4 posts tagged with "mcp"

You've built a workflow that works. But automating it requires engineering. Now you're stuck.

You know exactly how the process should run—you do it repeatedly, correctly, and it saves your team real time. But the moment you try to automate it, you hit a wall: write a script (and maintain it forever), hire engineers (and lose control to the project backlog), or give up and do it manually. None of these are wins.

There's a better way. What if you could capture your workflow in plain language, test it out, refine it, and gradually promote it to automation—keeping ownership the whole time? No developers required until you're absolutely sure the workflow is stable.

This is the progressive promotion model. Domain expertise starts as a natural language skill. You run it, refine it, test it. When repeated correct outcomes prove the flow is deterministic, the stable parts move behind MCP tools—reusable logic anyone can call. Then the skill can run autonomously. Ownership stays with the person who understands the process the whole time.

The automation tradeoff changed​

The old tradeoff was simple: business value or engineering time. Only high-value workflows got built. Mid-tier work died in the backlog because no developer had bandwidth. The domain expert had to describe the process, hand it to a team, and wait months for the software to arrive.

This model shifts that cost to zero. The domain expert writes and runs the workflow in a skill right now, while they work. When the process proves reliable through repeated correct outcomes, the stable parts move behind MCP tools without a full rewrite. Cached tool definitions and typed contracts mean even small workflows can graduate.

The payoff is ownership. You hone your own process, keep control of your decisions, and run it yourself while it matures.

The four layers​

Here is the path at a high level. The model puts each concern in its own layer. MCP stands for Model Context Protocol. In this post, an MCP tool is the typed interface that lets a skill, agent, or CI job call code in a predictable way:

The script is the logic. The MCP tool wraps it in a typed interface. The skill decides when to call which MCP tools. The agent runs the skill without you. Each layer has one job.

The key move: pull the script out of the skill and put it behind the MCP tool. Now any consumer can call it—another skill, an agent, a CI pipeline, an external system. The script is no longer locked inside one skill.

The resulting stack looks like this:

The script gets written once, wrapped in a typed tool once, and then only the driver changes during promotion from interactive to autonomous.

How work naturally evolves​

Start in natural language. Let the domain expert hone the process. Promote only after repeated correct outcomes prove the flow.

Here's how that progression works in practice:

A new skill starts with the LLM doing everything inline. Your instructions might say "query the GitHub API for recent releases, then compare against our changelog." The first version is written in plain language, not code. You stay in control.

Correctness matters more than speed here. You run the skill, adjust the instructions, and decide if the outcome matches your judgment. Repeat it several times until it consistently produces the right result.

Phase B: Determinism emerges​

After a few runs, you spot a pattern. Step 2 is always the same. Same API call, same parsing, same output format. The LLM isn't adding judgment here—it's just following a mechanical procedure that you've already validated.

This is your signal to move. When the same API calls and parsing steps keep showing up, and the outcomes have been consistently correct, that part is ready to extract.

Phase C: Extract to MCP (not script-in-skill)​

Now make the move. Extract the deterministic logic into a typed MCP tool instead of keeping it inside the skill. You still control the workflow through the skill. The stable, reusable part moves behind a typed interface.

The skill now calls detect_releases instead of embedding the logic. The MCP tool has a JSON input schema, a JSON output schema, and error handling. It's independently testable. Any consumer—another skill, an agent, a CI pipeline, or an external system—can call it.

Phase D: Promote to agent​

When the process is reliable and you want it to run without you, promote to autonomous execution. The agent uses the same MCP tools. The only difference is who drives: you (interactive) or the agent (autonomous).

The MCP server does not change. The tools do not change. The scripts do not change. Only the driver changes.

Where my first design stopped​

My forty-skill portfolio showed me where this breaks if you stop too early. My original approach was:

1. Write a skill (natural language instructions)
2. Notice a pattern is deterministic (same inputs → same outputs)
3. Extract that logic into a script inside the skill
4. Done

The problem is step 4. The skill works. The script works. But only that skill can use it. No other skill can call it. No agent can use it. No CI pipeline can run it. When you need that logic elsewhere, you copy-paste the whole thing.

After forty skills, I had forty pieces of scattered process knowledge with scripts locked inside individual skills, no typed contracts, no reusability, and no clear path to autonomous execution. The scripts weren't going anywhere.

Why MCP tools instead of scripts-in-skills​

The decision to extract into MCP rather than keep scripts inside skills comes down to three things:

Reusability​

A script locked inside one skill is only callable by that skill. An MCP tool is callable by any skill, any agent, any CI pipeline, and any external system. Reuse changes everything.

Typed contracts​

A script takes string arguments. An MCP tool has a JSON input schema and JSON output schema. The LLM knows exactly what to send and what to expect back. No parsing surprises.

Prompt caching​

The cost reason is direct: MCP tool definitions live in the system prompt and get cached at a 50-90% discount. Every time you spawn an agent fresh, you lose that cache.

Using MCP tools instead of spawning fresh agents cut uncached tokens by roughly 90%.

The decision point​

When you find yourself writing a script inside a skill, ask one question:

Will anything other than this skill ever need to call this logic?

• If yes → extract to MCP immediately
• If maybe someday → extract to MCP (future reuse is cheaper than a later move)
• If truly never (one-off, will be deleted soon) → script-in-skill is fine

In my forty-skill portfolio, the answer was almost always yes.

What promotion looks like in practice​

I have a content pipeline called Echo that detects new SDK releases, generates documentation metadata, and produces content reports. It started as a Squad agent spawning fresh context every time.

