Acronis logo

Acronis

Organization
Servosity
acronis

The first real CLI for the Acronis Cyber Protect Cloud platform - every tenant, agent, and usage metric mirrored locally, with cross-tenant rollups no single API call returns. Trigger phrases: `acronis backup health across tenants`, `find offline acronis agents`, `acronis usage and billing report`, `which acronis customers aren't protected`, `acronis agent version compliance`, `use acronis cyber protect`, `run acronis-cli`.

Overview

PublisherServosity
Repositorymsp-skills
Skill nameacronis
Stars
68
Forks
14
Bundled files
306
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 306 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Servosity on GitHub. Read the source before you install it.

Installation

Install the Acronis AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Servosity/msp-skills/tree/main/skills/acronis
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Servosity/msp-skills.git /tmp/msp-skills
mkdir -p .claude/skills
cp -r /tmp/msp-skills/skills/acronis .claude/skills/acronis
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Acronis in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Acronis on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Acronis is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Acronis Cyber Protect Cloud - Claude Code Skill

Prerequisites: Install the CLI

This skill drives the acronis-cli binary. You must verify the CLI is installed before invoking any command from this skill. If it is missing, install it first:

  1. macOS / Linux:
    bash
    bash <(curl -fsSL https://raw.githubusercontent.com/Servosity/msp-skills/main/skills/acronis/install.sh)
  2. Windows (PowerShell):
    powershell
    iwr -useb https://raw.githubusercontent.com/Servosity/msp-skills/main/skills/acronis/install.ps1 | iex
  3. Verify: acronis-cli --version
  4. Ensure ~/.local/bin (macOS / Linux) or %LOCALAPPDATA%\Programs\msp-skills (Windows) is on $PATH.

The installer downloads the acronis-cli and acronis-mcp binaries into ~/.local/bin (macOS / Linux) or %LOCALAPPDATA%\Programs\msp-skills (Windows). It does not register the skill with your agent and writes no MCP client config - see mcp-install.md for that wire-up.

If --version reports "command not found" after install, the runtime cannot see the binary directory on $PATH. Do not proceed with skill commands until verification succeeds.

Manage your whole MSP estate from one Go binary: tenants, users, agents, offering items, usage, billing reports, tasks, and activities - all synced to a local SQLite store. Then answer questions the Acronis console can't: which customers' backups failed last night (health), which agents went silently offline (agents stale), and where you're billing for protection that isn't running (coverage --unprotected).

When to Use This CLI

Use this CLI when you manage Acronis Cyber Protect Cloud as an MSP and need answers across many customer tenants at once - fleet backup health, offline-agent sweeps, agent-version compliance after a rollout, usage/billing reconciliation at month-end. It is also the right tool for scripting Acronis provisioning and reporting without re-implementing token exchange, pagination, and the datacenter-host dance in every script.

Unique Capabilities

Cross-tenant rollups

  • health - See backup success / failure / stale across your entire book of customer tenants in one table.

    Reach for this when an agent or tech needs the one-screen 'which customers' backups failed last night' answer the Acronis console can't give across tenants.

    bash
    acronis-cli health --agent
  • agents stale - List backup agents that haven't checked in within a threshold, across every tenant, sorted by customer.

    Use this to catch silently-offline agents before the customer calls - the most common MSP backup-failure root cause.

    bash
    acronis-cli agents stale --older-than 7d --agent
  • alerts repeat - Rank resources and tenants by how many distinct days in a window had a failed or missed backup.

    Use this to separate one-off backup blips from chronically-failing resources that need real remediation.

    bash
    acronis-cli alerts repeat --days 14 --agent
  • failures - Flat list of every failed or missed backup task across all tenants in a recent window, newest first.

    Reach for this when the question is 'show me each backup that failed last night' as individual actionable rows, not rollup counts.

    bash
    acronis-cli failures --since 24h --agent
  • freshness - Time since the last successful backup per tenant, flagged against an SLA threshold - including tenants never backed up.

    Reach for this when an agent needs SLA breach detection: which customers have gone too long without a good backup.

    bash
    acronis-cli freshness --sla 48h --breached --agent
  • customer - One cross-resource snapshot of a single customer: tenant record, users, licenses, usage, agents, and 7-day backup outcomes joined.

