What is ai-productivity-ADO?

A set of project rules for recording how AI-assisted work was done in an ADO item. ADO means Azure DevOps, a platform for planning work and tracking development tasks.

Use it when updating ADO fields such as Prompt Used, Plan Used, Lessons Learned, AI Productivity, and Discussion after an AI-assisted task.

It keeps the original prompts, implementation details, lessons learned, suggested AI usage, and effort information documented for later review.

ai-productivity-ADO — Cursor rule for Cursor by sudhasubash1990

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Clone the repo

git clone --depth 1 https://github.com/sudhasubash1990/Salesforce-Enterprise-skills

Made for: Cursor.

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.

README.md

[![agentmods](https://agentmods.dev/badge/rules/sudhasubash1990/salesforce-enterprise-skills/ai-productivity-ado/github.svg)](https://agentmods.dev/rules/sudhasubash1990/salesforce-enterprise-skills/ai-productivity-ado)

Your own site

<a href="https://agentmods.dev/rules/sudhasubash1990/salesforce-enterprise-skills/ai-productivity-ado"><img src="https://agentmods.dev/badge/rules/sudhasubash1990/salesforce-enterprise-skills/ai-productivity-ado/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.

Your own site · 80×15

<a href="https://agentmods.dev/rules/sudhasubash1990/salesforce-enterprise-skills/ai-productivity-ado"><img src="https://agentmods.dev/badge/rules/sudhasubash1990/salesforce-enterprise-skills/ai-productivity-ado.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 519 This file is loaded in full into every session.

When invoked 519 The same file — it is already loaded in full.

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.

Model	Per session	Once invoked
Fable 5.1	$0.00519	$0.00519
Opus 5	$0.00260	$0.00260
Sonnet 5	$0.00104	$0.00104
Haiku 4.5	$0.00052	$0.00052

Measured 11d ago against content hash 0a9c6ce247a6, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

ai-productivity-ADO 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 11d 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.

.cursor/rules/ai-productivity-ADO.mdc · 62 lines

How it starts

The opening of the file, as written. The whole thing — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Prompt Used field update on ADO

The prompt updated in prompt field should be exactly same as what user typed and if there is any rework prompts that also to be captured in "Prompt Used" field on
Also add how the user can reshape that prompt in better way next time (Cursor suggesting prompt) so that other user can use that as reference to input better prompt next time.
Also suggest user which AI model they can use next time for similar kind of prompt.
suggest the mode they can use that prompt out of given (Plan, Agent, Debug, multi task, Ask )
If they can use multiple prompt one for plan and one for agent also mention those details which would be better or if user can try single mode of prompt

Plan Used

summarise all technical and functional, configuration details used to implement the prompt.

Lessons Learned

Whatever lessons learned by cursor has to be given as detailed instructions to user on this field. so that if user manually do that next time, they should make sure all these are considered.

AI Productivity Calculation Rules

Before updating AI Productivity fields:

Ask user to input Traditional Effort based on task complexity as this varies from person to person
Calculate Actual Effort based on:
- Prompt creation time
- AI review time
- Validation time
- Rework time
Actual Effort = Prompt Time + Review Time + Validation Time + Rework Time
Hours Saved = Traditional Effort - Actual Effort
AI Contribution % = (Hours Saved / Traditional Effort) * 100
Confidence Rating: 1 = Low 2 = Moderate 3 = Good 4 = Strong 5 = Very Strong
Rework Required: None - if user doesnt ask to change anything once job completes Minor - if there is rework done by cursor on re-prompt is 25% Moderate - if there is rework done by cursor on re-prompt is 40% Major - if there is rework done by cursor on re-prompt is 60%

Provide reasoning before updating fields.

Discussion section on ADO:

Read the full file on GitHub · 62 lines

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.

11d ago First seen · 62 lines · 519 tokens per session scan A 0a9c6ce247a6

Subscribe to this mod's changes

ai-productivity-ADO is a cursor rule published in the GitHub repository sudhasubash1990/Salesforce-Enterprise-skills (3 stars, last pushed 23d ago), licensed MIT. It adds 519 tokens to every session, about $0.0026 per session on Opus 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 2026-08-31.

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ai-productivity-ADO