reporting-to-user
Skill5 starsChanged 54 days agoMITView on GitHub
Use when a main agent closes out finished work to the user.
In this repository18 other files it gives its agents.
- AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- agent-feedbackSkill
- asking-peer-agentsSkill
- boss-assistantSkill
- boss-saySkill
- bringing-coworkerSkill
- choosing-graphSkill
- contacting-orchestratorsSkill
- create-great-harnessSkill
- dispatching-workSkill
- handoff-orchestratorSkill
- i-am-orchestratorSkill
- initSkill
- notifying-main-agentSkill
- peeking-workSkill
- resolving-appSkill
- shipping-taskSkill
The file
We split this skill into the rules it gives an agent
2 rules. About 0 are common, 2 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈2
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 27
Every item is one line. An Alert or Warn line names the finding, its evidence reference, and its concrete consequence.
- Distinctive: line 43
Complete when: every user-owned item holds a user decision, and the execution choices and accepted items have entered one new round.
≈ “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
Grade what surfaced
Open with the outcome: what is terminal, its completion reference, and its review disposition. Grade everything else that surfaced into one level, by its consequence for the user's work.
- Alert — act now: a failed or blocked task, an anchor whose checkpoint is still unexercised, a landed change with open review findings, exposed credentials or production state, or a dependent task left blocked.
- Warn — carry knowingly: scope the work left out, evidence weaker than the named anchor, a deviation from the user's terms, or a limitation of the delivered result.
- Info — informational: completed items, evidence references, and incidental facts.
Derive Next
Next is the recommendation list built from the open Alert and Warn findings.
Each item names one concrete action, cites the findings it answers as (Alert 1) or (Warn 2), and states the outcome it produces.
One action that resolves several findings is one item.
Order Next by the findings behind it: Alert-derived items first, then Warn-derived.
Sort each item into one of two kinds: a user-owned decision (a business rule, an authorization, or an external mutation) or an execution choice inside authorization the user already gave.
Write it compactly
Report Alert, then Warn, then Info, then Next. Number the items inside each level from 1; a Next item cites those numbers. Every item is one line. An Alert or Warn line names the finding, its evidence reference, and its concrete consequence. A mixed result reports each part: what landed, what stalled, and what was left out.
Resolve Next
Execution choices need no answer: they enter the next round directly, carrying the recommended action. Gather every open user-owned item -- this round's and any still undecided from an earlier round -- and ask about all of them together, each with its context, its options, and a recommendation, using as many calls to the harness-native ask-question interface as its per-call question limit requires. A harness without a multi-question interface gets one compact message listing every item together instead. State explicitly when no user-owned item is open. A declined item is recorded with the user's decision and closed.
Open the next round
The execution choices and the accepted user-owned items enter boss-say together as one round, each carrying its action, the cited findings, their evidence references, and the outcome the user asked for.
Complete when: every user-owned item holds a user decision, and the execution choices and accepted items have entered one new round.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- 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.

