refactor: codebase audit cleanup — dead code, dedup, module splits
Multi-agent audit + aggressive cleanup across server/web/coder/booterm, delivered behind a DEFER discipline so none of the in-flight files were touched. Removes dead code/deps/columns, dedups server + coder helpers, and splits the oversized modules (tools.ts, opencode-server.ts, sentinel-summaries, turn.ts, TerminalPane.tsx) behind stable contracts. Adds 78 parity/unit tests (server 587, coder 323); fixes two latent bugs (ChatPane queue keys, FileViewerOverlay blank-line parity). Intended tag: v2.7.12-audit-cleanup. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
This commit is contained in:
216
apps/server/src/services/tools/misc-tools.ts
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216
apps/server/src/services/tools/misc-tools.ts
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import { z } from 'zod';
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import { getGitMeta } from '../git_meta.js';
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import { findSkills, getSkillBody, getSkillResource } from '../skills.js';
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import type { ToolDef } from './types.js';
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// v1.8 Level 1 branch awareness: gives the model a read-only view of the
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// project's git state. No path input — operates on the inference-resolved
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// project root via getGitMeta. Subprocess runs with a 2s timeout (see git_meta).
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const GitStatusInput = z.object({}).strict();
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type GitStatusInputT = z.infer<typeof GitStatusInput>;
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export const gitStatus: ToolDef<GitStatusInputT> = {
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name: 'git_status',
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description:
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"Returns the current git branch, whether the working tree is dirty, and ahead/behind counts vs upstream. Read-only. Use when you need to know which branch the user is currently working on.",
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inputSchema: GitStatusInput,
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jsonSchema: {
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type: 'function',
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function: {
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name: 'git_status',
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description:
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'Returns the current git branch, dirty flag, and ahead/behind counts vs upstream. Read-only.',
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parameters: {
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type: 'object',
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properties: {},
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additionalProperties: false,
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},
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},
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},
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async execute(_input, projectRoot) {
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const meta = await getGitMeta(projectRoot);
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if (meta === null) {
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return { repo: false, branch: null, is_dirty: false, ahead: 0, behind: 0 };
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}
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return { repo: true, ...meta };
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},
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};
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// Batch 9.6: skill_find, skill_use, skill_resource. Lazy-loaded markdown
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// playbooks at /data/skills/. Three tools rather than one to keep each call
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// cheap — the model lists, then loads, then optionally pulls support files.
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const SkillFindInput = z.object({
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query: z.string().optional(),
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});
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type SkillFindInputT = z.infer<typeof SkillFindInput>;
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export const skillFind: ToolDef<SkillFindInputT> = {
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name: 'skill_find',
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description:
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'Find skills (markdown playbooks under /data/skills) by name or description. Returns up to 5 matches. Empty query or "*" returns all available skills. Call this first to discover what skills are available.',
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inputSchema: SkillFindInput,
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jsonSchema: {
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type: 'function',
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function: {
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name: 'skill_find',
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description:
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'Find skills by name or description. Returns up to 5 matches. Empty or "*" returns all.',
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parameters: {
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type: 'object',
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properties: {
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query: { type: 'string', description: 'substring matched against skill name and description' },
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},
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additionalProperties: false,
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},
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},
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},
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async execute(input) {
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return await findSkills(input.query ?? '');
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},
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};
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const SkillUseInput = z.object({
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name: z.string().min(1),
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});
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type SkillUseInputT = z.infer<typeof SkillUseInput>;
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export const skillUse: ToolDef<SkillUseInputT> = {
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name: 'skill_use',
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description:
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"Load the full body of a skill's SKILL.md by name. Returns the markdown playbook to follow. Discover names via skill_find. Errors: unknown_skill.",
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inputSchema: SkillUseInput,
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jsonSchema: {
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type: 'function',
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function: {
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name: 'skill_use',
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description: "Load the full body of a skill's SKILL.md by name.",
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parameters: {
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type: 'object',
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properties: {
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name: { type: 'string', description: 'skill name from skill_find' },
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},
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required: ['name'],
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additionalProperties: false,
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},
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},
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},
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async execute(input) {
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const body = await getSkillBody(input.name);
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if (body === null) {
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return { error: 'unknown_skill', message: `unknown skill: ${input.name}` };
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}
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return { body };
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},
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};
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const SkillResourceInput = z.object({
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name: z.string().min(1),
