the explicit SDK sync boundary.
    • Framework/platform packages that must be excluded from task-scoped or API Management SDK sync.
    • Existing application.yml, bootstrap.yml, profile-specific config, or config-center conventions.
    • Existing Spring Security, Sa-Token, Shiro, custom login interceptors, CSRF rules, gateway routes, and ingress/firewall boundaries that can affect the inbound SDK sync callback.
  4. Resolve and add dependencies using the manifest sdkArtifacts, references/java-sdk-access.md, and templates/pom-dependencies.xml. Platform artifact links are the default when no corporate Maven publication exists.
  5. Add configuration using templates/application-reachai.yml. Do not add any capability startup-sync setting. Replace package placeholders only when preparing the explicit SDK sync boundary. Set reachai.project.base-url to an address reachable from the ReachAI server; use localhost, 127.0.0.1, or ::1 only when ReachAI and the business service actually share the same host or network namespace.
  6. Do not scan or sync APIs at application startup. Only when the user explicitly asks to prepare API metadata, select one or two low-risk query-style business methods and annotate them with @ReachCapability / @ReachParam. Use templates/reach-capability-example.java only as a style example.
  7. Inspect the business gateway boundary before declaring onboarding complete:
    • Spring Cloud Gateway, Nginx, backend-for-frontend, or front-end dev proxy configuration.
    • Existing authentication headers and current-user extraction.
    • Whether a server-side token broker already exists.
    • Whether ReachAI can send POST /reachai/registry/capabilities/sync to the Starter service through the configured base-url and context-path.
  8. Add or update the gateway route/token broker:
    • Route ReachAI capability traffic to the business service and preserve X-ReachAI-Invocation-Token, X-ReachAI-Trace-Id, X-ReachAI-Run-Id, and the business identity headers required by the service.
    • If reachai.project.base-url points to a gateway or ingress, route /reachai/registry/** to the business service that contains reachai-spring-boot2-starter. Preserve X-ReachAI-App-Key, X-ReachAI-Timestamp, X-ReachAI-Nonce, and X-ReachAI-Signature.
    • Let POST /reachai/registry/capabilities/sync bypass normal business login/JWT filters and CSRF so the request reaches the Starter controller. Apply the equivalent exclusion for Spring Security, Sa-Token, Shiro, or custom interceptors. Do not remove authentication from the endpoint: the Starter must still validate the ReachAI registry signature and return 401 for invalid requests.
    • Treat this callback as server-to-server traffic. CORS is irrelevant; restrict network exposure to the ReachAI service or trusted network when infrastructure supports it.
    • Expose a front-end token endpoint such as /api/reachai/embed-token.
    • Implement the token endpoint server-side with the Starter-provided ReachAiEmbedTokenClient. Business code maps the current authenticated user to ReachAiEmbedPrincipal and forwards the SDK-owned page identity; the client owns project signing, transport, and wrapped data.token parsing.
    • Keep /api/reachai/embed-token on the normal business login token path. It reads the current business user and exchanges that identity for a ReachAI embed token.
    • Add the gateway authentication whitelist or dedicated security chain for /api/reachai/embed/**. This path carries ReachAI embed tokens, so business OAuth/JWT filters must not validate it as a business login token; forward Authorization: Bearer <embedToken> unchanged to ReachAI.
    • In Spring Security WebFlux / OAuth2 Resource Server, permitAll() on /api/reachai/embed/** is not enough by itself: the resource server can still try to authenticate the Bearer <embedToken> before routing and return 401. Add a higher-priority SecurityWebFilterChain with securityMatcher(ServerWebExchangeMatchers.pathMatchers("/api/reachai/embed/**")) that permits all and does not enable oauth2ResourceServer() for that matcher.
    • Inspect whitelist/anonymous filters that remove or rewrite JWT headers, such as IgnoreUrlsRemoveJwtFilter, RemoveJwtFilter, RemoveRequestHeader=Authorization, or security filters that call mutate().header("Authorization", ""). Do not apply that header-clearing behavior to /api/reachai/embed/**; skipping business authentication must still preserve the embed token Authorization header.
    • If Spring Cloud Gateway proxies /api/reachai/embed/**, dedupe duplicate CORS response headers when both the gateway and ReachAI write them. A typical route filter is DedupeResponseHeader=Access-Control-Allow-Origin Access-Control-Allow-Credentials, RETAIN_FIRST.
    • For a task handoff, use domainContext.implementationGuidance.projectCopilotKeySlug as the front-end agentId; ReachAI Control owns idempotent Agent/Supervisor provisioning before handoff. Do not request a project key from the user or call project-key provisioning APIs from the task.