    Reach for this before a customer call: the full per-customer picture in one command instead of six console drill-downs.

    bash
    acronis-cli customer TENANT_ID --agent

Billing & licensing

  • reconcile usages - Flag usage with no matching offering item and offering items with zero usage, per tenant.

    Reach for this at month-end to catch under-billing (usage with no SKU) and waste (paid SKUs with zero usage) before invoices go out.

    bash
    acronis-cli reconcile usages --tenant <tenant_id> --agent
  • coverage - Surface tenants that pay for protection but have no online agent or no recent successful backup.

    Use this to find the highest-liability customers - billed for backup, not actually protected.

    bash
    acronis-cli coverage --unprotected --agent
  • usages drift - Compare per-tenant, per-metric usage between two stored snapshots to see what grew or shrank.

    Reach for this to explain month-over-month invoice changes and spot runaway storage growth early.

    bash
    acronis-cli usages drift --from 2026-04-01 --to 2026-05-01 --agent
  • tenants offering-items inventory - Estate-wide rollup of which offering items and editions are enabled, with per-SKU tenant counts.

    Reach for this for license trueups and edition migrations: which SKUs are deployed where, in one table.

    bash
    acronis-cli tenants offering-items inventory --agent

Fleet posture

  • agents compliance - Show the distribution of agent versions across the estate and flag tenants behind the target version.

    Use this after a release rollout to confirm every customer's agents updated, for security and support consistency.

    bash
    acronis-cli agents compliance --target 16.0 --agent
  • tree - Render the Partner -> Customer -> Folder -> Unit hierarchy with per-node agent and user counts.

    Reach for this to understand the shape of a partner's book of business at a glance.

    bash
    acronis-cli tree --depth 3
  • tenants audit - Flag enabled customer tenants missing users, offering items, agents, or OAuth clients - onboarding drift in one table.

    Reach for this after onboarding waves: catches half-provisioned tenants before they become missed-backup tickets.

    bash
    acronis-cli tenants audit --agent

Command Reference

agent-manager - Manage agent manager

  • acronis-cli agent-manager delete-agent - Cancel registration of a specific Acronis agent.
  • acronis-cli agent-manager delete-agents - Cancel registration and delete service accounts for multiple agents.
  • acronis-cli agent-manager force-agent-update - Launch a forced agent update bypassing maintenance windows for specified agents.
  • acronis-cli agent-manager get-agent - Retrieve details about a specific registered Acronis agent.
  • acronis-cli agent-manager get-agent-update-settings - Fetch update configuration settings for agents or tenants.
  • acronis-cli agent-manager get-hardware-node - Retrieve specific hardware node information including storage configuration.
  • acronis-cli agent-manager list-agents - List all registered Acronis protection agents visible from a specified tenant.
  • acronis-cli agent-manager list-hardware-nodes - List all hardware nodes visible from a specified tenant.
  • acronis-cli agent-manager update-agent-update-settings - Store or update agent update configuration settings including maintenance windows.

clients - OAuth2 client credential management

  • acronis-cli clients create - Create a new OAuth2 client credential for API authentication.
  • acronis-cli clients delete - Delete an OAuth2 client credential.
  • acronis-cli clients get - Retrieve details about a specific OAuth2 client.
  • acronis-cli clients list - List OAuth2 client credentials registered in the system.

idp - Manage idp

  • acronis-cli idp request-token - Request an OAuth2 access token using client credentials, authorization code, or other grant types.
  • acronis-cli idp revoke-token - Revoke an OAuth2 access or refresh token.

remote_search - Manage remote search

  • acronis-cli remote-search - Search for tenants and users by name, email, or login across the accessible hierarchy.

reports - Manage reports

  • acronis-cli reports - Create a scheduled or on-demand usage report configuration.

task-manager - Manage task manager

  • acronis-cli task-manager get-activity - Retrieve details about a specific task activity by ID.
  • acronis-cli task-manager get-task - Retrieve details about a specific backup or protection task by ID.
  • acronis-cli task-manager list-activities - Fetch a list of task activities with filtering and pagination.
  • acronis-cli task-manager list-tasks - Fetch a list of backup and protection tasks with filtering, ordering, and pagination support.

tenants - Tenant hierarchy management and configuration

  • acronis-cli tenants create - Create a new tenant as a child of an existing tenant.
  • acronis-cli tenants delete - Delete a tenant. The tenant must have no children or active services.
  • acronis-cli tenants get - Retrieve details about a specific tenant by ID.
  • acronis-cli tenants list - List tenants in the hierarchy. Can filter by parent tenant UUID or retrieve by specific UUIDs.
  • acronis-cli tenants update - Update tenant properties including name, contact, and enabled status.

agents

  • acronis-cli agents stale - List backup agents that haven't checked in within a threshold, across every tenant, sorted by customer.

