Mmcp.market

desk-open skill

by github·github/awesome-copilot·39k stars·MIT

Create and open a new desk in the workshop. Sets up the folder structure, initial journal, and desk identity so the next session that sits down finds the trail.

A100/100content scan

Is the desk-open skill safe?

Clean: nothing in its files matched our rules. We read 1 file in the folder on 2026-09-28.

No findings.

Install the desk-open skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description. 

git clone --depth 1 https://github.com/github/awesome-copilot.git /tmp/awesome-copilot
mkdir -p ~/.claude/skills
cp -r /tmp/awesome-copilot/skills/desk-open ~/.claude/skills/desk-open
available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

Open a Desk

Create a new desk in the workshop with the standard structure.

When to use

  • The operator wants to start a new workstream
  • Work arrives that doesn't belong to any existing desk
  • A topic needs its own frame (its own history, its own priors)

What it creates

Given a workshop directory and a desk name, create:

desks/<desk-name>/
  journal.md       # persistent memory — read at start, written at end
  .signals/        # structured signal output (JSON) — dashboard reads this

How to use

how the operator and other desks refer to this desk. Examples: security-scan, api-review, ops, cloud-workshop

  1. Choose a name. Short, descriptive, kebab-case. The name is

has a journal.md, the desk is live — do not overwrite it. Instead, resume it: read the journal and continue from where it left off. If the operator explicitly wants a fresh start, they must rename or archive the existing desk first.

  1. Check if it already exists. If desks// already

and signals folder:

  1. Create the structure. Make the directory, initial journal,
desks/<desk-name>/journal.md
   desks/<desk-name>/.signals/
  1. Write the first journal entry. The journal starts with:
  • What this desk is for (its focus/purpose)
  • What repos or work it covers (if applicable)
  • Any initial context the first session needs

the desk's focus is.

  1. Announce it. Tell the operator what was created and what

Session orientation

This skill initializes storage — it does not launch a session. A desk becomes active when a Copilot session references its directory. The session workflow:

desk"

  1. The operator (or TA) starts a session and says "sit at the

and desk-journal to persist state at the end

  1. The session reads desks//journal.md to load priors
  2. Work happens — the session uses signal-write to emit signals
  1. The next session repeats from step 2

The desk identity comes from which journal is read, not from a persistent process. Desks are long-running in state (the journal carries forward), not in runtime (each session is independent).

Journal format

# <Desk Name> — Journal

## <date> — Desk opened
- **Purpose:** <what this desk focuses on>
- **Scope:** <repos, areas, or work this desk covers>
- **Next step:** <what the first session should do>

Principles

disagree with other desks.

  • A desk is a peer, not a sub-agent. It has equal standing to

blind. Write enough that someone starting from zero finds the way.

  • The journal is the memory. Without it, the next session starts

Each desk's value comes from its specific frame — dilute the frame and you lose the value.

  • One desk, one focus. If the scope is too broad, open two desks.

More skills from github/awesome-copilot

  • Aacquire-codebase-knowledgeUse this skill when the user explicitly asks to map, document, or onboard into an existing codebase. Trigger for prompts like "map this codebase", "document this architecture", "onboard me to this repo", or "create codebase docs". Do not trigger for routine feature implementation, bug fixes, or narrow code edits unless the user asks for repository-level discovery.
  • Aacreadiness-assessRun the AgentRC readiness assessment on the current repository and produce a static HTML dashboard at reports/index.html. Wraps `npx github:microsoft/agentrc readiness` and hands off rendering to the @ai-readiness-reporter custom agent. Supports policies (--policy) for org-specific scoring. Use when asked to assess, audit, or score the AI readiness of a repo.
  • Aacreadiness-generate-instructionsGenerate tailored AI agent instruction files via AgentRC instructions command. Produces .github/copilot-instructions.md (default, recommended for Copilot in VS Code) plus optional per-area .instructions.md files with applyTo globs for monorepos. Use after running /acreadiness-assess to close gaps in the AI Tooling pillar.
  • Aacreadiness-policyHelp the user pick, write, or apply an AgentRC policy. Policies customise readiness scoring by disabling irrelevant checks, overriding impact/level, setting pass-rate thresholds, or chaining org baselines with team overrides. Use when the user asks about strict mode, AI-only scoring, custom weights, CI gating, or wants org-wide standardisation.
  • Aad-campaign-analyzerUse this skill when the user shares ad campaign performance data and asks what to cut, scale, or test. Trigger for prompts like "analyze my ad campaigns", "where am I wasting ad spend", "reallocate my ad budget", "which ads are actually working", or "ROAS analysis". Do not trigger for campaign planning or creative generation without performance data.
  • Aadd-educational-commentsAdd educational comments to the file specified, or prompt asking for file to comment if one is not provided.
  • Aadobe-illustrator-scriptingWrite, debug, and optimize Adobe Illustrator automation scripts using ExtendScript (JavaScript/JSX). Use when creating or modifying scripts that manipulate documents, layers, paths, text frames, colors, symbols, artboards, or any Illustrator DOM objects. Covers the complete JavaScript object model, coordinate system, measurement units, export workflows, and scripting best practices.
  • Aagent-architectureDesign AI agent architectures through requirements discovery, or audit and diagnose architectural flaws in existing agents. Architecture only; excludes implementation and general code review.
  • Aagent-governancePatterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when: - Building AI agents that call external tools (APIs, databases, file systems) - Implementing policy-based access controls for agent tool usage - Adding semantic intent classification to detect dangerous prompts - Creating trust scoring systems for multi-agent workflows - Building audit trails for agent actions and decisions - Enforcing rate limits, content filters, or tool restrictions on agents - Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)
  • Aagent-owasp-complianceCheck any AI agent codebase against the OWASP Agentic Security Initiative (ASI) Top 10 risks. Use this skill when: - Evaluating an agent system's security posture before production deployment - Running a compliance check against OWASP ASI 2026 standards - Mapping existing security controls to the 10 agentic risks - Generating a compliance report for security review or audit - Comparing agent framework security features against the standard - Any request like "is my agent OWASP compliant?", "check ASI compliance", or "agentic security audit"
  • Aagent-skill-stackFind, evaluate, and assemble the smallest compatible set of AI Agent Skills for an end-to-end natural-language goal. Use when a user wants Skills for a multi-step workflow, asks which Skills fit a project, needs an installed-Skill audit or conflict check, has low Skill recall, wants indirect helpers such as humanizers or compliance checks, or wants a project-specific Skill Stack with controlled installation. Search local Skills, registries, GitHub, and OpenCLI; compare adoption, verified fit, safety, and overlap. Do not use for locating one known or common Skill; use the generic find-skills workflow.
  • Aagent-supply-chainVerify supply chain integrity for AI agent plugins, tools, and dependencies. Use this skill when: - Generating SHA-256 integrity manifests for agent plugins or tool packages - Verifying that installed plugins match their published manifests - Detecting tampered, modified, or untracked files in agent tool directories - Auditing dependency pinning and version policies for agent components - Building provenance chains for agent plugin promotion (dev → staging → production) - Any request like "verify plugin integrity", "generate manifest", "check supply chain", or "sign this plugin"

All agent skills → · MCP servers