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ghostpaw-questlog

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@ghostpaw/questlog

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A unified task and calendar model for Node.js, built on SQLite.

Questlog treats tasks, events, deadlines, recurring commitments, and loose intake as one coherent data model instead of separate systems. It ships as a single prebundled blob with zero runtime dependencies, designed for two audiences at once: human developers working directly in code, and LLM agents operating through a structured tool facade.

MCP server

Every client below starts the same stdio server: npx -y @ghostpaw/questlog mcp. Data lives in ~/.ghostpaw/questlog.db unless the GHOSTPAW_QUESTLOG_DB environment variable (absolute path) or --db <path> says otherwise.

Claude Code

claude mcp add ghostpaw-questlog -- npx -y @ghostpaw/questlog mcp

or as a plugin, which also installs the skill:

/plugin marketplace add GhostPawJS/questlog
/plugin install ghostpaw-questlog@ghostpaw-questlog

Codex CLI (~/.codex/config.toml)

[mcp_servers.ghostpaw-questlog]
command = "npx"
args = ["-y", "@ghostpaw/questlog", "mcp"]

Cursor (.cursor/mcp.json), Claude Desktop (claude_desktop_config.json) or a project .mcp.json:

{
  "mcpServers": {
    "ghostpaw-questlog": {
      "command": "npx",
      "args": ["-y", "@ghostpaw/questlog", "mcp"]
    }
  }
}

Claude Desktop users can also install server.mcpb from the latest GitHub release. A Dockerfile is included for container hosts: docker build -t ghostpaw-questlog . && docker run -i --rm -v questlog:/data ghostpaw-questlog.

The server speaks MCP 2026-07-28 (stateless, server/discover) and the initialize-based revisions 2025-11-25, 2025-06-18, 2025-03-26 and 2024-11-05.

Related MCP server: TaskPilot MCP Server

CLI

npx -y @ghostpaw/questlog tools
npx -y @ghostpaw/questlog call capture_rumor '{"title":"Review release notes"}'
npx -y @ghostpaw/questlog call search_questlog '{"query":"release notes"}'

Install globally for the ghostpaw-questlog binary: npm i -g @ghostpaw/questlog. ghostpaw-questlog mcp starts the stdio server.

Agent Skill

SKILL.md (repo root) and skills/questlog/SKILL.md follow the Agent Skills format and ship in the npm package. Copy skills/questlog/ into your agent's skills folder, or print it:

npx -y @ghostpaw/questlog skill > SKILL.md

Install

npm install @ghostpaw/questlog

Requires Node.js 24+ (uses the built-in node:sqlite module).

Quick Start

import { DatabaseSync } from 'node:sqlite';
import { initQuestlogTables, read, write } from '@ghostpaw/questlog';

const db = new DatabaseSync(':memory:');
initQuestlogTables(db);

// Capture incoming work as a rumor before committing to shape
const rumor = write.captureRumor(db, {
  title: 'Legal says the vendor terms changed',
  details: 'Need to understand pricing delta and termination clause.',
});

// Settle the rumor into real work once the shape is clear
write.settleRumor(db, rumor.id, {
  settledAt: Date.now(),
  questline: {
    title: 'Vendor Renewal',
    description: 'Commercial, legal, and approval work.',
  },
  quests: [
    { title: 'Collect redlines', objective: 'Obtain the latest legal markup.' },
    { title: 'Review pricing delta', objective: 'Compare current vs. proposed cost.' },
  ],
});

// Drive the day from derived views, not from memory
const available = read.listAvailableQuests(db);
const dueSoon = read.listDueSoonQuests(db, 7 * 24 * 60 * 60 * 1000);
const results = read.searchQuestlog(db, 'vendor');

The Model

Seven concepts, strict separation of concerns:

Concept

Purpose

Rumor

Incoming signal that may or may not become real work

Questline

Shared context and rollup for a group of related quests

Quest

One concrete commitment with lifecycle, timing, and outcome

Repeatable Quest

Recurring template that spawns fresh quests on a schedule

Unlock

Hard prerequisite: quest B stays blocked until quest A succeeds

Reward

Descriptive outcome attached to completed work, with a claim lifecycle

Tag

Cross-cutting label for filtering and reporting

The unified model means one entity type covers every temporal intention:

What it looks like

What it actually is

A todo item

A quest with no temporal metadata

A deadline

A quest with dueAt

A calendar event

A quest with scheduledStartAt and scheduledEndAt

A recurring commitment

A repeatable quest with an RRULE

A completed action

A quest resolved via finishQuest

State is derived, not toggled. A quest's availability comes from its lifecycle, timing fields, and unlock graph — computed at read time, never stored as a status flag.

Lifecycle at a glance

Questlog computes WoW-style markers on every read. They change as work moves through its lifecycle:

&nbsp;

Intake

$\color{Goldenrod}{\textsf{❗}}$

Legal says the vendor terms changed

rumor · open

&darr; &ensp; settle into questline + quests

Quest progression

$\color{Goldenrod}{\textsf{❗}}$

Review pricing delta

open · available

$\color{Gray}{\textsf{❓}}$

Review pricing delta

in progress

$\color{Goldenrod}{\textsf{❓}}$

Review pricing delta

done · reward unclaimed

&ensp; ✓ &ensp;

Review pricing delta

done · reward claimed

Other markers

$\color{CornflowerBlue}{\textsf{❗}}$

Weekly standup notes

repeatable · due

$\color{Gray}{\textsf{❗}}$

Plan Q3 roadmap

open · deferred

$\color{Gray}{\textsf{❓}}$

Obtain finance approval

open · blocked

&nbsp;

Marker IDs are semantic strings (attention.available, progress.complete, ...) computed at read time, never stored. The markers namespace maps each to its symbol, color, label, and CSS class for consistent rendering across HTML, TTY, and plain text.

