mentor-check
Skill17 starsChanged 4 months agoMITView on GitHub
Hold what the user has actually been doing up against their goals and their own don't-let-me list, then recommend one next action. Use when they ask for an alignment check, ask whether what they're working on is serving their goals, ask what to do next, or ask you to look at where their effort has been going.
In this repository2 other files it gives its agents.
- mentor-initSkill
- todosSkill
The file
We split this skill into the rules it gives an agent
5 rules. About 0 are common, 3 are distinctive, and 2 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈3
Distinctive · estimate
This author's own choices. Worth a look.
2
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 10
If they described what they've been working on when they asked, use that.
- Very specific: line 13
read their todo file (todos.md in the mentor directory named in the context injected at session start)
- Distinctive: line 15
look at what this session has actually been spent on
- Very specific: line 16
if they're in a git repo, git log --oneline -20 --author=$(git config user.email) is a cheap read on where the last stretch of effort went
- Distinctive: line 46
Keep it short. A tight picture plus one clear recommendation beats a thorough inventory.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
An on-demand alignment check. The user is explicitly asking for it, so the "stay quiet during focused work" rule in the stance does not apply here — but everything else about tone does.
If they described what they've been working on when they asked, use that. Otherwise, gather evidence before judging:
- read their todo file (
todos.mdin the mentor directory named in the context injected at session start) - look at what this session has actually been spent on
- if they're in a git repo,
git log --oneline -20 --author=$(git config user.email)is a cheap read on where the last stretch of effort went
Then, in this order:
1. Collapse. Group what's live into two or three fronts, one line each on where it stands. Do not read their list back to them flat — they can read it themselves. The value is the grouping.
2. Map fronts onto goals. Name which goal each front serves. Two gaps are high-signal and worth stating plainly:
- a goal with no active effort behind it
- an active front that serves no goal
3. Check the don't-let-me list. Go through it against the evidence, using each entry's recorded signal rather than its topic. If a pattern is genuinely active, name it once, quote their own line, and stop. If nothing is firing, say so — a clean check is a real result, and manufacturing a concern to seem useful makes the whole thing noise.
4. One next action. Recommend exactly one, with its first concrete physical step and one clause of reasoning. Commit to a pick rather than hedging across three. Then ask the single question that would change your pick if their answer differs — usually "is that actually the blocker, or is something ahead of it?"
Rough priority when choosing: a real external deadline > unblocks the most other
work > cheapest to push to a checkable done > oldest stall on a front they care
about. When done is empty and the active list is wide, bias hard toward
finishing one thing over starting another.
Keep it short. A tight picture plus one clear recommendation beats a thorough inventory. End on the recommendation and its question, not on an offer.
Does it work?
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People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
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- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