  • acronis-cli agents compliance - Show the distribution of agent versions across the estate and flag tenants behind the target version.

alerts

  • acronis-cli alerts repeat - Rank resources and tenants by how many distinct days in a window had a failed or missed backup.

coverage

  • acronis-cli coverage - Surface tenants that pay for protection but have no online agent or no recent successful backup.

customer

  • acronis-cli customer - One cross-resource snapshot of a single customer: tenant record, users, licenses, usage, agents, and 7-day backup outcomes joined.

failures

  • acronis-cli failures - Flat list of every failed or missed backup task across all tenants in a recent window, newest first.

freshness

  • acronis-cli freshness - Time since the last successful backup per tenant, flagged against an SLA threshold - including tenants never backed up.

health

  • acronis-cli health - See backup success / failure / stale across your entire book of customer tenants in one table.

reconcile

  • acronis-cli reconcile usages - Flag usage with no matching offering item and offering items with zero usage, per tenant.

tenants

  • acronis-cli tenants audit - Flag enabled customer tenants missing users, offering items, agents, or OAuth clients - onboarding drift in one table.

  • acronis-cli tenants offering-items inventory - Estate-wide rollup of which offering items and editions are enabled, with per-SKU tenant counts.

tree

  • acronis-cli tree - Render the Partner -> Customer -> Folder -> Unit hierarchy with per-node agent and user counts.

usages

  • acronis-cli usages drift - Compare per-tenant, per-metric usage between two stored snapshots to see what grew or shrank.

Finding the right command

When you know what you want to do but not which command does it, ask the CLI directly:

bash
acronis-cli which "<capability in your own words>"

which resolves a natural-language capability query to the best matching command from this CLI's curated feature index. Exit code 0 means at least one match; exit code 2 means no confident match - fall back to --help or use a narrower query. --json (and other machine formats) keep that exit-2 contract and write {"matches":[]} on stdout so agents can inspect the envelope without treating a miss as success.

Recipes

Monday morning fleet triage

bash
acronis-cli sync && acronis-cli health --agent --select tenant_id,failed,stale

Sync the estate then narrow the health rollup to just the failing and stale columns per tenant.

Offline-agent sweep into a ticket

bash
acronis-cli agents stale --older-than 3d --json --select tenant_id,hostname,last_seen | jq '.[]'

List agents silent for 3+ days with only the fields a ticket needs.

Month-end billing reconciliation

bash
acronis-cli reconcile usages --tenant <tenant_id> --agent

Flag usage with no matching SKU and SKUs with zero usage before invoicing.

Post-rollout version audit

bash
acronis-cli agents compliance --target 16.0 --json

Confirm every tenant's agents reached the target version after a release.

Estate shape at a glance

bash
acronis-cli tree --depth 3

Render the partner/customer/folder/unit hierarchy with per-node agent and user counts.

Auth Setup

Acronis uses OAuth2 client credentials. Register an API client in the Acronis console to get a client_id, client_secret, and your datacenter region, then run acronis-cli auth login - it exchanges them at /api/2/idp/token for a JWT (valid 2 hours) and stores it. Tokens last 2 hours; re-run auth login to refresh. The datacenter host is per-partner; set it with --datacenter or ACRONIS_DATACENTER (e.g. us-cloud, eu2-cloud). You can also skip login and provide a JWT directly via ACRONIS_BEARER_AUTH or auth set-token.

Run acronis-cli doctor to verify setup.

Agent Mode

Add --agent to any command. Expands to: --json --compact --no-input --no-color.

Global format flags share one contract on promoted, novel, sync, and --deliver paths:

  • --json - one JSON document on stdout (sync progress events go to stderr)

  • --compact - keep identity/status/timestamp fields; does not change the document vs stream shape

  • --csv / --plain - tabular rows (collection envelopes unwrap to the row array). --csv prefixes ' on a cell or header key that starts with =, +, -, @, tab, CR, or LF (fullwidth =+-@ too) so a spreadsheet does not run it as a formula; a plain signed number such as -5 or +2.5e3 is unchanged.