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path: z.string().min(1),
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});
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type SkillResourceInputT = z.infer<typeof SkillResourceInput>;
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export const skillResource: ToolDef<SkillResourceInputT> = {
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name: 'skill_resource',
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description:
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"Read a support file inside a skill's folder (e.g. references/root-cause-tracing.md). Path is relative to the skill folder. Use skill_use to read SKILL.md itself. Errors: unknown_skill, unknown_resource, path_escape.",
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inputSchema: SkillResourceInput,
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jsonSchema: {
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type: 'function',
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function: {
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name: 'skill_resource',
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description: "Read a support file inside a skill's folder. Path is relative to the skill folder.",
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parameters: {
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type: 'object',
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properties: {
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name: { type: 'string', description: 'skill name' },
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path: { type: 'string', description: 'relative path under the skill folder' },
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},
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required: ['name', 'path'],
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additionalProperties: false,
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},
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},
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},
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async execute(input) {
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const result = await getSkillResource(input.name, input.path);
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if (!result.ok) {
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return { error: result.code, message: result.message };
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}
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return { content: result.content };
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},
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};
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// Batch 9.7: ask_user_input. Interactive elicitation. The model emits a tool
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// call with 1-3 structured questions; the inference loop PAUSES (does not
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// execute the tool server-side, does not recurse) and waits for the frontend
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// to POST /api/chats/:id/answer_user_input with the user's selections. See
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// routes/messages.ts for the resume path and services/inference.ts for the
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// pause branch in executeToolPhase.
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const AskUserInputInput = z.object({
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questions: z
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.array(
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z.object({
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question: z.string().min(1).max(200),
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type: z.enum(['single_select', 'multi_select']),
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options: z.array(z.string().min(1).max(80)).min(2).max(6),
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}),
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)
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.min(1)
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.max(3),
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});
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type AskUserInputInputT = z.infer<typeof AskUserInputInput>;
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export const askUserInput: ToolDef<AskUserInputInputT> = {
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name: 'ask_user_input',
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description:
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"Ask the user 1-3 structured questions through an inline picker UI. Use when you genuinely need a choice the user must make (e.g. scope, options, preferences) before continuing. Each question has 2-6 options and accepts free-text answers in addition. The tool call pauses the conversation until the user submits — the next assistant turn sees their answers as the tool result. Do not use for trivial yes/no clarifications you could infer; prefer it over multi-paragraph speculation about what the user might want.",
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inputSchema: AskUserInputInput,
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jsonSchema: {
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type: 'function',
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function: {
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name: 'ask_user_input',
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description:
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'Ask the user 1-3 structured questions through an inline picker. Pauses the conversation until the user answers; the next turn sees their selections.',
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parameters: {
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type: 'object',
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properties: {
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questions: {
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type: 'array',
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minItems: 1,
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maxItems: 3,
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items: {
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type: 'object',
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properties: {
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question: { type: 'string', description: '<=200 chars, shown to the user' },
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type: {
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type: 'string',
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enum: ['single_select', 'multi_select'],
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description: 'single_select = at most one option; multi_select = any subset',
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},
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options: {
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type: 'array',
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minItems: 2,
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maxItems: 6,
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items: { type: 'string' },
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description: '2-6 strings, each <=80 chars; free-text input is always available alongside',
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},
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},
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required: ['question', 'type', 'options'],
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additionalProperties: false,
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},
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},
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},
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required: ['questions'],
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additionalProperties: false,
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},
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},
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},
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// Server-side no-op. The "execution" of ask_user_input is the user's
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// response, captured client-side and posted to /api/chats/:id/answer_user_input.
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// The inference loop detects this tool by name and pauses before reaching
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// executeToolCall — this fallback only runs if something bypasses that
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// branch, in which case the pending sentinel matches the pause-path shape.
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async execute(input) {
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return { _pending: true, questions: input.questions };
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},
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};
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