Whenever people talk about AI, they highlight the flashiest use cases like fully coded apps built by agents or cinematic video generation. Those things are certainly cool, but most days I'm just delegating mundane tasks to the bots.
Today, I didn't build an app. I didn't write a screenplay. I just got stuff done.
Here are 5 real, everyday tasks I gave to my AI agent, Goose, that saved me hours. None of them took more than one minute from prompt to result.
For all of these, I used Anthropic's Claude 4 Sonnet
1️⃣ Summarizing GitHub Activity into Actionable Insights
Task
I asked Goose to review all closed GitHub issues across my organization for the month and give me a breakdown. I wanted to see where our time went, how work was distributed, and any patterns or dependencies across projects.
Result
In under a minute, Goose gave me a report with productivity metrics, workload distribution, and notable dependencies between issue threads (e.g. one fix blocking another).
This kind of synthesis normally requires me to manually scan a bunch of repos and cross-reference PRs or issue comments. Not today.
MCPs used
2️⃣ Extracting Action Items from a Long Slack Thread
Task
You know when a Slack thread starts as a quick brainstorm and somehow grows into a novel? Ours had 169 replies today 😂, and buried in there were some important ideas.
So, I asked Goose to analyze the entire thread and extract a clean list of action items.
Result
In one minute, I had a focused to-do list with responsible parties, deadlines (when mentioned), and themes. These takeaways will likely shape our Q3 goals, and when I'm ready, I can even have Goose go create GitHub issues for all of them!
MCPs used
- Slack
3️⃣ Creating a Roadmap from Community Feedback
Task
Our Goose community is active across GitHub, Slack, and Discord. There's tons of feedback, but it's scattered.
I had Goose pull and analyze open questions, bug reports, feature requests, and discussion threads across all three platforms.
Results
A ranked list of the top 10 items we need to address, including a short description of each issue along with the estimated effort of the tasks. This gave us a nice jumpstart on our roadmap planning.
MCPs used
4️⃣ Fixing My CSS Breakpoints (Because I Gave Up)
Task
Confession: CSS and I are not friends. After 30 minutes of fighting with breakpoints, spacing, and container widths, I gave the problem to Goose by showing it a screenshot of the page.
Result
Goose spotted the issue immediately and rewrote my media query logic as well as some other key CSS I was missing.
MCPs used
5️⃣ Fixing Broken Links After a Big Doc Restructure
Task
I restructured a big internal doc set and needed to update all internal links, reroute old paths, and make sure nothing was broken.
I handled the restructure manually (it was delicate so I wanted to do it myself), then asked Goose to crawl the doc, find broken or outdated links, fix them and add redirects where needed.
Result
No broken links leading to 404 errors. Just tidy documentation.
MCP used
Most AI posts show off what's possible. I'm focused on what was promised.
The whole point was to offload the tedious stuff so we could focus on the work that actually matters, and that's exactly what I'm using AI for.
What everyday tasks are you delegating to AI agents? Drop a comment!
Top comments (9)
This is the kind of task where AI might have a place: Not fighting on the front, building the actual products where every minor imperfection can have massive impacts; but in meta-tasks, where a heuristic "good enough" is already the default.
Specially things like compiling and summarising information seem to be something LLMs (note how "Language" is even in their name) seem quite good at.
100%
Hi Angie love the fact that you use all those MCPs!
I'm curious, how do you make sure all the actions are observable and auditable?
Are you like using centralised MCPs for the whole team so you can apply fine-grainde access control rules? How do you make sure Goose just has the permissions it need for the specific task and not more?
Langfuse! block.github.io/goose/docs/tutoria...
That makes a lot of sense for monitoring and audit!
But what about Fine-grained access control? How do you make sure Goose just has the permissions it need for the specific task and not more?
some of the MCP servers like GitHub have granular scopes. But even if not, within Goose, we allow users to assign permissions to each tool within an MCP server
This is one of the reasons we love Goose!
However, what I meant was truly fine-grained authorizations at an "argument" level.
We're noticing, just like with standard apps, that companies would like to give Agents specific permissions.
Something like: "read_message_tool" only for "work" emails, or "send" only "during working hours".
Those permissions need to be changed live, just as access to a G Drive Doc for humans, based on the task the Agent is doing. This is necessary for lot of companies we're speaking with, as they do not want to just let the Agent inherit ALL the permissions from the human it is acting on behalf of.
Have you ever thought about that? I'd love to discuss that with you
I totally vibe with using AI for this kind of grunt work - it's a lifesaver when I'm wrangling random feedback from Google Sheets, Notion, and Discord all at once.
What do you use to connect your agent with all these platforms?
yes exactly! I use MCP servers