    • For an independently authenticated manifest flow, manifest.agentProvisioning.provisionAgentUrl remains available to an AI coding tool, local shell, or server-side integration. It is idempotent and creates or reuses the project page copilot Agent, selects an active LLM model, and publishes an ACTIVE AgentScope Supervisor config. It does not create a placeholder Workflow.
    • When the user asks for another project Agent, Supervisor changes, or Skill binding, download manifest.agentSupervisor.endpoints.skillPackageUrl and follow the agent-ai-coding Skill. Use only the project-key endpoints in that manifest; never call console /api/agents/** or /api/skills/** with an AI Coding key.
    • Write only the supplied project copilot key slug into browser configuration. Never write an internal Agent id or any credential.
    • Create Workflow drafts only for real business capabilities. Validate and publish each Workflow before adding it through manifest.agentSupervisor.endpoints.workflowToolAttachUrlTemplate; the attach operation publishes the next Agent config version containing that Workflow-as-Tool.
    • Workflow attachment is additive by default because one page may legitimately expose several independent tools. To supersede one predecessor, first read the currently attached catalog and send its exact id as replaceWorkflowId; ReachAI removes only that entry and preserves every other Workflow. Never infer replacement from pageKey, name, or display order.
    • If a project needs both page operations and explicit API-only queries, publish and attach two tools: a PAGE_ASSISTANT for page behavior and a read-only GENERAL Workflow for the API chain. Agent risk is declared per attached Workflow, not per branch inside one mixed graph.
    • Do not call provisioning from browser runtime code, and do not expose aiCodingKey to the business front end.
    • Do not ask the business user to manually create, choose, or configure the page copilot Agent during SDK onboarding.
    • Treat that Agent as the single embedded page copilot entry. AgentScope Supervisor uses the conversation plus page context to select zero, one, or multiple published Workflows from the Agent config's Workflow-as-Tool allow-list.
    • Never move appSecret into browser code.
  9. Add or update the business front-end integration:
    • Add the ReachAI chat/embed entry in a real business page or shared shell, not only in documentation.
    • Mount the launcher only after the authenticated application shell is ready. Do not initialize it on login, logout, silent-refresh, OAuth callback, or public routes. If authentication is lost, destroy the chat client before redirecting; an unauthenticated Token Broker request from an auth page is a defect, not runtime proof.
    • Do not call manifest.agentProvisioning.provisionAgentUrl from browser runtime code. Use the already provisioned bare JSON agent.keySlug as agentId (not data.agent.keySlug).
    • Use @reachai/embed-chat for browser embedding when available. Configure apiBase as the ReachAI platform origin by default; if the browser uses a gateway prefix, set embedPathPrefix such as /api/reachai/embed, or set apiBase directly to a recognized embed root such as /api/reachai/embed.
    • Configure projectCode, agentId, and a tokenProvider that calls the business gateway token broker. Use the already provisioned page copilot Agent keySlug for agentId.
    • Read pageKey, pageInstanceId, route, and origin from the @reachai/embed-chat tokenProvider context and forward them unchanged through the business token broker. The SDK reuses the same Page Bridge identity for Chat Session creation and page actions.
    • For an SPA whose Page Workflow can target another registered page, configure the documented createEafPageBridge({ onNavigate }) adapter. Validate the requested target against the business route registry, use the normal router to navigate, then call chat.rebindPage({ bridge, page }) from the target page only after its actions are registered. Do not register or hard-code the reserved navigation action key, session id, or navigation request id; the public SDK owns those details.
    • Never generate a fallback pageInstanceId in the token provider or business broker. A replacement UUID may make token exchange pass while causing Chat Session or Page Action identity mismatch.
    • Import @reachai/embed-chat/style.css, mount one visible global launcher, and keep the SDK's visible-first Token state: Token Broker pending/failure must remain visible and retryable instead of being replaced by a business-side hidden failure.
    • Do not reuse the business login token for ReachAI chat session or message calls. Use the broker-returned short-lived embed token for /api/reachai/embed/**, /api/embed/chat/sessions, and message APIs.
    • Chat message calls must use POST /api/embed/chat/sessions/{sessionId}/messages or the /messages/stream variant with body { "message": "..." }.
    • Do not send ReachAI chat requests as { "content": "..." }, { "text": "..." }, or { "question": "..." }; map any business UI field to message at the ReachAI API boundary.
