Graph Patterns

Graph Patterns

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.

Read references/graph-mechanics.md before answering. Use it to design exact, low-waste Graph calls that default to beta, narrow fields, server-side filtering, safe pagination, and all-GET batching. Use explicit apiVersion: "beta" for every Greybeard Graph request.

When discovering delegated permissions on a service principal, prefer publishedPermissionScopes and fall back to oauth2PermissionScopes for compatibility with older response shapes. Do not confuse either property with application roles in appRoles.

When advising live Greybeard runs, route reads through graph and writes through change-plan. Do not suggest direct non-GET graph calls.

After the admin confirms a recommended call pattern works in their environment, recall for an equivalent query memory, then record it with remember as type: "query": the endpoint, the query options, and why that shape was needed. Never store response payloads.

Output

Return the recommended request shape, the reason for each query option, expected failure modes, and the token-discipline choices.

Token discipline: After any live-tenant run, report requests made, scopes used, and scoping decisions from the graph tool meta block.

Bounded tenant read recipes

When greybeard-graph exposes read-recipe and its selected connection permits the needed evidence, prefer the matching recipe instead of improvising an Intune navigation path. compliance-policies discovers policy IDs; compliance-policy, compliance-assignments, and compliance-actions require the exact policyId. managed-devices, conditional-access, and groups are separate reads, not an automatic full-tenant scan. Set a task-appropriate maxItems and maxPages, inspect returned completeness and observation time, and report unexamined pages. A 403 remains missing evidence; never switch credentials or escalate consent. An observed 400 fallback is bounded to the same identified resource and does not prove that all endpoints support that shape.

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.