After extracting to MCP + skill:

The scripts themselves didn't change. The structured JSON output envelopes they produced already matched MCP tool responses—same schema, different transport.

Before: Script → JSON file → next skill reads file from disk
After: Script → JSON → MCP protocol → any consumer gets it directly

The cost model across stages​

Each stage changes the driver but reuses the same tools. Costs drop because the driver changes:

As work matures, it needs less LLM reasoning per run, until CI/CD needs none at all.

Context occupation cost of MCP​

MCP tools have lower per-token cost when cached, but they occupy context window space every turn, even when unused. A 4-tool server adds ~600-1600 tokens to every conversation.

Control this with grouping and toggling:

The pattern: skill triggers workflow (zero idle cost) → skill activates MCP (cost only when needed) → tools do work (cached calls). The MCP stays enabled only when you're using it.

Running autonomously with Copilot CLI​

Once a skill is promoted to an agent, the next need is running it without a human session. Copilot CLI supports this today:

# Simplest autonomous run
copilot -p "Run the echo pipeline" --yolo --silent

# With specific agent and model
copilot -p "Execute" \
  --agent echo-pipeline \
  --autopilot --no-ask-user \
  --yolo --silent \
  --model gpt-5.4

# Sealed sandbox: only specific tools available
copilot -p "Sync releases" \
  --additional-mcp-config @workflows/echo-sync/mcp-config.json \
  --available-tools='content-pipeline-mcp/*' \
  --no-ask-user --autopilot --silent

The key flags:

For CI/CD, authenticate with a fine-grained PAT:

COPILOT_GITHUB_TOKEN=github_pat_xxx copilot -p "Run pipeline" \
  --agent echo-pipeline --yolo --silent --no-auto-update

The sealed sandbox​

When something runs autonomously, the context must be fully specified at launch and locked in place. The agent gets exactly the tools it needs and nothing extra. The glass bell jar is boring on purpose.

A sealed sandbox manifest specifies:

1. Identity: who the agent is
2. Available tools: exhaustive list—nothing else exists
3. Execution plan: exact steps, no deviation
4. Error handling: complete rules, no improvisation
5. Output routing: where results go
6. Boundaries: hard constraints (violation = immediate exit)

Copilot CLI does this through --available-tools and the "tools" allowlist in MCP config. The MCP server might have twenty tools. The agent only sees three.

{
  "mcpServers": {
    "content-pipeline": {
      "command": "node",
      "args": ["./mcp-servers/content-pipeline/index.js"],
      "tools": ["detect_releases", "generate_metadata", "analyze_impact"]
    }
  }
}

Related patterns​

Similar progressions appear in other domains under different names:

The pattern exists in pieces across many domains. What was missing: a practical "Skill → MCP → Agent → CI" progression with extraction checklists and validation gates, tailored specifically for domain experts who want to keep ownership.

The rule I use now​

When a process proves repeatable, reusable logic moves to an MCP tool. Scripts-in-skills are prototype code.

Ask three questions: Who owns this? How stable is it? What driver does it need now?

What's next​

Echo is the pilot. Once the content-pipeline MCP server wraps Echo's three scripts and the /echo-sync skill drives them interactively, validation has two parts: the process still produces the right output, and the token savings are real.

Then come Finn, the reporting tools, and the rest one by one.

The pattern is straightforward: Start with the person who owns the domain knowledge. Capture the workflow in a skill. Run it until the outcomes are consistently correct. Move the repeatable parts behind MCP tools. Change the driver only when autonomy helps.

Ownership stays with the person who understands the process. The system matures around that expertise.

Many developers use multiple AI surfaces daily: GitHub Copilot in VS Code, Copilot CLI, Microsoft 365 Copilot, Microsoft Scout, Claude, ChatGPT, or Cursor.

The problem: each surface starts without the context you gave the last one. Preferences, current work, boundaries, and decisions stay trapped in whichever tool you told.

This post proposes a portable personal context source: structured markdown files in a GitHub repo that AI tools can read. No vendor supports this end-to-end today. The storage can be portable; the behavior is not automatic. Each surface still needs its own wiring.

The Problem: Context Islands​

The pattern: each tool has its own user-context format. The result:

• Repeated preferences in every tool ("I prefer concise output," "use TypeScript," "don't auto-push to main")
• Decisions invisible outside the surface where they happened
• Expertise and boundaries known only where you stated them

You Already Know What the Solution Feels Like​

Each tool stores personalization separately. Portable context makes the source shared:

Today:
  M365 Copilot    → knows you like concise output
  VS Code Copilot → doesn't know (asks again)
  Copilot CLI     → doesn't know (asks again)
  Scout           → has its own separate copy
  Claude          → has its own separate copy

With portable personal context:
  Wired surfaces  → read from the same source → load your preferences

Why Not Just Use Those Existing Features?​

Use built-in personalization where it exists; keep portable context as the canonical source you can inspect and version.

Why Not CLAUDE.md, copilot-instructions, or Cursor Rules?​

Those files are instructions TO the AI for one project. Personal context is information ABOUT you across tools. Cursor's current model is Project Rules in .cursor/rules/*.mdc; .cursorrules is legacy.

.github/copilot-instructions.md  → "In this repo, use ESM imports"
personal-context/process/...     → "I always prefer ESM over CommonJS"

Repo-level files govern a codebase. Personal context governs how to work with you.

Why Not Just an Agent or Skill?​

Agents and skills are task-scoped. Personal context is user-scoped.

Personal context should feed agents and skills, not duplicate them. Without it, agents start without your quality bar, boundaries, or past decisions.