    • Chat responses are wrapped ApiResult objects. Top-level code/message describe transport status only; never render top-level message: "success" as the assistant reply. Render data.answer first, with old-shape fallback only under data.reply, data.message, or data.content.
    • Embed SSE ends with message.completed; there is no done event.
    • See references/platform-apis.md for ApiResult vs bare JSON response shapes and apiBase rules.
    • Treat data.metadata.pageActionQueue as the preferred UI/Page Action queue. Treat data.uiRequest and data.uiRequest.extension.pageActionRequest as compatible single-action instructions. Execute them through the page bridge and report each request id back to /api/embed/chat/sessions/{sessionId}/page-actions/{requestId}/result; do not only render data.answer.
    • When the selected business page needs Page Actions, read references/page-action-contract.md. For Angular, reuse references/angular-page-action.md and templates/angular/ rather than inventing a second bridge protocol.
    • The optional helper can scaffold the Angular bridge without overwriting existing files: powershell -File scripts/reachai-page-actions.ps1 -Mode scaffold-angular -FrontendRoot <frontend> -PageKey <pageKey>. Adapt the generated registry example to the page's real component methods. Scaffolding is not runtime verification.
    • Run static alignment with powershell -File scripts/reachai-page-actions.ps1 -Mode verify-static -FrontendRoot <frontend> -PageKey <pageKey> -ActionKeys <keys>. A static PASS proves only source alignment; use an authenticated browser and a fresh Embed session before reporting runtime PASS. Use only an existing authorized business-system test session or account supplied outside ReachAI. If none is available, keep browserVerification null and report browser acceptance as NOT RUN; never fabricate evidence or place login credentials in task artifacts.
    • Cache embed tokens only until before their expiresIn boundary. If a session or message request returns embed token is expired, clear the cached embed token, call the broker again, and retry once.
  10. Run the smallest meaningful verification commands for the touched backend, gateway, and front-end modules.
  11. Call the manifest's sdkAccessCheckUrl only after local compile/config succeeds, or explain why a live check cannot run.
Show full SKILL.md (694 more words)Show less
  • Interpret the platform SDK self-check separately: CODE_READY requires observed Starter registration, RUNTIME_READY requires a fresh instance heartbeat, and SDK_CALLBACK_READY requires a successful signed callback plus a received capability snapshot.
  • For an active task handoff, after CODE_READY and RUNTIME_READY are observed, explicitly call POST <taskRoot>/verifications/SDK_SYNC with the task Bearer token. No body is required. This operation is project-scoped, succeeds only for a RUNNING onboarding task, and writes a verification event back to the task.
  • For a non-task manifest flow, use the API Management manual SDK sync action; do not invent project-key or public trigger endpoints.
  • The SDK self-check does not prove browser acceptance. Task readiness E2E_READY remains pending until ReachAI observes an authorized Embed session, user message and assistant reply created after the current task started. The session may come from the real browser SDK or from reachai-doctor --mode e2e using a business-supplied test Authorization/Cookie; doctor never mints or mocks the business identity. This proves only the authorized conversation protocol: declared Workflow capability nodes and Page Actions need their own exact-Trace / real-browser evidence in the Page Workbench. Final launcher visibility and interaction quality remain user acceptance items.
  • A computed SDK sync callback target is not proof of connectivity. The actual task-scoped or API Management sync must distinguish unreachable host/timeout, business-auth or CSRF 401/403, route/context-path 404/405, and Starter signature rejection.
  1. For a task handoff, follow protocolGuide.eventStateRules and write real STARTED / PROGRESS events to the current task. While the task is WAITING_USER, you may report work that does not depend on the answer with PROGRESS; it preserves WAITING_USER and every open question. Submit blocking ambiguities through /questions, poll for the user's answer, then write RESUMED only after all answers have been read. Before submission, run the Bootstrap-provided Test-ReachAiArtifact -Content <artifact-content> local schema check; Send-ReachAiArtifact runs the same check again before posting. Finish by submitting exactly one artifact matching the JSON Schema in task context; never invent success evidence.
  2. Report changed files, commands run, results, SDK sync verification status, the observed SDK sync callback target, its route/login/CSRF handling, optional scan package choices, gateway route/token broker status, front-end integration status, and whether the user-scoped secret still needs to be configured.