  • --quiet - one identity value per row, no envelope

  • Pipeable - JSON on stdout, errors on stderr

  • Filterable - --select keeps a subset of fields. Dotted paths descend into nested structures; arrays traverse element-wise. Critical for keeping context small on verbose APIs:

    bash
    acronis-cli clients list --agent --select client_id,type,status
  • Previewable - --dry-run shows the request without sending

  • Offline-friendly - sync/search commands can use the local SQLite store when available

  • Non-interactive - never prompts, every input is a flag

  • Explicit confirmation - --agent does not imply --yes; pass --yes separately only after the target, arguments, and side effects are clear

  • Explicit retries - use --idempotent only when an already-existing create should count as success, and use --ignore-missing only when a missing delete target should count as success

Response envelope

Commands that read from the local store or the API wrap output in a provenance envelope:

json
{
  "meta": {"source": "live" | "local", "synced_at": "...", "reason": "..."},
  "results": <data>
}

Parse .results for data and .meta.source to know whether it's live or local. A human-readable N results (live) summary is printed to stderr only when stdout is a terminal AND no machine-format flag (--json, --csv, --compact, --quiet, --plain, --select) is set - piped/agent consumers and explicit-format runs get pure JSON on stdout.

Paths and state

Agents should treat the CLI's path resolver as part of the runtime contract:

  • Use --home <dir> for one invocation, or set ACRONIS_HOME=<dir> to relocate all four path kinds under one root.

  • Use per-kind env vars only when a specific kind must diverge: ACRONIS_CONFIG_DIR, ACRONIS_DATA_DIR, ACRONIS_STATE_DIR, ACRONIS_CACHE_DIR.

  • Resolution order is per-kind env var, --home, ACRONIS_HOME, XDG (XDG_CONFIG_HOME, XDG_DATA_HOME, XDG_STATE_HOME, XDG_CACHE_HOME), then platform defaults.

  • config contains settings like config.toml and profiles. data contains credentials.toml, data.db, cookies, and auth sidecars. state contains persisted queries, jobs, and teach.log. cache contains regenerable HTTP/cache files.

  • Stored secrets live in credentials.toml under the data dir. Existing legacy config.toml secrets are read for compatibility and leave config.toml on the first auth write.

  • Run acronis-cli doctor --fail-on warn to surface path and credential-location warnings. agent-context exposes a schema v4 paths block for agents that need the resolved dirs.

  • For MCP, pass relocation through the MCP host config. The MCP binary does not inherit CLI flags:

    json
    {
      "mcpServers": {
        "acronis": {
          "command": "acronis-mcp",
          "env": {
            "ACRONIS_HOME": "/srv/acronis"
          }
        }
      }
    }

Fleet precedence: an inherited per-kind env var overrides an explicit --home for that kind. Use ACRONIS_HOME or per-kind vars as durable fleet levers, and use --home only for a single invocation. Relocation is not reversible by unsetting env vars; move files manually before clearing ACRONIS_HOME, or doctor will not find credentials left under the former root.

Local data is unencrypted and relies on OS account permissions plus disk encryption. Reads of tenants, clients, agents, tasks and activities in the default --data-source auto mode are written to the SQLite store, each in its own endpoint's table; GET responses are cached for five minutes under the cache dir. local-data purge previews what it would erase (store files, response cache, learn journal) and deletes only with --yes; it is not exposed over MCP. ACRONIS_NO_LOCAL_DATA=1 disables the response cache and store writes, turns auto reads into live reads, and blocks sync and workflow archive; --data-source local still reads an existing store, and ACRONIS_DATA_SOURCE sets the --data-source default.

Automatic learning

This CLI ships a self-capturing learning loop. The CLI does its own bookkeeping: every invocation is journaled locally, a failed flag followed by a corrected retry auto-derives a flag_alias candidate, and a teach on a query family without a playbook auto-synthesizes a playbook_candidate from the session's journal. Your job is judgment only: recall first, act on surfaced candidates, teach the final answer, playbook amend when you observe a correction. You never record failures by hand.

Step 1: recall before any discovery

Before list/search/drill commands on a new user question, pass the question as an argv or MCP tool argument to recall --agent. Do not interpolate user-controlled text into a shell command line.