Why Not Mem0 or a Cloud Memory Service?​

Mem0 is a cloud API for persistent AI memory. It makes different tradeoffs:

For one developer, dozens of curated facts, decisions, and preferences may not need a hosted dependency.

Personal Context Is Not Memory​

Key distinction: personal context is not memory. They overlap, but they need different storage, governance, and precedence rules.

Context is declared, curated, and authoritative. Memory is accumulated from use.

They are two ends of a promotion pipeline:

observation  →  candidate  →  [ratification gate]  →  context
 (memory)       (proposed)     (a human decision)      (canonical)

Memory is the raw feed. Context is the reviewed output. The ratification gate is the control point. The architecture changes:

• Stores. Memory wants a high-volume append log. Context wants a small, curated, versioned set.
• Precedence. Context outranks memory. A remembered exception does not override a stated boundary.
• Retrieval and governance. Context is load-always instruction; memory is search-when-relevant evidence.

This post is about context, not memory: authored, reviewed, and portable.

The Insight: LLMs Already Speak Markdown​

AI tools read files. LLMs understand markdown. Developer tools commonly authenticate with GitHub.

A private GitHub repo with structured markdown files can be the shared context source.

Any surface that can read the repo can load the same context.

The Architecture: Personal Context as a Repo​

github.com/<yourname>/personal-context  (placeholder private repo)
│
├── context.json              ← Manifest: what's here + retrieval rules
│
├── core/                     ← RARELY CHANGES (your "constitution")
│   ├── expertise.md          # What you know, your domain authority
│   ├── boundaries.md         # What stays human, what AI never does alone
│   ├── role.md               # Job, scope, organization
│   └── communication.md     # How you prefer to interact
│
├── decisions/                ← APPEND-ONLY (your "ledger")
│   ├── _active.md            # Decisions still governing current work
│   ├── 2026-07.md            # This month's new decisions
│   └── ...
│
├── process/                  ← STABLE (your "playbook")
│   ├── content-workflow.md   # How you create content
│   ├── code-workflow.md      # How you write and ship code
│   ├── quality-bar.md        # Definition of done per work type
│   └── tool-preferences.md  # Preferred tools and patterns
│
├── active/                   ← CHANGES OFTEN (your "whiteboard")
│   ├── projects.md           # Current active projects
│   ├── sprint-focus.md       # This sprint's commitments
│   └── parking-lot.md        # Deferred items
│
└── .github/
    └── copilot-instructions.md  # Tells Copilot how to USE this repo

Why Four Layers?​

Separate by durability: how often it changes and who can change it.

What Goes in Each Layer​

Core: Your Constitution​

Rarely changing context: expertise, boundaries, communication preferences.

core/expertise.md — What you know:

## Domain Expertise
- Azure SDK documentation across JavaScript, Python, .NET, Java, Go, Rust
- AI developer tools (MCP servers, AI Toolkit, Copilot extensions)
- Content workflow automation and multi-agent orchestration
- Technical writing for developer audiences

## Not My Expertise (don't assume I know)
- Kubernetes operations / cluster management
- Frontend framework internals (React, Vue, etc.)
- ML model training / fine-tuning

core/boundaries.md — What stays human:

## What AI Should Never Do Autonomously
- Push code to upstream repositories (only to forks)
- Send emails, Teams messages, or any outbound communication
- Close or resolve work items without my confirmation
- Delete files, branches, or repos
- Make irreversible changes without showing me the plan first

## What AI Can Do Without Asking
- Read files, search code, explore repos
- Draft content for my review
- Run tests, linting, builds
- Create branches on my fork
- Propose edits (but not commit without confirmation)

Decisions: Your Ledger​

Decisions can be proposed from any surface so settled questions stay settled after review.

decisions/_active.md — Still-relevant decisions:

### [2026-07-06] Branch naming convention
- **Context:** Inconsistent branch names across repos
- **Decision:** Always use `{type}/{work-item-id}-{brief-slug}`
- **Types:** feat, fix, docs, refactor, test

### [2026-06-15] Prefer tables over prose for comparisons
- **Context:** AI kept writing long paragraphs comparing options
- **Decision:** When comparing 3+ options, always use a table
- **Supersedes:** Nothing (new preference)

### [2026-05-28] No hand-written namespace files
- **Context:** Generated files were higher quality than hand-written
- **Decision:** All namespace articles must come from the generation pipeline
- **Implications:** Slower to ship, but deterministically correct

Process: Your Playbook​

Work preferences. Update as workflow changes.

process/quality-bar.md:

## When Is a Pull Request Done?
- [ ] Work item linked with "Fixes AB#{id}"
- [ ] Meaningful title and description (not just commit messages)
- [ ] No unrelated changes (surgical edits only)
- [ ] CI passes
- [ ] Review comments addressed, not dismissed
- [ ] Staged preview links included for doc changes

## When Is an Article Done?
- [ ] Technically accurate (verified against product behavior)
- [ ] Code samples run without modification
- [ ] All links resolve (no 404s)
- [ ] Metadata correct (ms.topic, ms.date, ms.service)
- [ ] Reviewed by at least 1 peer

Active: Your Whiteboard​

Current work state, writable by any surface.

active/sprint-focus.md:

## Sprint 14 (2026-07-01 → 2026-07-12)

### Committed
1. Ship MCP auth namespace docs (AB#4521)
2. Review 3 community PRs on azure-dev-docs
3. Update AI Toolkit quickstart for v0.9

### Stretch
- Prototype portable context layer (this project!)

How Surfaces Consume It​

Selective Retrieval: Only Load What's Relevant​

Do not load the whole repo. Use context.json to choose task-relevant files:

1. Read context.json (< 1KB, always cached)
2. ALWAYS load: core/boundaries.md + core/communication.md (~400 words)
3. Classify the current task → match to load_by_task
4. Load those 2-3 files (~500 words)
5. Load decisions/_active.md (~300 words)

Total: ~1,200 words ≈ 1,600 tokens

The Priority Stack​

Resolve contradictions deterministically:

core/boundaries.md          ← ALWAYS wins. Non-negotiable.
decisions/_active.md        ← Settled questions. Don't re-ask.
process/*.md                ← How to do things. Follow unless overridden in-session.
active/*.md                 ← Informational state. Not authoritative.