References

  • For exact Java SDK signatures, read references/java-sdk-api-reference.md; then use references/java-sdk-access.md for placement, configuration and runtime boundaries.
  • For platform API contracts, read references/platform-apis.md.
  • For browser SDK public types and the minimal integration, read references/embed-chat-quick-reference.md.
  • For Spring Cloud Gateway, Nginx and Kong authentication boundaries, read references/gateway-examples.md and reuse the copy-ready files under examples/gateway/.
  • For credential handling and prompt safety, read references/security.md.
  • For Page Bridge and action safety, read references/page-action-contract.md.
  • For Angular Page Action integration, read references/angular-page-action.md.
  • For ready-to-copy snippets, use files under templates/.
  • For an optional local verification helper, run scripts/verify-reachai-access.py.
  • For layered static/runtime onboarding diagnostics, run node scripts/reachai-doctor.mjs --mode static --business-root <repo> and then node scripts/reachai-doctor.mjs --mode runtime --manifest-url <onboardingManifestUrl> after services are available. For /api/ai-coding/projects/**, put the project AI Coding key in the current process environment variable REACHAI_AI_CODING_KEY; never pass the key as a command-line argument. Use --ai-coding-key-env <name> only when the repository already uses another secret environment variable name.
  • For non-interactive authorized conversation verification, let the business system provision a least-privilege test account/token outside ReachAI. Put the complete business Authorization value in REACHAI_E2E_AUTHORIZATION or the business Cookie header in REACHAI_E2E_COOKIE, then run node scripts/reachai-doctor.mjs --mode e2e --broker-url <business-origin>/api/reachai/embed-token --embed-api-base <business-origin>/api/reachai/embed --agent-id <provisioned-key-slug> --page-key <page-key> --route <route>. Never put authorization values on the command line or into task events/artifacts. EMBED_CONVERSATION_E2E=PASS proves only the authorized broker/proxy/session/message protocol; doctor deliberately leaves WORKFLOW_CAPABILITY_E2E and PAGE_ACTION_BROWSER_E2E as PENDING until real intent and real browser evidence are available. It does not prove launcher visibility.
  • For a prompt-only hidden secret setup, run scripts/set-reachai-registry-secret.ps1. It stores the value in the current Windows user environment and never prints it; start a new terminal/process before launching the business service.
  • For optional Angular Page Action scaffolding/static alignment, run scripts/reachai-page-actions.ps1.

Output Contract

End with:

  • Files changed.
  • Dependency/configuration summary.
  • Gateway route and embed token broker summary.
  • Front-end embed/chat integration summary.
  • SDK sync verification/API Management handoff status and any optional capability annotations prepared.
  • Verification commands and results.
  • Whether REACHAI_REGISTRY_APP_SECRET still needs to be configured outside the repository.
  • Task id, whether progress/questions were written back, and the submitted artifact key.

© w8123, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 33 other files (scripts, references) in reachai-control-service/src/main/resources/ai-assist/skills/reachai-onboarding of w8123/EnterpriseAgentFramework.

