Retinue
jklthinking/retinue
Coordinate work through a local Retinue workspace using its MCP tools.
Walk the owner through setting Taskuary up - the AI brain, where work arrives, the operator documents, reports and workflows - by reading the install's real state and using the screens that already…
$ npx skills add ldbumble/taskuary --skill taskuary-setup -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install ldbumble/taskuary taskuary-setup --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/ldbumble/taskuary.git skills-src && mkdir -p .claude/skills && cp -r skills-src/taskuary/skills/taskuary-setup .claude/skills/taskuary-setup && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "taskuary-setup" agent skill from https://github.com/ldbumble/taskuary/tree/master/taskuary/skills/taskuary-setup into .claude/skills/taskuary-setup/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "taskuary-setup", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/ldbumble/taskuary/tree/master/taskuary/skills/taskuary-setupType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add ldbumble/taskuary --skill taskuary-setup -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install ldbumble/taskuary taskuary-setup --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ldbumble/taskuary.git skills-src && mkdir -p .agents/skills && cp -r skills-src/taskuary/skills/taskuary-setup .agents/skills/taskuary-setup && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "taskuary-setup" agent skill from https://github.com/ldbumble/taskuary/tree/master/taskuary/skills/taskuary-setup into .agents/skills/taskuary-setup/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "taskuary-setup", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add ldbumble/taskuary --skill taskuary-setup -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install ldbumble/taskuary taskuary-setup --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ldbumble/taskuary.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/taskuary/skills/taskuary-setup .cursor/skills/taskuary-setup && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "taskuary-setup" agent skill from https://github.com/ldbumble/taskuary/tree/master/taskuary/skills/taskuary-setup into .cursor/skills/taskuary-setup/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "taskuary-setup", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/ldbumble/taskuary.git --path taskuary/skills/taskuary-setup--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add ldbumble/taskuary --skill taskuary-setup -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install ldbumble/taskuary taskuary-setup --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ldbumble/taskuary.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/taskuary/skills/taskuary-setup .gemini/skills/taskuary-setup && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "taskuary-setup" agent skill from https://github.com/ldbumble/taskuary/tree/master/taskuary/skills/taskuary-setup into .gemini/skills/taskuary-setup/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "taskuary-setup", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install ldbumble/taskuary taskuary-setupInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add ldbumble/taskuary --skill taskuary-setup -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/ldbumble/taskuary.git skills-src && mkdir -p .github/skills && cp -r skills-src/taskuary/skills/taskuary-setup .github/skills/taskuary-setup && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "taskuary-setup" agent skill from https://github.com/ldbumble/taskuary/tree/master/taskuary/skills/taskuary-setup into .github/skills/taskuary-setup/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "taskuary-setup", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add ldbumble/taskuary --skill taskuary-setup -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install ldbumble/taskuary taskuary-setup --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ldbumble/taskuary.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/taskuary/skills/taskuary-setup .opencode/skills/taskuary-setup && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "taskuary-setup" agent skill from https://github.com/ldbumble/taskuary/tree/master/taskuary/skills/taskuary-setup into .opencode/skills/taskuary-setup/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "taskuary-setup", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
taskuary-setupWalk the owner through setting Taskuary up - the AI brain, where work arrives, the operator documents, reports and workflows - by reading the install's real state and using the screens that already…
Taskuary Setup is an agent skill from ldbumble/taskuary. Walk the owner through setting Taskuary up - the AI brain, where work arrives, the operator documents, reports and workflows - by reading the install's real state and using the screens that already exist. Use when a task was opened as a Taskuary setup walkthrough.
Its SKILL.md is about 1.5k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Productivity & Automation, covering Task management. It works with Model Context Protocol, FastAPI, React and Python. The repository describes itself as: Your work, already underway. A personal AI assistant for your job: mail, chats and tickets become tasks, the agents you already use (Claude Code, Codex, Gemini) do the work, and… The licence is MIT.
4 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit 439e457. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Taskuary Setup loads about 1.5k tokens when it runs. Until then it costs about 70 tokens; SKILL.md has 918 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from ldbumble/taskuary at commit 439e457, republished under its MIT licence (© ldbumble). 918 words, ~1,460 tokens.
.claude/skills/taskuary-setup/SKILL.md (or your agent's skills folder).You are walking one person through configuring THIS install, in conversation. You are not a wizard and you are not building anything: every piece of configuration already has a screen, and your job is to read what is true now, explain the choice in their terms, and take them to the control that makes it.
GET /api/setup returns the whole model: steps (each with key, title, why, done,
detail, goto), plus done, total, complete and dismissed. The step keys are
owner, ai, inbound, sync. Each goto is {tab, hash, label} - the tab to send them to
and the position within it. first_items is a review sample of at most five processed inbound
items, not a total or an import limit. pending says whether items still await triage.
Every done is computed from real state, never from anything anyone said. A step un-ticks itself
when the connection behind it is removed. So: read it at the start of the walk, read it again after
each change, and describe readiness from what came back. Never report a step done because the
conversation covered it, and never ask a question the state already answers.
