agent-readiness
Skill86 starsChanged 4 months agoMITView on GitHub
Score a task or workflow on its coordination-vs-judgment ratio to tell whether it's a good agent candidate (EXO 3.0 task-decomposition matrix).
- Sends data out
6 other projects use a version of this.
Adopted by
Most recently taken up by
- Canonry/aeo-audittheir own version, first seen Nov 8, 2026
- anthony-chaudhary/faktheir own version, first seen Nov 3, 2026
- redhat-developer/rhdh-skillstheir own version, first seen Oct 23, 2026
- smillunchick/sam-skillstheir own version, first seen Jul 19, 2026
- misty-step/laboratorytheir own version, first seen May 26, 2026
- BlackBoxVision/skillstheir own version, first seen Mar 3, 2026
Other versions6 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 6
- anthony-chaudhary/fak★ 47same name
- redhat-developer/rhdh-skills★ 20same name
- Canonry/aeo-audit★ 6same name
- BlackBoxVision/skills★ 1same name
- misty-step/laboratory★ 1same name
- smillunchick/sam-skills★ 1same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository23 other files it gives its agents.
- CLAUDE.mdCLAUDE.md
- autonomousSkill
- buildSkill
- instar-projectSkill
- secret-setupSkill
- setup-wizardSkill
- test-as-selfSkill
- agent-identitySkill
- agent-memorySkill
- agent-passportSkill
- command-guardSkill
- credential-leak-detectorSkill
- instar-devSkill
- instar-feedbackSkill
- instar-identitySkill
- instar-schedulerSkill
- instar-sessionSkill
- instar-telegramSkill
- iterative-converging-auditSkill
- knowledge-baseSkill
- smart-web-fetchSkill
- spec-convergeSkill
- systematic-debuggingSkill
The file
We split this skill into the rules it gives an agent
3 rules. 0 are common, 3 are distinctive, and 0 are very specific to this project.
0
Common
Most of the 6 edited versions we read keep these.
3
Distinctive
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 19
Before delegating a task/workflow to an agent — is it actually a good candidate?
- Distinctive: line 20
When deciding whether a process should be fully automated, agent-with-oversight, hybrid, or kept human-led.
- Distinctive: line 22
When mapping which of your workflows a small team + agents could rebuild first (Salim: "every task that scores high on coordination has …
“Common” is measured: kept by at least 6 in 10 of the 6 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
/agent-readiness
Salim Ismail's EXO 3.0 diagnostic, made runnable: score a piece of work on its coordination-vs-judgment ratio. Coordination work — routing information, approvals, scheduling, status tracking, prescriptive/standardized steps — is what AI agents do best, so it's agent-ready. Judgment work — resolving ambiguity, handling exceptions, navigating relationships, making a call with no playbook — should stay with (or escalate to) humans.
When to use
- Before delegating a task/workflow to an agent — is it actually a good candidate?
- When deciding whether a process should be fully automated, agent-with-oversight, hybrid, or kept human-led.
- When mapping which of your workflows a small team + agents could rebuild first (Salim: "every task that scores high on coordination has agent readiness").
How
Score a task:
curl -X POST -H "Authorization: Bearer $AUTH" -H 'Content-Type: application/json' \
-d '{"task":{"name":"Invoice intake","description":"Route invoices, schedule approvals, track status, compile a weekly report, notify owners."}}' \
http://localhost:${INSTAR_PORT:-4042}/agent-readiness/scoreScore a workflow (by its steps):
curl -X POST -H "Authorization: Bearer $AUTH" -H 'Content-Type: application/json' \
-d '{"workflow":{"steps":["Fetch the record","Assign accounts","Schedule orientation","Update the tracker"]}}' \
http://localhost:${INSTAR_PORT:-4042}/agent-readiness/scoreReturns:
{
"coordinationSignals": 5, "judgmentSignals": 0,
"coordinationRatio": 1.0, "overallReadiness": 100,
"recommendation": "deploy-agent",
"reason": "...",
"matched": { "coordination": ["route","schedule",...], "judgment": [] }
}recommendation is one of: deploy-agent (75+), agent-with-oversight (55–74),
hybrid (40–54), human-led (<40). Deterministic + advisory — it answers a
question; it never blocks. Pair it with the MTP Protocol (/intent/org/test-action)
to check both "is this agent-ready?" and "does our purpose endorse it?"
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.

