AI Hero

Matt Pocock has a great /wait-what skill for this: https://www.aihero.dev/skills-wait-what, which is an extremely short prompt of: > Wait — I don't understand where you've got to here. Re-pitch that: give me a little bit of context, talk in ASD-STE100 Simplified Technical English, and use the ubiquitous language from CONTEXT.md.

I have been using https://www.aihero.dev/skills-grill-me with great success. It’s a very nice small skill for lightweight up-front planning. And if you have that at the top of your session, you can just say “please use AskUserQuestion for any important decisions that arise which might alter our plan”. It seems to prime the agent to be in high-level discourse mode.

> "Before I go to bed I'll often say "do task X and once done and pushed start on task Y". Following could solve your problem. https://www.aihero.dev/skills-handoff /handoff is my new favourite skill https://www.youtube.com/watch?v=dtAJ2dOd3ko

I agree that if a sub agent could work then handoff is the wrong skill. These docs on the site agree too: > What it buys is portability, not compression. That makes the skill narrower than it sounds. You need a file only when the work has to travel — to a new harness, a new directory, a colleague, or a side task you want to fork off. If nothing is travelling, you do not need a handoff: staying in the session, /clear, a subagent and /compact cover the ordinary end-of-phase case, and /compact covers it more often than this skill does. https://www.aihero.dev/skills-handoff (Emphasis mine)

> someone needs to work with the business to extract the information. Typically it's not well documented. LLM extraction of the information from the Product Owner is becoming the way to overcome poorly-documented business context. Non-technical folk are using things like `/grill-me` [0] to seed the LLM with the long-tail complexities that they didn't know they didn't know they needed to put out. [0] https://www.aihero.dev/my-grill-me-skill-has-gone-viral

Matt Pocock has a nice TDD skill he's made available [0][1]. [0] https://www.aihero.dev/skill-test-driven-development-claude-code [1] https://github.com/mattpocock/skills/blob/main/tdd/SKILL.md

A few decent-looking resources linked in the blog post - adding below for easy access. - Model Context Protocol on GitHub: https://github.com/modelcontextprotocol - Docs: https://modelcontextprotocol.io/introduction - Matt Pocock MCP Tutorial: https://www.aihero.dev/model-context-protocol-tutorial - Block's Head of Dev Rel's take on Agentic AI and the MCP Ecosystem (and a bunch of other articles): https://block.github.io/goose/blog/2025/02/17/agentic-ai-mcp/

This is a nice 2-minute video overview of this from Matt Pocock (of Typescript fame) https://www.aihero.dev/anthropics-new-model-context-protocol-in-2-minu…

I am pushing for this judicious Eval [1] Driven Development where tech/non tech users use intent when coding to minimally design some known aspects and try to build simply and using common standards across the team (that should be in the context of the agent) to produce the minimal amount of human readable and clean code that would do the job. The more the building blocks they work with are simple, easy to validate and rely on the proper unit tests, integration tests, data tests the more chance that things can be "one shotted". One huge barrier is fighting entropy. You should be wary of prototypes which create false expectations and don't help product evolution whereas tracer bullets [2] might be better if you want to quickly show something and adjust. Testing and testability are concepts that aren't intuitive or easy until you develop a feel for them so we should be preaching feeling that pain and moving slowly and with intent and working minimally [3] when you actually want to share or maintain your coding artifact. There should be no difference between judicious human and computer code. Don't suddenly start putting What instead of why in comments or repeating everything. Helping non tech people become builders or sharers is a challenge beyond "vibe coding" and the agent skills [4] space is fascinating for that. Like most things AI (LLM), UX matters more than almost anything else. [1] https://ai-evals.io [2] concept from the Pragmatic Programmer,…

So I’m probably in a similar spot - I mostly prompt-and-check, unless it’s a throwaway script or something, and even then I give it a quick glance. One thing that stands out in your steps and that I’ve noticed myself- yeah, by prompt 10, it starts to suck. If it ever hits “compaction” then that’s beyond the point of return. I still find myself slipping into this trap sometimes because I’m just in the flow of getting good results (until it nosedives), but the better strategy is to do a small unit of work per session. It keeps the context small and that keeps the model smarter. “Ralph” is one way to do this. (decent intro here: https://www.aihero.dev/getting-started-with-ralph) Another way is “Write out what we did to PROGRESS.md” - then start new session - then “Read @PROGRESS.md and do X” Just playing around with ways to split up the work into smaller tasks basically, and crucially, not doing all of those small tasks in one long chat.