  • SKILL.md
  • agents/openai.yaml
  • artifacts/manifest-artifact.json
  • artifacts/reachai-embed-chat-1.0.0-SNAPSHOT.tgz
  • examples/gateway/README.md
  • examples/gateway/kong/kong.yml
  • examples/gateway/nginx/reachai.conf
  • examples/gateway/spring-cloud-gateway/ReachAiEmbedProxySecurity.java
  • examples/gateway/spring-cloud-gateway/application-reachai-gateway.yml
  • references/angular-page-action.md
  • references/embed-chat-quick-reference.md
  • references/gateway-examples.md
  • references/java-sdk-access.md
  • … and 21 more

Open the folder on GitHubat commit 7179aab

Compare with similar skills

Reachai Onboarding next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Reachai Onboarding compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Reachai Onboarding this skillw8123/EnterpriseAgentFramework865—~6.1kAutomated safety check: PassMIT
Agent Prompt Engineeringagentailor/fullstack-langgraph-nextjs-agent132—~3.6kAutomated safety check: PassMIT
Agent Inspectrajudandigam/agent-inspect165—~424Automated safety check: PassMIT
Tool Designagentailor/fullstack-langgraph-nextjs-agent132—~3.2kAutomated safety check: PassMIT
Agent Eval Casesagentailor/fullstack-langgraph-nextjs-agent132—~5.3kAutomated safety check: PassMIT
Bootui Java Developmentjdubois/boot-ui315—~1.3kAutomated safety check: PassApache-2.0

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Questions about Reachai Onboarding

What does Reachai Onboarding do?

Integrate Java business systems with ReachAI SDK registration, SDK instance heartbeat, gateway/embed access, and optional API Management handoff. Reachai Onboarding is an agent skill from w8123/EnterpriseAgentFramework. Integrate Java business systems with ReachAI SDK registration, SDK instance heartbeat, gateway/embed access, and optional API Management handoff.

When should I use Reachai Onboarding?

Reachai Onboarding fits situations like: asked to connect a Spring Boot service to ReachAI; add reachai-capability-sdk; reachai-spring-boot2-starter; configure reachai.registry/reachai.project/reachai.capability.

How do I install Reachai Onboarding in Claude Code?

Run `npx skills add w8123/EnterpriseAgentFramework --skill reachai-onboarding -a claude-code`. Or copy the skill folder (reachai-control-service/src/main/resources/ai-assist/skills/reachai-onboarding in w8123/EnterpriseAgentFramework) into .claude/skills/reachai-onboarding in your project. Claude Code loads it when a task matches its description.

How do I install Reachai Onboarding in Codex?

Run `npx skills add w8123/EnterpriseAgentFramework --skill reachai-onboarding -a codex`. Or copy the skill folder (reachai-control-service/src/main/resources/ai-assist/skills/reachai-onboarding in w8123/EnterpriseAgentFramework) into .agents/skills/reachai-onboarding in your project. Codex loads it when a task matches its description.

Can I use Reachai Onboarding in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add w8123/EnterpriseAgentFramework --skill reachai-onboarding -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/reachai-onboarding, .gemini/skills/reachai-onboarding, .github/skills/reachai-onboarding and .opencode/skills/reachai-onboarding in your project.

What does Reachai Onboarding need to run?

Going by SKILL.md and its folder, Reachai Onboarding needs Java for the scripts in its folder, the command-line tools its instructions call (npm and node) and credentials named REACHAI_REGISTRY_APP_SECRET and REACHAI_AI_CODING_KEY. Our summary lists: Node.js; A credential in REACHAI_REGISTRY_APP_SECRET.

Does Reachai Onboarding access the network?

SKILL.md contains no URLs. Its commands use npm, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Reachai Onboarding safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Reachai Onboarding use?

Reachai Onboarding is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Reachai Onboarding use?

About 6.1k tokens (SKILL.md is roughly 24k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 21k tokens, read only when the agent opens those files.

What are the alternatives to Reachai Onboarding?

Skills that share tags, products or a category with Reachai Onboarding: Agent Prompt Engineering (agentailor/fullstack-langgraph-nextjs-agent, 132 stars), Agent Inspect (rajudandigam/agent-inspect, 165 stars), Tool Design (agentailor/fullstack-langgraph-nextjs-agent, 132 stars) and Agent Eval Cases (agentailor/fullstack-langgraph-nextjs-agent, 132 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Reachai Onboarding?

w8123 (a GitHub user) maintains it in w8123/EnterpriseAgentFramework, which has 865 GitHub stars. The repository holds 3 skills in this directory. The repository was last updated on October 7, 2026.

Source: w8123/EnterpriseAgentFramework on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.