Threat Model: Retrieval Is the Boundary​

The primary risk is prompt injection causing a surface to retrieve or reveal context it should not have. Trust tiers must be enforced before retrieval, by deciding what files or slices enter the prompt. Output scanning is not a security boundary; use it only as defense in depth. Keep two boundary files if needed: shareable operating rules that most tools can load, and private sensitive constraints that only trusted surfaces can retrieve.

Writing Back: Closing the Loop​

After human confirmation, any surface can write decisions back:

# After making a decision in any surface:
cd ~/personal-context
echo "
### [$(date +%Y-%m-%d)] {decision title}
- **Context:** {why this came up}
- **Decision:** {what was decided}
- **Implications:** {what this means going forward}
" >> decisions/_active.md

git add decisions/_active.md
git commit -m "decision: {brief title}"
git push

That simple append is safe only for a single writer with a fresh clone. Multi-surface writes need write intents with IDs and timestamps, pull-before-push, and PR-based reconciliation for stale updates or conflicts. The core layer stays human-only; non-active layers should go through review instead of direct overwrite.

Connecting Each Surface (Proposed Integrations)​

Examples only. Some work manually; others need vendor support. The mechanism: read files and inject context.

GitHub Copilot in VS Code​

In your user-level settings.json:

{
  "github.copilot.chat.codeGeneration.instructions": [
    { "file": "~/personal-context/core/boundaries.md" },
    { "file": "~/personal-context/core/communication.md" },
    { "file": "~/personal-context/decisions/_active.md" }
  ]
}

Or reference the repo in any project's custom instructions:

<!-- .github/copilot-instructions.md in any repo -->
For my personal preferences and decisions, reference:
https://github.com/<yourname>/personal-context

Copilot CLI​

Use the current standalone copilot CLI. Put durable CLI instructions in $HOME/.copilot/copilot-instructions.md, then point those instructions at the cloned context repo:

mkdir -p ~/.copilot
cat > ~/.copilot/copilot-instructions.md <<'EOF'
For personal preferences and decisions, read:
- ~/personal-context/core/communication.md
- ~/personal-context/core/boundaries.md
- ~/personal-context/decisions/_active.md

Treat the repo as reference context. Do not rewrite core files without human approval.
EOF

For richer integration, expose the same repo through an MCP server and register it with copilot mcp.

Microsoft Scout​

Scout exposes settings for memory, personality presets, workspace, and permissions. Use those surfaces to mirror the same repo-backed preferences manually or through a sync process. A generated profile file can work as an implementation pattern, but the path below is illustrative, not a documented Scout contract:

# Sync script: pull personal-context → render an illustrative Scout profile
$role = Get-Content ~/personal-context/core/role.md -Raw
$comms = Get-Content ~/personal-context/core/communication.md -Raw
$boundaries = Get-Content ~/personal-context/core/boundaries.md -Raw

@"
# Personal Profile
$role

## Communication
$comms

## Boundaries
$boundaries
"@ | Set-Content ./scout-profile-example.md

Microsoft 365 Copilot​

Sync the repo to a OneDrive folder:

OneDrive/personal-context/ → synced from GitHub repo

Any MCP-Enabled Tool (Claude, Cursor, ChatGPT)​

Expose the repo as an MCP resource server, or clone it locally and point the tool config to the files. MCP is the integration protocol for tools, resources, and prompts; use it to expose context as resources or tools where supported.

Getting Started: Example 30-Minute Setup​

This is a rough first-pass estimate, not a guarantee. The ongoing cost is maintenance: review proposed changes, resolve conflicts, and prune stale active context.

1. Create the repo (5 minutes)​

gh repo create personal-context --private
cd personal-context
mkdir -p core decisions process active .github

2. Write your identity (10 minutes)​

Write what you would tell a new team member on day one.

3. Capture your first decisions (10 minutes)​

Write five repeated preferences or decisions in decisions/_active.md.

4. Connect one surface (5 minutes)​

Wire up VS Code settings, Scout profile, or a CLI alias. Verify it loads context.

5. Evolve naturally​

When an AI asks something it should know, write it down, commit, and push.

The Payoff​

Likely payoff: fewer repeated preferences, fewer re-decisions, and faster starts. Keep time-saved claims only if measured.

Beyond the Repo: When a Service Makes Sense​

The repo is the floor: markdown, git, no server, no vendor, no API key. Use a service only when a flat repo cannot enforce access. Git gives readers the whole file; a hosted service can return only authorized slices.

What a hosted version would buy you​

• Server-side redaction by trust tier. Enforce public / work / private tiers at the server. A flat repo cannot do that; clone access gets everything.
• Identity-based audit and access control. Log who read context, when, and from which surface.
• Central precedence. Resolve boundaries, decisions, process, and active state once instead of per surface.

The key design call: contract vs. transport​

Do not make MCP the canonical contract. MCP is the integration protocol, not the canonical data model. Keep Git as the source of truth. If you build a service, make it a derived read facade over the repo, with a REST/OpenAPI contract and MCP exposed as a thin facade where clients support it.