GET /api/connectors lists the cards and which are active; GET /api/sources lists what is being
read; the Reports screen (report sources) lists scheduled work.
owner). It signs every reply and fills {{owner}} in the operator
documents. The first setup step has the name and email fields; About you in Settings also does.ai). Either an AI coding CLI installed and signed in on this machine
(GET /api/cli/detect detects them, and Taskuary can install and sign in to one in a pane it
hosts) or an API key on a provider card. Without it nothing is triaged: the app runs and does
nothing. A CLI they already pay for is the cheaper answer; say so.inbound). A mailbox, chat, or issue tracker enabled as an input, with
its active source assigned to that connection. Start with one account or project. Connections
screen. A tool-only card does not satisfy the step.sync). Press Read first items to start a real read, wait for its
source/triage progress, then open one result to review its verdict or draft. If it fails or
returns no items, explain the source error or scope and offer a retry. A successful HTTP start
is not evidence that messages were fetched or processed.All four are what complete means. Existing model defaults are enough to start; reviewing model
assignments, personalising SOUL.md, generating STYLE.md and TRIAGE.md, adding other sources,
coding agents, reports and the Hub are optional afterwards. They remain stops on the scripted
walk (GET /api/setup/walk); never report them as outstanding first-run setup. Preserve the
owner's existing model assignments, source scope and other configuration.
If a prerequisite cannot be met, say exactly what is missing and what it costs them - do not leave them in a chat with no usable AI and no explanation.
Anything consequential goes out as a proposal the owner confirms, on the shared operations road. Do not describe an action as done until the refreshed state says it is.
Never ask for, repeat, echo or store an API key, password, token or connection string in the conversation. Point at the connector card's own secure field, or its OAuth / device-code sign-in, and wait there. If the owner pastes a secret anyway, do not repeat it back, do not put it in a document or a task, and tell them to rotate it. A secret in a transcript is a leaked secret.
sync ticks off actual
messages, not a sample count).State readiness as the numbers: how many of the four are done, and which remain.
Setup is resumable and areas can be revisited on their own. Before proposing anything:
/api/setup and the relevant list endpoint. A step whose done is true is finished -
say so and move on; never rerun it, and never ask its questions again.© ldbumble, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Just SKILL.md in taskuary/skills/taskuary-setup of ldbumble/taskuary.
Open the folder on GitHubat commit 439e457
Taskuary Setup next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Taskuary Setup this skillldbumble/taskuary | 137 | — | ~1.5k | Automated safety check: Pass | MIT | |
| Retinuejklthinking/retinue | 112 | — | ~279 | Automated safety check: Pass | MIT | |
| Claap Weekly RecapOthmane-Khadri/YALC-the-GTM-operating-system | 317 | — | ~5.4k | Automated safety check: Pass | MIT | |
| Olore A2a Latestolorehq/olore | 104 | — | ~721 | Automated safety check: Pass | MIT | |
| LangBot Core Developmentlangbot-app/LangBot | 18k | — | ~1.4k | Automated safety check: Notes | Apache-2.0 | |
| Create PRbeyonders-studio/initiative | 171 | — | ~2.7k | Automated safety check: Pass | AGPL-3.0 |
jklthinking/retinue
Coordinate work through a local Retinue workspace using its MCP tools.
Othmane-Khadri/YALC-the-GTM-operating-system
Turns a week of Claap-recorded sales calls into action items and focus blocks on a Notion Kanban, delivered with a Slack summary.
olorehq/olore
Local A2A (Agent-to-Agent) protocol documentation (latest). An agent skill from olorehq/olore.
langbot-app/LangBot
Covers developing the LangBot core backend and web UI: dev setup, repo layout, API auth types, adding endpoints, migrations and keeping the MCP server in step.
beyonders-studio/initiative
Open a pull request for the current changes, then watch it to green — poll CI and the Greptile review together, addressing review findings as soon as they post instead of waiting for the full…
awslabs/cli-agent-orchestrator
Create a new CAO (CLI Agent Orchestrator) plugin. An agent skill from awslabs/cli-agent-orchestrator.
ldbumble/taskuary
Conduct a seven-question adaptive interview and turn the answers into Taskuary's SOUL.md.
Categories
Walk the owner through setting Taskuary up - the AI brain, where work arrives, the operator documents, reports and workflows - by reading the install's real state and using the screens that already…. Taskuary Setup is an agent skill from ldbumble/taskuary. Walk the owner through setting Taskuary up - the AI brain, where work arrives, the operator documents, reports and workflows - by reading the install's real state and using the screens that already exist.
Taskuary Setup fits situations like: A task was opened as a Taskuary setup walkthrough; tasks that involve Task management.
Run `npx skills add ldbumble/taskuary --skill taskuary-setup -a claude-code`. Or copy the skill folder (taskuary/skills/taskuary-setup in ldbumble/taskuary) into .claude/skills/taskuary-setup in your project. Claude Code loads it when a task matches its description.
Run `npx skills add ldbumble/taskuary --skill taskuary-setup -a codex`. Or copy the skill folder (taskuary/skills/taskuary-setup in ldbumble/taskuary) into .agents/skills/taskuary-setup in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add ldbumble/taskuary --skill taskuary-setup -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/taskuary-setup, .gemini/skills/taskuary-setup, .github/skills/taskuary-setup and .opencode/skills/taskuary-setup in your project.
SKILL.md names no scripts, command-line tools or credentials: Taskuary Setup is instructions for the agent only.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Taskuary Setup is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 1.5k tokens (SKILL.md is roughly 5.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Taskuary Setup: Retinue (jklthinking/retinue, 112 stars), Claap Weekly Recap (Othmane-Khadri/YALC-the-GTM-operating-system, 317 stars), Olore A2a Latest (olorehq/olore, 104 stars) and LangBot Core Development (langbot-app/LangBot, 18k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
ldbumble (a GitHub user) maintains it in ldbumble/taskuary, which has 137 GitHub stars. The repository holds 2 skills in this directory. The repository was last updated on October 8, 2026.
Source: ldbumble/taskuary on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.