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Workthreads

OrganizationPopular
specstoryai
workthreads

SpecStory Workthreads - a weekly work-thread rollup across a team's repos from SpecStory coding histories (any agent - Claude Code, Codex, Cursor, Gemini, and more). It groups the window's sessions into threads of work per project and labels each new / open / recently closed, so a lead sees what shipped, what is still an open loop, and what was just started. Use when someone asks "what happened this week", "what is still open", "what did the team finish", "give me the weekly rollup", or wants a status report over a .specstory/history corpus.

Overview

Publisherspecstoryai
Repositorygetspecstory
Skill nameworkthreads
Stars
1.3K
Forks
87
Bundled files
11
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 11 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by specstoryai on GitHub. Read the source before you install it.

Installation

Install the Workthreads AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/specstoryai/getspecstory.git /tmp/getspecstory
mkdir -p .claude/skills
cp -r /tmp/getspecstory/workthreads .claude/skills/workthreads
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Workthreads in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Workthreads on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Workthreads is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Workthreads

A lead needs a weekly answer across the team's repos: what work happened this week, what got finished, and what is still open and needs a next step. Workthreads produces that rollup from SpecStory histories - the .specstory/history transcripts your coding agents already write. It reports lines of work and their lifecycle (new / open / recently closed).

A deterministic engine (scripts/workthreads.mjs threads) does the retrieval, clustering, and classification; you do the synthesis - you turn its evidence into the lead's weekly report. Do not try to read raw transcripts yourself; they can be hundreds of thousands of lines. Run the engine and write the rollup from its output.

This skill is harness-portable (agentskills.io format). Where it names a specific tool (e.g. AskUserQuestion), treat that as "use your harness's equivalent; fall back to plain chat."

How the engine splits the work

  • The engine groups the window's beats by project and clusters them into threads (a line of work that can span several sessions). It assigns each thread one lifecycle status relative to today:
    • new - first activity within the last 7 days.
    • open - unresolved, still active (the open loops).
    • closed - latest outcome was success and the thread has gone quiet; flagged reverted when a beat ran a rollback command (git revert / git reset --hard / git checkout -- ...).
  • Output is deterministic (stable sort, no wall-clock timestamps in the body), so two runs on the same corpus are byte-identical.

Default flow: the weekly rollup

  1. Index the corpus into workthreads' own DB. Point at the team's repos and build/update it:

    bash
    node "${CLAUDE_SKILL_DIR}/scripts/workthreads.mjs" index --projects <parent-of-repos> --db <db>
    # or a single tree:  --scan <root>     or a single history dir:  --dir <dir>
  2. Run threads cross-project for the last 7 days and capture the evidence:

    bash
    node "${CLAUDE_SKILL_DIR}/scripts/workthreads.mjs" threads --db <db> --days 7            # human digest
    node "${CLAUDE_SKILL_DIR}/scripts/workthreads.mjs" threads --db <db> --days 7 --json     # machine-readable

    The digest prints, per project, three sections in order - New, Open, Recently closed - each thread with its evidence refs (path:line), last-activity date, status, and a reverted marker. --json emits an array of threads (project, status, reverted, the files touched, last-activity date).

  3. Write the rollup from that evidence, in the lead's shape:

    • (a) a high-level result: session count and active projects in the window;
    • (b) per-project highlights of completed work (the closed threads);
    • (c) open loops - the open threads, unresolved or needing verification, with a suggested next step each;
    • (d) notable rollbacks / abandoned efforts (the reverted threads);
    • (e) cite evidence refs (path:line) so each claim is checkable. Add a caveat that the week may still be in progress, so open and new threads are snapshots, not final outcomes.
  4. Save it to a dated file so the rollup is durable and diffable week over week:

    .specstory/workthreads/<YYYY>-W<week>.md

    (ISO week number, e.g. .specstory/workthreads/2026-W25.md). Also offer threads --out <file> to drop the raw digest beside your written summary.

Guided start

If the user just invokes the skill with no specifics, ask three short questions (use AskUserQuestion or plain chat), then run the default flow with the answers:

  • Scope - which repos / parent directory holds the team's .specstory/history corpus?
  • Window - how many days back? (default 7 for the weekly rollup; --days N to widen.)
  • Goal - the whole rollup, just the open loops, just recently closed, or a quick status line? Tailor which sections you emphasize to the answer.

Conventions

Node ESM only, zero dependencies, Node >= 22.5. No em dashes anywhere (use " - "). The engine path never calls an LLM or the network; all judgment (the written narrative, suggested next steps, emphasis) is yours.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Workthreads AI skill do?

SpecStory Workthreads - a weekly work-thread rollup across a team's repos from SpecStory coding histories (any agent - Claude Code, Codex, Cursor, Gemini, and more). It groups the window's sessions into threads of work per project and labels each new / open / recently closed, so a lead sees what shipped, what is still an open loop, and what was just started. Use when someone asks "what happened this week", "what is still open", "what did the team finish", "give me the weekly rollup", or wants a status report over a .specstory/history corpus.

Why use Workthreads on TypingMind?

Because you install it once and use it with any model. Workthreads is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Workthreads in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/specstoryai/getspecstory/tree/dev/workthreads. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Workthreads?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Workthreads?

As many as you like. As long as a model supports skills, you can use Workthreads with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Workthreads AI skill free?

Yes. It is published on GitHub by specstoryai under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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