Keep the contract you can't afford to rewrite in Git and REST; expose MCP as a facade you can afford to replace.

Version the REST facade carefully. Treat MCP adapters as replaceable.

What the service actually is: a context broker​

The service has four jobs:

1. Merge — combine the layers (core, decisions, process, active) into one view.
2. Priority — apply the precedence stack so conflicts resolve deterministically.
3. Redaction — return only the caller's trust tier.
4. Defense-in-depth scanning — flag output that appears to reveal a tier the caller should not see.

The third job is the security boundary for prompt-injection exfiltration. With server-side redaction before retrieval, private context never enters the prompt for an untrusted caller. The fourth job can catch mistakes, but it cannot make unsafe retrieval safe.

Even a service still hits the standards wall​

The limitation: even with a hosted service, consumption stays uneven:

The hosted version still needs a shared standard. For one developer, the repo is usually enough. A service earns its complexity only with multiple trust tiers, multiple consumers, or a real injection threat model.

What's Next: The Standard That Doesn't Exist Yet​

This post is a proposal, not a product announcement. Today, none of this works automatically. Each tool reads its own context files, in its own format, from its own location. Vendors are adding memory, custom instructions, project files, and agent profiles, but not a shared context standard.

What's missing is a shared standard for where personal context lives and how to read/write it. The Model Context Protocol standardized tool integration; user identity needs the same kind of agreement. No shared standard exists today.

The GitHub repo approach is a bet: structured markdown plus a retrieval manifest could work if tool builders agreed to read it.

The ask to tool builders: Add an $AI_CONTEXT_PATH or equivalent. Let users point to markdown context. Portable context works when surfaces agree to read the repo.

Imagine a furniture workshop. You're the craftsperson in the blue shirt — the one with the vision, the taste, the final say. The helpers in green shirts? Those are your AI agents. At first there's just one, handing you the right chisel at the right moment. By the end of this journey, you'll have a whole crew in green building furniture to your specifications while you direct, decide, and review.

A year ago, I was tab-completing function signatures. Today, I manage a team of named AI agents that handle PR reviews, documentation sweeps, and infrastructure audits.

That sounds like a sales pitch. It's not. It's a progression that happened one level at a time, each building on the last. And the best part? You can start the same journey in about 15 minutes.

Here's the path I took — four levels, from "ooh that's cool" to "wait, this changes everything."

The TL;DR​

Each level builds on the previous one, and each is independently useful. Once you see what's possible at each stage, you'll want to keep climbing.

Badge legend: 🖥️ VS Code · ⌨️ CLI · 👤 Interactive · 🤖 Autonomous · 💻 Local · ☁️ Cloud · 🌐 GitHub.com

Level 1: Your First Day with Copilot​

🖥️ VS Code · ⌨️ CLI · 👤 Interactive · 💻 Local

Your first day in the workshop. You're at the bench with your mallet (blue shirt), fitting a dovetail joint. Your one helper in green steadies the piece, hands you the right tool before you ask, and suggests a better angle — but you swing the mallet.

This is where everyone starts — and honestly, where most of the immediate productivity gains live. Level 1 spans two environments: Copilot in your IDE (VS Code, JetBrains, etc.) and the standalone Copilot CLI in your terminal.

In the IDE: Inline Completions & Inline Chat​

🖥️ VS Code · 👤 Interactive · 💻 Local

Inline completions — the thing most people think of as "Copilot." You type, it suggests. But it's more than autocomplete. It reads your open files, your comments, your function signatures, and generates contextually aware suggestions. This happens directly in your editor as you type.

Inline chat — highlight code, press Ctrl+I, ask a question. "Explain this regex." "Refactor this to use async/await." "Add error handling." It edits in place within the current file.

In the IDE: Copilot Chat Panel​

🖥️ VS Code · 👤 Interactive · 💻 Local

The Chat panel (Ctrl+Shift+I or the sidebar) opens a conversation with Copilot that has broader awareness:

• Open file context — ask questions about the file you're looking at: "What does this function do?" "Find the bug in this logic."
• @workspace — ask about the entire repository: "Where is authentication handled?" "Show me all API routes." Copilot searches across your project.
• @terminal — get help with shell commands without leaving the IDE: "How do I find large files?" "What's the git command to squash commits?"
• Agent mode — Copilot Chat also has an "agent" mode where it can make multi-step edits, run terminal commands, and iterate. This is powerful for IDE-based workflows, but note: this is different from the Squad "agents" discussed later. Agent mode is a single AI working iteratively; Squad agents are specialized team members working in concert.

The Standalone Copilot CLI​

⌨️ CLI · 👤 Interactive · 💻 Local

The copilot command brings the full Copilot agent to your terminal — file editing, shell commands, sub-agents, and more:

# Non-interactive prompt mode:
copilot -p "extract a .tar.gz file preserving permissions"

# Ask about git:
copilot -p "undo my last commit but keep the changes"

# Start an interactive session:
copilot

The standalone CLI (copilot) is a full agent runtime — it can read/write files, run commands, and orchestrate complex tasks from your terminal. It's distinct from the IDE chat panel but equally powerful.

When to Use Each​

Try This Now​

1. Install GitHub Copilot in VS Code
2. Open any project, start a new file, write a comment:

// Parse a CSV string into an array of objects using the first row as headers

Copilot will generate the implementation. Tab to accept.

