investigating-in-parallel
Skill0 starsChanged 46 days agoMITView on GitHub
Use when facing several independent problems, such as unrelated test failures or separate subsystems, that could be investigated at the same time without sharing state
In this repository13 other files it gives its agents.
- debugging-systematicallySkill
- delegating-to-subagentsSkill
- executing-plansSkill
- finishing-workSkill
- isolating-workspacesSkill
- planning-workSkill
- receiving-reviewSkill
- requesting-reviewSkill
- shaping-designsSkill
- testing-firstSkill
- using-tackleSkill
- verifying-before-claimingSkill
- writing-skillsSkill
The file
We split this skill into the rules it gives an agent
1 rule. About 0 are common, 1 is distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
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 8
When problems are truly independent, one focused agent per problem finishes faster than working through them in sequence, and each agent …
≈ “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
Investigating in Parallel
When problems are truly independent, one focused agent per problem finishes faster than working through them in sequence, and each agent stays focused on its own problem.
Scale it
- Two small problems: usually faster to handle yourself in sequence.
- Three or more independent problems, or slow investigations: dispatch in parallel.
How
- Group by root domain, not by symptom. Failures in the same subsystem, or ones that could share a cause, belong to one agent.
- Check independence. Would the agents edit the same files or share a resource (database, port, fixture)? If so, run them in sequence.
- Give each agent a self-contained brief:
- Scope: one file, subsystem or failure set
- Evidence: the exact error messages and test names
- Constraints: what it must not change
- Output: root cause, what it changed, and how it verified the change
- Dispatch together, then integrate: read each summary and diff, look for overlapping edits, and run the full suite on the combined result.
Skip or shorten when
- The failures might be related. Investigate together first, because one fix may clear several.
- You don't yet know what's broken. Exploration needs one coherent view.
- The harness has no subagents. Work through them in sequence using the same briefs as a checklist.
Done means
Each agent's change has been read, conflicts are resolved, and the full suite passes on the combined result.
Next
debugging-systematicallyfor any problem an agent couldn't explainverifying-before-claiming
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.

