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cates: Command for GitHub Copilot

.github/prompts/sdlc-implement.prompt.md

sdlc-implement is a command for GitHub Copilot from microsoft/cates. It costs 10 tokens per session (105 once invoked), scanned A, original, MIT.

An implementation command for carrying out an accepted software task with a focused code change, regression tests, documentation updates, and validation evidence. A regression test checks that a previously working behaviour does not break again.

In plain words
What is it for?
Use it to implement an issue or approved plan, run targeted tests and the project's full verification command, inspect the final difference, and prepare a pull-request description.
Why use it?
It encourages the smallest complete change and checks the final result against every acceptance criterion. It also records compatibility risks and does not publish or merge work without permission.

Command for GitHub Copilot ✓ vendor

Written for GitHub Copilot: a Copilot chat mode or prompt. Also seen: agent in frontmatter.

This is microsoft/cates's own configuration. It tells GitHub Copilot how to work on cates itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything cates configures →

not rated 15repo yesterday A scan 10 tokens original MIT
View source ↗ microsoft/cates
About the project

CATES is a vendor-neutral analyzer for measuring coding-agent configurations against token-efficiency, security, and conformance rules. Teams use its command-line tools or Docker image to inspect instructions, prompts, MCP settings, hooks, and editor configuration, and to apply selected lossless optimizations.

microsoft/cates · 15 stars · on GitHub

Reuse

Borrowing it

Nothing to install: this file belongs to microsoft/cates. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/microsoft/cates/main/.github/prompts/sdlc-implement.prompt.md
Clone the repo
git clone --depth 1 https://github.com/microsoft/cates

Made for: GitHub Copilot.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for sdlc-implement

README.md
[![agentmods](https://agentmods.dev/badge/commands/microsoft/cates/sdlc-implement/github.svg)](https://agentmods.dev/commands/microsoft/cates/sdlc-implement)
Your own site
<a href="https://agentmods.dev/commands/microsoft/cates/sdlc-implement"><img src="https://agentmods.dev/badge/commands/microsoft/cates/sdlc-implement/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for sdlc-implement

Your own site · 80×15
<a href="https://agentmods.dev/commands/microsoft/cates/sdlc-implement"><img src="https://agentmods.dev/badge/commands/microsoft/cates/sdlc-implement.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 10 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 105 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00010 $0.00105
Opus 5.5 $0.00004 $0.00042
Sonnet 5.5 $0.00002 $0.00021
Haiku 4.5 $0.00001 $0.00011

Measured 3d ago against content hash a453168281b6, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

sdlc-implement scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.github/prompts/sdlc-implement.prompt.md · 12 lines

What it actually says

Implement the user's accepted task or issue contract. Reuse existing components. Add a failing regression test when practical, make the smallest complete change, update directly related documentation, and run targeted tests followed by npm run verify. Inspect the final diff. Return evidence against each acceptance criterion, actual validation results, compatibility risks and a PR description using the repository template. Do not push, merge or publish without authorization.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 3d ago First seen · 12 lines · 10 tokens per session scan A a453168281b6

Subscribe to this mod's changes

sdlc-implement is a command published in the GitHub repository microsoft/cates (15 stars, last pushed yesterday), licensed MIT. It adds 10 tokens to every session and 105 once invoked, about $0.0000 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-11-08.

Code reviewTDDTesting
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