Quoted recall "<question>" breaks on an apostrophe, which is ordinary English. A quoted heredoc breaks when a body line equals the delimiter, and that delimiter is published in these docs. Write the question with a non-shell file-writing tool, then read it back as data:

bash
# Write the question verbatim with your file-writing tool (no shell involved).
# Command substitution on a file only ever yields data  -  the shell never
# parses the file's bytes as syntax.
QUERY=$(cat /path/to/question.txt)
acronis-cli recall "$QUERY" --agent

Prefer MCP: pass the question as the tool's query argument. "$QUERY" after a file read is argv-safe; putting the question itself in the command text is not.

The response envelope:

json
{
  "query": "...",
  "normalized": "<normalized form>",
  "query_entities": ["..."],
  "found": true | false,
  "match_score": 0.0,
  "results": [
    { "resource_id": "...", "resource_type": "...", "venue": "...",
      "confidence": 2, "entity_match": "exact|partial|unknown",
      "source": "taught|preseed|pattern", "warnings": ["..."] }
  ],
  "mismatches": [ /* only when --debug-mismatches */ ],
  "warnings": [ /* top-level */ ],
  "candidates": [
    { "id": 12, "class": "flag_alias | playbook_candidate",
      "summary": "...", "sightings": 3, "last_seen": "...",
      "rationale": "...",
      "next_action": ["<trial command>", "acronis-cli learnings confirm 12"] }
  ],
  "playbook": {
    "query_family": "...",
    "playbook": {
      "steps": [ { "cmd": "<command with {slot} substitution>", "purpose": "..." } ],
      "entity_slots": ["$ENTITY"],
      "expected_tool_calls": 3
    },
    "slots_resolved": { "$ENTITY": { "token": "<live token>", "canonical": "<canonical>" } },
    "notes": "<workarounds + gotchas for this query family>"
  },
  "notes": "<duplicate surface for non-playbook callers>"
}

Empty-store short-circuit: if the store has no learnings, playbooks, or candidates yet (recall finds nothing and learnings list and learnings candidates are both empty), skip recall for the rest of this session instead of taxing every query; resume recall-first once something has been taught.

Step 2: decision tree

Read candidates, playbook, notes, results[0], and warnings in that order:

if Candidates present (warnings include "candidates_present"):
    -> candidates are try-then-confirm, never facts. Follow each candidate's
       two-step next_action verbatim: run the trial command first, then run
       `learnings confirm <id>` only after the trial verified the behavior.
       Reject a wrong candidate with `learnings reject <id>`.
    -> NEVER re-teach something recall surfaced as a candidate; confirm or
       reject that candidate instead of teaching a duplicate.
    -> candidates ride alongside playbooks and resource hits, not instead of
       them; continue with the branches below after acting on them.

if Playbook present:
    -> READ Playbook.notes verbatim FIRST (workarounds + gotchas the CLI surface doesn't expose)
    -> replay Playbook.steps in order, substituting Playbook.slots_resolved entries
       for the entity slot tokens. If a step's slot is unresolved, fall back to
       discovery for that step only.
    -> the Playbook's expected_tool_calls is a budget; if you find yourself running
       materially more, record the divergence via `acronis-cli playbook amend`
       at end-of-session.

elif Notes present (no Playbook):
    -> read Notes verbatim before any discovery step; they carry known gotchas
       for this query family even when no structured choreography exists yet.

elif Found AND Results[0].EntityMatch == "exact" AND Results[0].Confidence >= 2:
    -> skip discovery; fetch live data for Results[*].ResourceID in parallel

elif Found AND Results[0].EntityMatch == "partial":
    -> candidate hint, NOT a hit; read the resource title to validate before trusting

elif (any row in Mismatches[] when --debug-mismatches was passed):
    -> treat as cold start; the stored learning is for a different entity
       (different canonical resolved from query_entities)

else:  // Found == false, no playbook, no notes
    -> cold start; run discovery normally; teach the answer afterward (Step 4).
       If the family has no playbook yet, that teach auto-synthesizes a
       playbook candidate from this session's journal - you do not need to
       record one by hand.

Playbook and Notes are orthogonal to the per-resource path. A recall response can carry both a Playbook AND a Results[] hit - use both: the Playbook tells you which choreography to run; the resource hits short-circuit specific steps. Default to skipping mismatches; pass --debug-mismatches only when investigating cold-start surprises.

