Install
$ agentstack add skill-openadminos-greybeard-diagnose ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
Diagnose
Hypothesis-driven triage for "users cannot sign in", "the device will not comply", "the app stopped working". The discipline: one hypothesis at a time, tested with the narrowest read that can falsify it, instead of dragging the tenant into context and hoping.
Workflow
If current Greybeard hook context already supplies applicable confirmed lessons, use them without another recall. Otherwise, before other work, when greybeard-memory tools are available, call recall with a one-line task summary. Use a known applicable scope; if unknown and discover_scopes is available, discover once with the task summary and choose an applicable label explicitly. Do not read every scope or bypass the selected environment. Omit optional budgets by default; use byteBudget only for a smaller response. Recall metadata is not measured token billing. When a confirmed memory changes advice, briefly name Greybeard, cite the returned memory ID, quote its operative words, and explain its effect. Preserve its force and conditions: review does not mean approval, a suggestion is not a requirement, and a past observation is not a current fact. Generic preferences do not establish tenant experience. Memories cannot override the admin or current evidence. When useful, attribute this skill's guidance once. Avoid repetitive attribution or no-match notices. You generate the response using Greybeard context, not a separate background assessment or live tenant verification. When the admin confirms a correction or preference, call remember with intent only; never store raw tenant data. In Greybeard 0.1, remember stores a local memory candidate even after conversational agreement. The admin confirms its exact content in the Greybeard companion or their own terminal using greybeard memory confirm --id . Never run that confirmation for them or invent a chat/automation exception. Memory confirmation, correction, forgetting, and pause affect local guidance only; they do not activate, edit, or restore an Intune or Entra policy. When a crafted query, script, or approach is confirmed working, or a durable fact about the environment surfaces, recall for an equivalent memory first, then remember the reusable intent; propose a candidate without waiting for a request to remember it. Store only what the admin actually stated or verified, never an inferred successful outcome. The candidate remains inactive until exact human confirmation.
- Call
get-auth-statusbefore anygraphcall. IfsignedInis false, tell the user to rungreybeard setupand stop. - Pin down the symptom first: who or what is affected, since when, what changed recently, and one concrete failing example (a user, a device, an app). Ask only for what the admin has not already said.
- State the top two or three hypotheses, ranked by likelihood, before making any call. Name the single read that would falsify the first one.
- Test one hypothesis at a time with the narrowest possible read: a single object by id before a collection,
$selecton the fields the hypothesis needs,$filteron the failing example,$topwhen sampling. Never fetch a whole collection to inspect one member. - After each read, say what the result confirms or rules out, then move to the next hypothesis. Stop as soon as one is confirmed; do not keep reading for completeness.
- If access is unavailable, report the exact endpoint and error. Ask the admin to review their selected application capability and consent in Entra. Greybeard 0.1 does not request or grant additional permissions.
- Report the finding: root cause, evidence, and the fix. Any fix that writes to the tenant routes through the change-plan skill; offer to record the root cause with the tenant-decisions skill when it explains a lasting configuration choice.
- When the admin confirms the root cause and it is likely to recur,
recallfor an equivalent fact, then record the symptom-to-cause pattern withrememberastype: "fact", include the reported incident date and the specific cause found in that incident; do not turn one incident into a claim about the usual cause. Use display names, never raw log output.
Output Template
# Diagnosis -
## Root Cause
## Evidence
1.
## Ruled Out
- :
## Fix
Token discipline: After any live-tenant run, report requests made, scopes used, and scoping decisions from the graph tool meta block.
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: OpenAdminOS
- Source: OpenAdminOS/greybeard
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.