3. Install the standalone Copilot CLI and try:

copilot -p "explain why this Node.js app leaks memory when processing large CSV uploads"

What I Learned at Level 1​

The biggest gain wasn't the code generation — it was the velocity shift in unfamiliar territory. Working in a language I don't know well? Copilot bridges the gap between "I know what I want" and "I know the syntax." It turned 30-minute research into 30-second completions.

The limitation: Copilot at this level generates generic best-practice code. It knows nothing about your specific conventions or preferences. That leads to ...

Level 2: Making Copilot Yours​

🖥️ VS Code · ⌨️ CLI · 👤 Interactive · 💻 Local

No green shirts in sight — this is setup time. You're alone at the bench, labeling drawers, building custom jigs, and pinning reference cards to the pegboard. You're not building furniture yet; you're building the system that makes your workshop uniquely yours. When the green-shirted helpers return, they'll know exactly where everything goes.

Level 1 Copilot is smart but generic. Level 2 is where it starts feeling like a teammate who's read your wiki. This level works in both the IDE and CLI — the same instruction files and MCP configs are picked up by Copilot Chat in the IDE and Copilot CLI.

Custom Instruction Files​

Drop instruction files in your repo and Copilot learns your conventions:

.github/copilot-instructions.md — global instructions for all Copilot interactions:

# Project Conventions

- Use TypeScript strict mode with explicit return types
- Prefer `Result<T, Error>` pattern over throwing exceptions
- All API responses follow our envelope format: `{ data, error, meta }`
- Tests use vitest with the `describe/it` pattern
- Never use `any` — prefer `unknown` with type guards

AGENTS.md — agent instructions that can live anywhere in your repo. Unlike copilot-instructions.md (which must be in .github/), you can place multiple AGENTS.md files at different directory levels — the nearest one in the directory tree takes precedence. This makes it ideal for monorepos where each package needs its own agent behavior:

my-monorepo/
├── AGENTS.md              ← shared team-wide instructions
├── packages/
│   ├── frontend/
│   │   └── AGENTS.md      ← React-specific agent rules (wins here)
│   └── backend/
│       └── AGENTS.md      ← API-specific agent rules (wins here)

Every suggestion Copilot makes now respects these rules. No more "helpful" suggestions that violate your architecture.

MCP Servers: Giving Copilot New Abilities​

Model Context Protocol (MCP) servers let you plug external data sources and tools into Copilot's context. Think of them as APIs that Copilot can call mid-conversation — in both the IDE and CLI.

// .copilot/mcp.json
{
  "mcpServers": {
    "azure": {
      "command": "npx",
      "args": ["-y", "@azure/mcp@latest", "server", "start"]
    }
  }
}

Now Copilot can query your Azure resources, check deployment status, or read your database schema — all within the conversation.

Some MCP servers I use daily:

• Copilot for Azure — query Azure resources, check deployments
• GitHub MCP — deep repo operations beyond what's built-in
• Microsoft Learn MCP — let Copilot read/write files outside the workspace

Skills: Repeatable, Deterministic Work​

Skills are the underrated powerhouse of Level 2. A skill is a directory with a SKILL.md file that defines a repeatable pattern — including deterministic steps from scripts and code.

.<directory>/skills/
├── pr-review/
│   └── SKILL.md        # "Run lint, check test coverage, review diff"
├── doc-sync/
│   └── SKILL.md        # "Compare API surface to docs, flag drift"
└── sdk-sample-check/
    └── SKILL.md        # "Validate all samples compile and match SDK version"

Read the Visual Studio documentation for the best directory location for your skill usage.

Skills differ from instructions in that they define executable workflows — not just preferences. A skill can include shell commands to run, files to check, and decision trees to follow. They're reusable across sessions and agents.

Why skills matter:

• Repeatable — same process every time, no drift
• Composable — skills can reference other skills
• Deterministic where needed — embed scripts and validation steps that always run the same way
• Shareable — check them into your repo, the whole team benefits

Try This Now​

1. Create .github/copilot-instructions.md with your project's conventions
2. Add an MCP server for a tool you use daily (Azure, database, etc.)
3. Create a .github/skills/quick-review/SKILL.md that describes your code review checklist

Then open Copilot Chat or run copilot and notice the difference — it follows YOUR patterns now.

What I Learned at Level 2​

Custom instructions are absurdly high-leverage. A 50-line markdown file eliminates 80% of the "no, not like that" moments. MCP servers bridge "Copilot that knows code" and "Copilot that knows your infrastructure." Skills turn tribal knowledge into executable processes.

The limitation: everything is still per-session. Copilot doesn't automatically carry context between sessions — it won't remember decisions from yesterday's refactor. It doesn't have persistent context about your project's evolving state. It doesn't coordinate with other instances of itself.

Enter Squad.

Level 3: Squad — A Team Working in Concert​

🖥️ VS Code · ⌨️ CLI · 👤 Interactive · 💻 Local

The workshop is getting busy. You're at the bench, studying the blueprint. Around you, a small team of helpers is assembling a cabinet together — one holds the frame, another drives the dowels, another checks the level. Each knows their role. Each stays in their lane. The work moves faster because they each know their job and coordinate with each other, not just with you.

This is where the mental model shifts from "AI assistant" to "AI team."

Squad gives you a team of specialized agents working in concert to complete tasks, where each member's expertise and boundaries positively impact the result. It's not just "named agents with memory" — it's a coordinated system where the reviewer's standards shape the coder's output, and the docs writer's perspective catches gaps the implementer missed.

Squad runs on the Copilot CLI (copilot --agent squad) and adds the organizational layer that makes agents feel like a real team rather than a single assistant wearing different hats.

