Move project and host queries into native MCP with host-owned OAuth/context, while retaining memory, skills, actions and publication plugins. Add nearby-model queries and explicit chat/task/learning model defaults. Adopt Responses HTTP/SSE and native steer/queue inbox delivery, preserving scoped session ownership, pending input edits and investigation communication. Disable local-provider discovery: Server /health allowed native vLLM to probe /v1/models on port 8000. A native before/after regression prevents recurrence. Validation: 170 focused Bun tests; service/plugin typechecks; native discovery, provider-discovery and input-inbox smoke checks; no paid model inference.
64 lines
2.1 KiB
TypeScript
64 lines
2.1 KiB
TypeScript
import { tool } from "../tool.js";
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import { getInternalBridgeConfig } from "../internalBridge.js";
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export default tool({
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description:
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"维护已验证、可复用、非敏感的通用执行方法。事件业务处置 Skill 由 Server 经任务 MCP 分发,不在此复制或覆盖。支持 list、write_skill、remove_skill、append_pattern、remove_pattern、write_reference、remove_reference、write_script、remove_script。",
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args: {
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action: tool.schema
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.enum([
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"list",
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"write_skill",
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"remove_skill",
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"append_pattern",
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"remove_pattern",
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"write_reference",
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"remove_reference",
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"write_script",
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"remove_script",
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])
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.describe("Skill maintenance operation."),
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reason: tool.schema
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.string()
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.describe(
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"Why this skill maintenance action is justified for future reuse.",
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),
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skill_path: tool.schema
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.string()
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.describe(
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"Target skill directory path relative to .opencode/skills. Use 'workflow' for the workflow index, or '__root__' for the root skills index.",
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),
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pattern: tool.schema
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.string()
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.optional()
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.describe("Pattern text used by append_pattern."),
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target_id: tool.schema
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.string()
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.optional()
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.describe("Stable learned pattern id used by remove_pattern."),
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file_path: tool.schema
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.string()
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.optional()
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.describe(
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"Asset file path. For references use references/*.md; for scripts use scripts/*.py.",
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),
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content: tool.schema
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.string()
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.optional()
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.describe(
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"Content used by write_skill, write_reference, or write_script.",
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),
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},
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async execute(args, context) {
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const { baseUrl, internalToken } = getInternalBridgeConfig();
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const response = await fetch(`${baseUrl}/internal/tools/skill-manager`, {
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method: "POST",
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headers: { "Content-Type": "application/json", "x-agent-internal-token": internalToken },
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body: JSON.stringify({ session_id: context.sessionID, input: args }),
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});
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const text = await response.text();
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if (!response.ok) throw new Error(`learning tool failed (${response.status}): ${text}`);
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return text;
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},
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});
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