Candidate judgment details: learnings confirm <id> prints the candidate's full payload before materializing it - check that the printed payload matches the behavior you verified. learnings reject <id> tombstones the derivation signature so the same candidate does not resurface. The envelope carries only the few candidates worth acting on now; acronis-cli learnings candidates lists the full open set.

Graceful degradation: if learnings confirm is an unknown command, you are driving an older binary - ignore the candidates guidance and follow the rest of the protocol.

Step 3: always read warnings

  • low_confidence: row exists at confidence<2. Treat as a hint, not a skip-discovery hit.
  • resource_not_in_store: the local store doesn't have the resource the learning points at. The match validator couldn't classify entities - direct-fetch and re-evaluate.
  • cross_alias_match (per-result): the row was taught under a different alias and matched the live query's canonical via entity_lookups (e.g., a "USA" teach satisfying a "United States" recall). Trust the resource_id.
  • similar_shape_different_entity:<canonical> (top-level): a structurally matching row exists but its canonical entity differs from the live query's. Treated as cold start; the warning carries the conflicting canonical as a hint, but the row is NOT promoted into Results.
  • ambiguous_alias (top-level): a single query entity resolved to multiple canonicals (e.g., "Cards" → Arizona Cardinals + St. Louis Cardinals). Surface the ambiguity from context before committing to a resource.
  • candidates_present (top-level): the envelope carries a candidates section. Handle it via the candidates branch in Step 2 before anything else.
  • lookup_refresh_available (top-level): an entity in the query has no lookup row yet, but synced data could provide one. Run acronis-cli sync to refresh entity lookups.
  • Top-level no_learnings_for_query_family: the table had no rows above the Jaccard floor. Pure cold start.

Step 4: teach & after finalizing your response - always

Teaching is unconditional. After resolving a query the store could not answer, background-teach the final resource mapping - no call-count threshold, no judging whether it was "worth" learning. The teach is the anchor of the loop: it triggers playbook synthesis for a family without a playbook, and same-referent phrasings fold into one family so near-duplicate teaches do not fragment the store. Fire it after assembling your user-facing response but BEFORE emitting it, with a shell & so the call returns immediately. Pass the query the same way as recall - argv/MCP, or file-then-$QUERY. Do not splice the question into the command text:

bash
QUERY=$(cat /path/to/question.txt)
acronis-cli teach --query "$QUERY" --resource-type <type> --resource <id1> --resource <id2>
# (append shell `&` to background it)

Silent on success. Errors only land in teach.log under the resolved state dir. Teach the most specific resource - if the user asked a broad question and you walked through parent records to find the specific answer, teach the leaf id, not the parent. The CLI uses seeded entity_lookups for cross-alias resolution at recall time, so a teach under one alias (e.g., "Niners") satisfies future queries under another alias (e.g., "49ers", "San Francisco") automatically.

PII rule: teach the structural question with identifiers stripped - never include names, emails, phone numbers, account ids, or other personal identifiers in taught queries or notes. The CLI scans teach queries for obvious email/phone shapes and warns, but does not block; strip before teaching rather than relying on the warning.

Step 5: playbooks - optional flags, automatic synthesis

You do not need to decide whether a session "deserves" a playbook: a teach on a family without one auto-synthesizes a playbook_candidate from the session's journal, and the next session judges it via confirm/reject. Attach explicit playbook flags only when you already hold choreography worth recording verbatim - workarounds the CLI didn't surface (silently-dropped flags, undocumented params, pagination tricks, payload gotchas). Prefer the integrated one-call form - record the resource learning and the playbook in the same teach invocation:

bash
# Common case: record both the resource learning AND the playbook in one call.
QUERY=$(cat /path/to/question.txt)
acronis-cli teach \
  --query "$QUERY" \
  --resource <id> \
  --playbook-file ~/playbooks/<shape>.json \
  --playbook-notes-file ~/playbooks/<shape>-notes.md
# (append shell `&` to background it)

# Alternate: playbook-only (no resource to record alongside).
QUERY=$(cat /path/to/question.txt)
acronis-cli teach-playbook \
  --query "$QUERY" \
  --playbook-file ~/playbooks/<shape>.json \
  --notes-file ~/playbooks/<shape>-notes.md

Playbook files are JSON with steps, entity_slots, expected_tool_calls. Notes files are markdown carrying the gotchas verbatim. File-free callers (MCP-only agents) pass the same content inline: --playbook-json and --playbook-notes on the integrated teach form, --playbook-json and --notes on teach-playbook. On the integrated teach form, the playbook flags are optional - omit them entirely for a resource-only teach. On the standalone teach-playbook form, at least one of the playbook and notes flags must be set; both empty is rejected. Playbooks are keyed on the structural query family (entities stripped) so a recipe taught from one entity-shaped query applies to every other query of the same shape, with slots_resolved binding the live query's canonical at recall time.