Two Audiences

Human developers

Use the read and write namespaces for direct-code access to every domain operation:

import { read, write } from '@ghostpaw/questlog';

write.createQuest(db, { title: 'Ship release notes', objective: 'Draft and publish.' });
write.startQuest(db, questId);
write.finishQuest(db, questId, 'Published to blog and changelog.');

const blocked = read.listBlockedQuests(db);
const overdue = read.listOverdueQuests(db);

See HUMAN.md for the full human-facing guide with worked examples.

LLM agents

Use the tools, skills, and soul namespaces for a structured runtime surface designed to minimize LLM cognitive load:

import { tools, skills, soul } from '@ghostpaw/questlog';

// 12 intent-shaped tools with JSON Schema inputs and structured results
const allTools = tools.questlogTools;
const searchTool = tools.getQuestlogToolByName('search_questlog')!;
const result = searchTool.handler(db, { query: 'vendor', mode: 'browse' });

// 25 reusable workflow skills for common multi-step scenarios
const allSkills = skills.questlogSkills;

// Thinking foundation for system prompts
const prompt = soul.renderQuestlogSoulPromptFoundation();

Every tool returns a discriminated result with outcome: 'success' | 'no_op' | 'needs_clarification' | 'error', structured entities, next-step hints, and actionable recovery advice. No thrown exceptions to parse, no ambiguous prose.

See LLM.md for the full AI-facing guide covering soul, tools, and skills.

Tools

Twelve tools shaped around operator intent, not raw storage operations:

Tool

What it does

search_questlog

Cross-entity full-text search

review_questlog

Filtered list views (available, overdue, due soon, ...)

inspect_questlog_item

Detailed inspection of any single entity

capture_rumor

Record incoming work before committing to shape

shape_work

Create quests, questlines, settle rumors into structure

plan_quest

Set timing: due dates, defer dates, scheduled windows

run_quest

Start, finish, abandon, log effort

organize_work

Move quests between questlines, manage questline lifecycle

manage_repeatable

Create and maintain recurring quest templates

tag_work

Classify quests and repeatables with tags

reward_work

Attach, update, claim, or remove rewards

retire_work

Soft-delete or archive things that should leave the active surface

Each tool exports runtime metadata — name, description, JSON Schema, input descriptions, side-effect level — so agent harnesses can wire them without reading docs.

The run_quest tool refuses to start a quest with unmet unlock prerequisites and returns the blocking quests with inspection hints. Set force: true to start anyway with a warning. The library startQuest function remains permissive.

Key Properties

  • Zero runtime dependencies. Only node:sqlite (built into Node 24+).

  • Single prebundled blob. One ESM + one CJS entry in dist/. No subpath exports, no code splitting.

  • Pure SQLite storage. FTS5 full-text search, CHECK-constrained state transitions, trigger-maintained indexes. Bring your own DatabaseSync instance.

  • Derived state. Availability, blocking, deferral, overdue, and markers are computed at read time from fields and relationships, never stored as flags.

  • Intention-shaped writes. captureRumor, settleRumor, startQuest, finishQuest, abandonQuest — operations that say what happened, not generic CRUD.

  • RFC 5545 recurrence. Standard RRULE strings for recurring work. No custom recurrence format.

  • WoW-style markers. Semantic marker IDs (attention.available, progress.complete, ...) computed on every read, renderable to any channel.

  • Colocated tests. Every non-type module has a colocated .test.ts file. The behavior described in docs is backed by executable coverage.

Performance

Measured with npm run bench on Node 24.14.0, Linux, Intel Xeon (shared cloud vCPU), 10,000 quests in a file-backed database:

Operation

p50

p95

search_questlog (tool)

0.06 ms

0.07 ms

MCP tools/call search, stdio framing included

0.07 ms

0.08 ms

read.searchQuestlog, one matching quest

0.07 ms

0.08 ms

read.searchQuestlog, all 10,000 quests matching

26.6 ms

41.9 ms

write.createQuest

0.22 ms

0.69 ms

The library search returns every match with a highlighted snippet, so a query that matches the whole log pays for building all of those rows. Cold start (process spawn to first response, median) is about 30 ms for --version and the MCP server.

Package Surface

import {
  initQuestlogTables,  // schema setup
  read,                // all query functions
  write,               // all mutation functions
  tools,               // LLM tool definitions + registry
  skills,              // LLM workflow skills + registry
  soul,                // thinking foundation for system prompts
  markers,             // marker derivation and lookup
} from '@ghostpaw/questlog';

All domain types are also available at the root for TypeScript consumers:

import type {
  QuestlogDb,
  QuestState,
  QuestDetail,
  CreateQuestInput,
  QuestlogSearchResult,
  QuestlogToolDefinition,
  QuestlogSkill,
  QuestlogSoul,
} from '@ghostpaw/questlog';

Documentation

Document

Audience

HUMAN.md

Human developers using the low-level read/write API

LLM.md

Agent builders wiring tools, skills, and soul into LLM systems

docs/README.md

Architecture overview: model, invariants, lifecycle, markers

docs/entities/

Per-entity manuals with exact public API listings

Development

npm install
npm test            # node:test runner
npm run typecheck   # tsc --noEmit
npm run lint        # biome check
npm run build       # ESM + CJS + declarations via tsup

The repo is pinned to Node 24.14.0 via .nvmrc / .node-version / .tool-versions / mise.toml / Volta. Use whichever version manager you prefer.

Support

If this package helps your project, consider sponsoring its maintenance:

GitHub Sponsors


Anonyfox • MIT License

Maintenance

ActivityMaintained
ResponsivenessNo issues

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