68 lines
2.2 KiB
TypeScript
68 lines
2.2 KiB
TypeScript
import { tool } from "@opencode-ai/plugin";
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const internalBaseUrl =
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process.env.TJWATER_AGENT_INTERNAL_BASE_URL ?? "http://127.0.0.1:8787";
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const internalToken = process.env.TJWATER_AGENT_INTERNAL_TOKEN ?? "";
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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 workflow 或方法模式。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("Why this skill maintenance action is justified for future reuse."),
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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.string().optional().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("Asset file path. For references use references/*.md; for scripts use scripts/*.py."),
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content: tool.schema
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.string()
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.optional()
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.describe("Content used by write_skill, write_reference, or write_script."),
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},
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async execute(args, context) {
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const response = await fetch(
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`${internalBaseUrl}/internal/tools/skill-manager`,
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{
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method: "POST",
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headers: {
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"Content-Type": "application/json",
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"x-agent-internal-token": internalToken,
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},
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body: JSON.stringify({
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...args,
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session_id: context.sessionID,
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}),
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},
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);
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const text = await response.text();
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if (!response.ok) {
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throw new Error(text);
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}
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return text;
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},
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});
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