What Makes Squad Different​

Installing Squad​

# Install Squad CLI
npm install -g @bradygaster/squad-cli

# Initialize in your project
npx @bradygaster/squad-cli init

# Start Copilot with Squad (standalone CLI)
copilot --agent squad

This scaffolds a .squad/ directory:

.squad/
├── agents/
│   ├── ralph/           # Orchestrator
│   │   └── charter.md
│   ├── reviewer/
│   │   └── charter.md
│   └── docs-writer/
│       └── charter.md
├── ceremonies/
│   └── sweep.md
└── memory/
    └── decisions.md

Agent Charters: Expertise + Boundaries​

Each agent has a charter — a markdown file that defines who they are and what they do and, critically, what they won't do:

# Reviewer Agent Charter

## Identity
You are the code reviewer for this project. You focus on:
- Security vulnerabilities
- Performance anti-patterns
- Consistency with project conventions

## What I Own
- TypeScript files and build system

## Boundaries
- Never approve your own changes
- Escalate architectural concerns to the team lead
- Don't refactor code that isn't in the PR scope

The boundaries matter as much as the expertise. A reviewer that knows when to escalate produces better outcomes than one that tries to handle everything. The interplay between agents — where one's output becomes another's input — is what makes Squad feel like a team rather than parallel solo workers.

Ceremonies: On-Demand Structured Workflows​

Ceremonies are repeatable workflows you trigger when needed:

# Ceremonies & Rituals

## Design Review

**When:** Before PRD implementation begins  
**Who:** <list of named agents>
**Purpose:** Validate requirements, issue templates, and process flow before work starts

## Retrospectives

**When:** After major deliveries (GitHub Projects setup, issue templates, Actions automation)  
**Who:** All team members  
**Facilitator:** <single agent name> 
**Purpose:** Reflect on what worked, what didn't, continuous improvement

## Cross-Repo Sync

**When:** As needed  
**Owner:** <single agent name>
**Purpose:** Ensure coordination across all projects (reads repos.json for scope)

Ceremonies encode your team's best practices into executable workflows that any agent can run consistently.

Try This Now​

With the Squad open in a Copilot CLI interactive chat, assign work to the squad.

# Then talk to the team:
"Team, fan out and review this PR for security issues"
"Ralph go"

What I Learned at Level 3​

The agents and charter system is what makes Squad click. With it, you have agents that maintain consistent behavior, remember decisions, and build expertise over time. Without it, you have "Copilot with extra steps."

The real insight: diversity, expertise, coordination, and boundaries create quality.When the reviewer can't approve its own work, when the docs writer must verify against actual code, when the security agent escalates instead of guessing — the team produces better results than any single agent could alone.

The honest trade-off: Squad requires investment in codifying your work patterns and practices. A poorly-defined agent is worse than no agent because it gives inconsistent results. Spend the time upfront.

Level 4: Autonomous Operations​

🖥️ VS Code · ⌨️ CLI · 🤖 Autonomous · 💻 Local · ☁️ Cloud

The helpers are working independently across the shop — each at their own bench, each building a different piece from your specifications. One saws, one hammers, one planes. You glance across the room and trust the work because you wrote clear blueprints for your team of experts. They don't need you hovering.

Level 4 is where the work has been fully defined and you just need it completed. You've already figured out what needs to happen — now you hand it off and let the system execute.

This is the difference between "AI that helps me work" and "AI that does the work I've specified."

Five Ways to Run Autonomously​

1. VS Code Agent Mode​

🖥️ VS Code · 🤖 Autonomous · 💻 Local

In VS Code, Copilot's agent mode executes multi-step tasks — reading files, running commands, editing code — without manual intervention. You describe the outcome, and agent mode figures out the steps:

# In VS Code Copilot Chat (agent mode):
"Refactor all API handlers to use the new error envelope format"

Agent mode uses your custom instructions and MCPs from Level 2, so it already knows your project's conventions. Best for: well-scoped tasks while you're in the IDE.

2. Copilot CLI Agent Mode​

⌨️ CLI · 🤖 Autonomous · 💻 Local

The standalone CLI provides the same autonomous execution outside VS Code:

# CLI agent mode — executes the full task autonomously
copilot -p "Refactor all API handlers to use the new error envelope format"

Best for: well-scoped tasks from the terminal, scripted workflows, or when you prefer the command line over the IDE.

3. "Ralph, go" — Squad Work Queue (In-Session)​

⌨️ CLI · 🤖 Autonomous · 💻 Local

Ralph, the Squad work monitor, processes your entire work queue autonomously within a Copilot session. First, connect Squad to your repo's issues ("pull issues from owner/repo"). Then Ralph triages those issues, assigns work to the right specialist agents, monitors progress, and keeps going until the board is clear:

# In a Copilot CLI session with Squad:
copilot --agent squad

# Then:
"Ralph, go"          # → Starts processing the work queue
"Ralph, status"      # → Shows what's open, stalled, or ready to merge

Ralph monitors GitHub issues, triages incoming work, and drives tasks through your agent team without you intervening. It doesn't stop between tasks — it keeps cycling until everything is done.

Best for: in-session work queue processing, multi-agent coordination, and batching related tasks.

4. Squad Watch — Persistent Local Monitoring​

⌨️ CLI · 🤖 Autonomous · 💻 Local

When you're away from the keyboard but your machine is on, squad watch provides persistent polling of your GitHub issues:

# Polls every 10 minutes (default)
npx @bradygaster/squad-cli watch

# Custom intervals
npx @bradygaster/squad-cli watch --interval 5    # every 5 minutes
npx @bradygaster/squad-cli watch --interval 30   # every 30 minutes

This runs as a standalone local process (not inside Copilot) that auto-triages issues from your connected repo, assigns work based on team roles and keywords, and routes issues to agents or @copilot for pickup. It runs until you Ctrl+C. (Requires the same repo connection set up via Squad.)

