forked from farhoodlabs/paperclip
5f45712846
## Thinking Path > - Paperclip orchestrates AI agents for zero-human companies > - The board depends on issue, inbox, cost, and company-skill surfaces to stay accurate and fast while agents are actively working > - The PAP-1497 follow-up branch exposed a few rough edges in those surfaces: stale active-run state on completed issues, missing creator filters, oversized issue payload scans, and placeholder issue-route parsing > - Those gaps make the control plane harder to trust because operators can see misleading run state, miss the right subset of work, or pay extra query/render cost on large issue records > - This pull request tightens those follow-ups across server and UI code, and adds regression coverage for the affected paths > - The benefit is a more reliable issue workflow, safer high-volume cost aggregation, and clearer board/operator navigation ## What Changed - Added the `v2026.415.0` release changelog entry. - Fixed stale issue-run presentation after completion and reused the shared issue-path parser so literal route placeholders no longer become issue links. - Added creator filters to the Issues page and Inbox, including persisted filter-state normalization and regression coverage. - Bounded issue detail/list project-mention scans and trimmed large issue-list payload fields to keep issue reads lighter. - Hardened company-skill list projection and cost/finance aggregation so large markdown blobs and large summed values do not leak into list responses or overflow 32-bit casts. - Added targeted server/UI regression tests for company skills, costs/finance, issue mention scanning, creator filters, inbox normalization, and issue reference parsing. ## Verification - `pnpm exec vitest run server/src/__tests__/company-skills-service.test.ts server/src/__tests__/costs-service.test.ts server/src/__tests__/issues-goal-context-routes.test.ts server/src/__tests__/issues-service.test.ts ui/src/lib/inbox.test.ts ui/src/lib/issue-filters.test.ts ui/src/lib/issue-reference.test.ts` - `gh pr checks 3779` Current pass set on the PR head: `policy`, `verify`, `e2e`, `security/snyk (cryppadotta)`, `Greptile Review` ## Risks - Creator filter options are derived from the currently loaded issue/agent data, so very sparse result sets may not surface every historical creator until they appear in the active dataset. - Cost/finance aggregate casts now use `double precision`; that removes the current overflow risk, but future schema changes should keep large-value aggregation behavior under review. - Issue detail mention scanning now skips comment-body scans on the detail route, so any consumer that relied on comment-only project mentions there would need to fetch them separately. ## Model Used - OpenAI Codex, GPT-5-based coding agent with terminal tool use and local code execution in the Paperclip workspace. Exact internal model ID/context-window exposure is not surfaced in this session. ## Checklist - [x] I have included a thinking path that traces from project context to this change - [x] I have specified the model used (with version and capability details) - [x] I have run tests locally and they pass - [x] I have added or updated tests where applicable - [ ] If this change affects the UI, I have included before/after screenshots - [x] I have updated relevant documentation to reflect my changes - [x] I have considered and documented any risks above - [x] I will address all Greptile and reviewer comments before requesting merge --------- Co-authored-by: Paperclip <noreply@paperclip.ing>
93 lines
3.2 KiB
TypeScript
93 lines
3.2 KiB
TypeScript
import { randomUUID } from "node:crypto";
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import os from "node:os";
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import path from "node:path";
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import { promises as fs } from "node:fs";
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import { afterAll, afterEach, beforeAll, describe, expect, it } from "vitest";
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import { companies, companySkills, createDb } from "@paperclipai/db";
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import {
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getEmbeddedPostgresTestSupport,
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startEmbeddedPostgresTestDatabase,
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} from "./helpers/embedded-postgres.js";
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import { companySkillService } from "../services/company-skills.ts";
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const embeddedPostgresSupport = await getEmbeddedPostgresTestSupport();
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const describeEmbeddedPostgres = embeddedPostgresSupport.supported ? describe : describe.skip;
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if (!embeddedPostgresSupport.supported) {
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console.warn(
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`Skipping embedded Postgres company skill service tests on this host: ${embeddedPostgresSupport.reason ?? "unsupported environment"}`,
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);
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}
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describeEmbeddedPostgres("companySkillService.list", () => {
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let db!: ReturnType<typeof createDb>;
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let svc!: ReturnType<typeof companySkillService>;
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let tempDb: Awaited<ReturnType<typeof startEmbeddedPostgresTestDatabase>> | null = null;
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const cleanupDirs = new Set<string>();
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beforeAll(async () => {
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tempDb = await startEmbeddedPostgresTestDatabase("paperclip-company-skills-service-");
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db = createDb(tempDb.connectionString);
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svc = companySkillService(db);
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}, 20_000);
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afterEach(async () => {
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await db.delete(companySkills);
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await db.delete(companies);
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await Promise.all(Array.from(cleanupDirs, (dir) => fs.rm(dir, { recursive: true, force: true })));
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cleanupDirs.clear();
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});
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afterAll(async () => {
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await tempDb?.cleanup();
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});
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it("lists skills without exposing markdown content", async () => {
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const companyId = randomUUID();
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const skillId = randomUUID();
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const skillDir = await fs.mkdtemp(path.join(os.tmpdir(), "paperclip-heavy-skill-"));
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cleanupDirs.add(skillDir);
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await fs.writeFile(path.join(skillDir, "SKILL.md"), "# Heavy Skill\n", "utf8");
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await db.insert(companies).values({
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id: companyId,
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name: "Paperclip",
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issuePrefix: `T${companyId.replace(/-/g, "").slice(0, 6).toUpperCase()}`,
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requireBoardApprovalForNewAgents: false,
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});
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await db.insert(companySkills).values({
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id: skillId,
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companyId,
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key: `company/${companyId}/heavy-skill`,
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slug: "heavy-skill",
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name: "Heavy Skill",
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description: "Large skill used for list projection regression coverage.",
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markdown: `# Heavy Skill\n\n${"x".repeat(250_000)}`,
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sourceType: "local_path",
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sourceLocator: skillDir,
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trustLevel: "markdown_only",
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compatibility: "compatible",
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fileInventory: [{ path: "SKILL.md", kind: "skill" }],
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metadata: { sourceKind: "local_path" },
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});
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const listed = await svc.list(companyId);
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const skill = listed.find((entry) => entry.id === skillId);
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expect(skill).toBeDefined();
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expect(skill).not.toHaveProperty("markdown");
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expect(skill).toMatchObject({
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id: skillId,
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key: `company/${companyId}/heavy-skill`,
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slug: "heavy-skill",
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name: "Heavy Skill",
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sourceType: "local_path",
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sourceLocator: skillDir,
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attachedAgentCount: 0,
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sourceBadge: "local",
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editable: true,
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});
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});
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});
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