When you DO find a playbook on a future recall, treat it as ground truth: replay the steps with slots_resolved substitutions, skip the discovery that the choreography already documents, and read notes before any step.

Step 6: playbook amend & when your debug response identifies a correction

If your debug-protocol response identifies a concrete correction the notes or playbook should know - a workaround, an undocumented endpoint shape, a stale field name, observed schema drift, an empty-payload fallback - fire playbook amend BEFORE emitting your user-facing response. Same fire-and-forget posture as teach. Pass the query and note as argv/MCP arguments, or write each with a non-shell file tool and read them back (QUERY=$(cat ...), NOTE=$(cat ...)). Do not interpolate either string into the command text:

bash
QUERY=$(cat /path/to/question.txt)
NOTE=$(cat /path/to/note.txt)
acronis-cli playbook amend \
  --query "$QUERY" \
  --add-note "$NOTE"
# (append shell `&` to background it)

What counts as worth amending: a behavior you OBSERVED this session that future-you would benefit from knowing. Examples worth amending:

  • A workaround for a CLI surface that silently drops or misorders a flag.
  • An undocumented endpoint shape (response wrapped in {meta, results}, payload nested two levels deeper than the docs claim).
  • Observed schema drift (a field renamed, an index that shifted between seasons, a category label that the API now returns lower-cased).

What does NOT belong in notes:

  • The year-specific or entity-specific answer to the user's question. That's the response, not a learning.
  • Per-team / per-athlete / per-row data the playbook already retrieves at runtime.
  • Statements that paraphrase what the existing notes already say.

The amend command appends to the family's existing notes with a timestamped marker ([amend YYYY-MM-DDTHH:MMZ]: <text>). Multiple amends accumulate; the audit trail is visible. If no playbook exists yet for the family, amend creates a notes-only one (so cold-start corrections still land).

PII discipline for amend notes

playbook amend notes are designed to potentially flow upstream as shared knowledge in future versions of the Printing Press. Keep them clean of user-identifying content so the upstream-contribution path stays open without retroactive scrubbing:

  • Do NOT embed paths to user filesystems, personal API keys or tokens, user email addresses, user GitHub handles, or specific query histories tied to a single user.
  • Acceptable: endpoint shapes, undocumented field names, API gotchas, observed schema drift, workarounds for CLI surfaces, generalizable pagination or retry tactics.

If a correction is only meaningful with user-specific context, it belongs in a personal note, not in the playbook amend.

Measuring the loop

acronis-cli learnings stats reports recall hit rate, teach-to-reuse, playbook resolution rate, and candidate confirm/reject counts from the local learn_events table. Rates are null until they have a denominator; everything stays on this machine. Use it to check whether the loop is earning its keep for this CLI.

Disabling learning

  • --no-learn on a single command short-circuits both recall and the teach write path. Use for deterministic agent flows or tests that must not be affected by accumulated learnings.
  • ACRONIS_NO_LEARN=true in the environment globally disables the pipeline.

Agent Feedback

When you (or the agent) notice something off about this CLI, record it:

acronis-cli feedback "the --since flag is inclusive but docs say exclusive"
acronis-cli feedback --stdin < notes.txt
acronis-cli feedback list --json --limit 10

Entries are stored locally as feedback.jsonl under the resolved data dir. They are never POSTed unless ACRONIS_FEEDBACK_ENDPOINT is set AND either --send is passed or ACRONIS_FEEDBACK_AUTO_SEND=true. Default behavior is local-only.

Write what surprised you, not a bug report. Short, specific, one line: that is the part that compounds.