Best for: overnight monitoring, catching issues while you're in meetings, and persistent triage between active sessions.

5. Copilot Cloud Agent (GitHub Issues)​

☁️ Cloud · 🤖 Autonomous · 🌐 GitHub.com

Assign a GitHub issue to Copilot and it works independently — no terminal open, no local setup. The cloud agent runs in a GitHub Actions-powered ephemeral environment: it researches your repo, creates a plan, makes code changes, and opens a PR.

Trigger it from a GitHub issue comment:

<!-- In a GitHub issue comment: -->
@copilot implement this

Or assign the issue to Copilot directly from the GitHub Issues UI, VS Code, JetBrains, or the GitHub CLI.

The cloud agent works best for well-scoped, clearly described issues: "add a new endpoint that follows the existing pattern," "write tests for this module," "update the config schema to support the new field." Think of this as a single async task you hand off — describe the outcome clearly, and come back to a PR.

It uses GitHub Actions minutes and Copilot premium requests, so you're trading compute for time. No local session required; the work happens entirely on GitHub's infrastructure.

When to Use Each​

The Autonomy Spectrum​

These options form a spectrum from "I'm here watching" to "I'm asleep":

You're present              You're away              You're asleep
─────────────────────────────────────────────────────────────────
VS Code agent mode    →    squad watch        →    Copilot cloud agent
Copilot CLI           →    (machine on)       →    (GitHub cloud)
Ralph, go                                         

What I Learned at Level 4​

The key insight: autonomous execution requires fully-defined work. The quality of autonomous output is directly proportional to how clearly the task was specified. Vague issues get vague results. A well-written issue with acceptance criteria, examples, and constraints? That's where autonomous execution shines.

The cloud agent on GitHub is the lowest-friction option — no local setup, just assign an issue. squad watch bridges the gap between active sessions and cloud — your machine monitors and triages even when you're not in a Copilot session. Ralph is best when you're actively working through a backlog and want coordinated multi-agent execution.

Finding Your Path​

Not everyone takes the same route through these levels:

The Ecosystem at a Glance​

The Copilot ecosystem is growing fast. Here are the key resources:

Essential Tools​

• GitHub Copilot Extension — the IDE extension (VS Code, JetBrains, etc.)
• Copilot CLI — standalone copilot command for terminal
• Squad CLI — named agents working in concert (npm i -g @bradygaster/squad-cli)

Learning & Community​

• Agentic SDLC Handbook — patterns for AI-first development
• Copilot Insights — measure your Copilot usage
• Awesome Copilot — community-curated extensions, skills, and tools (repo)

Infrastructure​

• Microsoft MCP — Model Context Protocol servers
• Copilot for Azure — Azure resource context in Copilot

What Actually Changed for Me​

I want to be honest about what's different after six months at Level 3+:

What improved:

• PR turnaround dropped from days to hours (the green shirts handle first-pass review)
• Documentation stays in sync with code (sweep ceremonies catch drift)
• I work in unfamiliar codebases with dramatically less ramp-up time
• Boilerplate tasks that used to take 30 minutes take 2 minutes
• Skills encode my best practices — I define a process once, it runs the same way forever

What didn't change:

• Architecture decisions still require human judgment
• Debugging subtle logic errors still requires deep thought
• Agent output needs review — trust but verify
• Writing good charters and instructions is a skill that takes time to develop, update, and improve

The mental model shift: I stopped thinking "what code do I need to write?" and started thinking "what work needs to happen, and who should do it?" Sometimes the answer is me — blue shirt at the bench, swinging the mallet. Often it's a green shirt with clear instructions and a well-scoped task.

Start Today​

You don't need to plan all four levels. Start where you are:

Never used Copilot? → Install the extension, write a comment, press Tab. That's it.

Using Copilot but it's generic? → Write a copilot-instructions.md file and one skill. 10 minutes, massive payoff.

Want more than autocomplete? → Install Squad CLI, write one agent charter, run copilot --agent squad.

Ready for autonomous execution? → Try copilot --autopilot on a well-defined task, or assign an issue to the cloud agent.

The progression is natural. Each level solves a real problem you'll discover at the previous one. And unlike most "AI transformation" pitches, you can validate the value at every step before investing in the next.

The future of development isn't AI replacing developers. It's developers who know how to orchestrate AI systems outperforming those who don't. The tools are here. The ecosystem is open source. The only question is which level you start at.

Want to go further? The next post in this series covers Cloud-Scale Agent Fleets for Level 5 — coming soon.

📣 GitHub Copilot Dev Days — Next Week!​

Want to go deeper? GitHub Copilot Dev Days are happening next week with sessions in multiple languages and time zones:

• 🗓️ May 25, 2026 at 7 PM (BRT) — GitHub Copilot Dev Days Brazil [Portuguese]
• 🗓️ May 26, 2026 at 12 PM (CDMX) — GitHub Copilot Dev Days LATAM [Spanish]
• 🗓️ May 26, 2026 at 7:30 PM (CST) — GitHub Copilot Dev Days 中文版 [Simplified Chinese]
• 🗓️ May 27, 2026 at 9 AM (PST) — GitHub Copilot Dev Days [English]

These are free, virtual events covering the latest in Copilot extensibility, agentic development, and the ecosystem tools discussed in this post. See you there!

Have questions or want to share your own journey? Find me on GitHub at @dfberry or check out my other posts on the Copilot ecosystem.