Output Delivery

Every command accepts --deliver <sink>. The output goes to the named sink in addition to (or instead of) stdout, so agents can route command results without hand-piping. Three sinks are supported:

SinkEffect
stdoutDefault; write to stdout only
file:<path>Atomically write output to <path> as a private 0600 file (exclusive randomly named temp in the destination directory, then rename; a planted symlink is not followed). Binary-response commands write decoded payload bytes (not the base64 JSON envelope) and print a small JSON receipt on stdout; --json/--csv do not refuse when this sink is set.
webhook:<url>POST the output body to the URL (application/json). Redirects are not followed; a 3xx response fails the delivery and the body is not sent to the Location.

Unknown schemes are refused with a structured error naming the supported set. Webhook failures, including redirects, return non-zero and log the URL and HTTP status on stderr.

Named Profiles

A profile is a saved set of flag values, reused across invocations. Use it when a scheduled or recurring agent reuses the same saved flags while providing different input each run.

acronis-cli profile save briefing --json
acronis-cli --profile briefing clients list
acronis-cli profile list --json
acronis-cli profile show briefing
acronis-cli profile delete briefing --yes

Explicit flags always win over profile values; profile values win over defaults. agent-context lists all available profiles under available_profiles so introspecting agents discover them at runtime.

Async Jobs

For endpoints that submit long-running work, the generator detects the submit-then-poll pattern (a job_id/task_id/operation_id field in the response plus a sibling status endpoint) and wires up three extra flags on the submitting command:

FlagPurpose
--waitBlock until the job reaches a terminal status instead of returning the job ID immediately
--wait-timeoutMaximum wait duration (default 10m, 0 means no timeout)
--wait-intervalInitial poll interval (default 2s; grows with exponential backoff up to 30s)

Use async submission without --wait when you want to fire-and-forget; use --wait when you want one command to return the finished artifact.

If --wait times out or polling fails after the job was accepted, the command exits 8 and prints the job ID plus a recovery command that fetches the result. The job may still be running and may already be billed, so run the recovery command instead of resubmitting.

Exit Codes

CodeMeaning
0Success
2Usage error (wrong arguments)
3Resource not found
4Authentication required
5API error (upstream issue)
6Partial failure
7Rate limited (wait and retry)
8Async job submitted but not finished (run the printed recovery command; do not resubmit)
10Config error

Argument Parsing

Parse $ARGUMENTS:

  1. Empty, help, or --help → show acronis-cli --help output
  2. Starts with install → ends with mcp → MCP installation; otherwise → see Prerequisites above
  3. Anything else → Direct Use (execute as CLI command with --agent)

MCP Server Installation

Install the MCP binary from this CLI's published public-library entry or pre-built release, then register it:

bash
claude mcp add acronis-mcp -- acronis-mcp

Verify: claude mcp list

For a remote host, acronis-mcp --transport http requires ACRONIS_MCP_HTTP_TOKEN (callers send Authorization: Bearer <token>), serves http://127.0.0.1:7777/mcp by default, and needs --tls-cert/--tls-key for any non-loopback --addr. Typed endpoint tools that delete or replace (DELETE, PUT) carry the MCP destructiveHint annotation, and those that create or trigger (POST, such as agent-manager_force-agent-update and idp_revoke-token) are marked not read-only but not destructive. The server asks for no confirmation on either: that step is your MCP host's. ACRONIS_MCP_READ_ONLY=1 removes every tool not marked read-only.

Direct Use

  1. Check if installed: which acronis-cli If not found, offer to install (see Prerequisites at the top of this skill).
  2. Match the user query to the best command from the Unique Capabilities and Command Reference above.
  3. Execute with the --agent flag:
    bash
    acronis-cli <command> [subcommand] [args] --agent
  4. If ambiguous, drill into subcommand help: acronis-cli <command> --help.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

and 140 more files.

Frequently asked questions

What does the Acronis AI skill do?

The first real CLI for the Acronis Cyber Protect Cloud platform - every tenant, agent, and usage metric mirrored locally, with cross-tenant rollups no single API call returns. Trigger phrases: `acronis backup health across tenants`, `find offline acronis agents`, `acronis usage and billing report`, `which acronis customers aren't protected`, `acronis agent version compliance`, `use acronis cyber protect`, `run acronis-cli`.

Why use Acronis on TypingMind?

Because you install it once and use it with any model. Acronis is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Acronis in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Servosity/msp-skills/tree/main/skills/acronis. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Acronis?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Acronis?

As many as you like. As long as a model supports skills, you can use Acronis with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Acronis AI skill free?

Yes. It is published on GitHub by Servosity under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