Datto Bcdr logo

Datto Bcdr

Organization
Servosity
datto-bcdr

Sync your whole Datto BCDR fleet into local SQLite and answer the questions the per-appliance Partner Portal can't: which backups failed screenshot verification, which are stale, and which clients are at risk. Trigger phrases: `which datto backups failed screenshot verification`, `datto bcdr fleet health`, `which clients are at risk in datto`, `find stale datto backups`, `datto storage runway`, `use datto-bcdr`, `run datto-bcdr-cli`, `datto recoverability score`, `client backup report for qbr`, `triage datto alerts fleet-wide`.

Overview

PublisherServosity
Repositorymsp-skills
Skill namedatto-bcdr
Stars
68
Forks
14
Bundled files
227
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.

  • 227 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 Datto Bcdr 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/datto-bcdr
  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/datto-bcdr .claude/skills/datto-bcdr
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Datto Bcdr 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 Datto Bcdr 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 Datto Bcdr 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.

Datto BCDR - Claude Code Skill

Prerequisites: Install the CLI

This skill drives the datto-bcdr-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/datto-bcdr/install.sh)
  2. Windows (PowerShell):
    powershell
    iwr -useb https://raw.githubusercontent.com/Servosity/msp-skills/main/skills/datto-bcdr/install.ps1 | iex
  3. Verify: datto-bcdr-cli --version
  4. Ensure ~/.local/bin (macOS / Linux) or %LOCALAPPDATA%\Programs\msp-skills (Windows) is on $PATH.

The installer downloads the datto-bcdr-cli and datto-bcdr-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.

The Datto BCDR API is strictly per-device - to check backup health you query one appliance at a time. This CLI syncs every device, agent, share, and alert into a local store, then runs fleet-wide local joins: screenshots --failed surfaces every silently-unbootable backup, client-risk ranks your clients by composite risk, and storage-runway tells you which appliance fills up first. All read-only, all agent-native with --json and --select.

When to Use This CLI

Use this CLI when an agent needs the fleet-wide view of a partner's Datto BCDR estate: proving backups are recoverable, triaging which clients are at risk, finding stale or unprotected machines, and planning storage capacity. It is the right tool whenever the question spans more than one appliance, because the Datto API itself only answers per-device.

When Not to Use This CLI

Do not activate this CLI for requests that require creating, updating, deleting, publishing, commenting, upvoting, inviting, ordering, sending messages, booking, purchasing, or changing remote state. This printed CLI exposes read-only commands for inspection, export, sync, and analysis.

Unique Capabilities

These capabilities aren't available in any other tool for this API.

Recovery Assurance

  • screenshots - See every protected machine whose last backup-bootability screenshot failed, across your entire fleet, ranked by how long it's been failing and grouped by client.

    Reach for this first every morning - it surfaces silently-unbootable backups before a client ever needs to restore.

    bash
    datto-bcdr-cli screenshots --failed --stale-days 7 --json
  • stale-backups - Find every agent whose last local snapshot or last offsite sync is older than a threshold, across all devices and clients at once.

    Use to check recovery-point freshness fleet-wide; catches a backup that quietly stopped taking points without firing an alert.

    bash
    datto-bcdr-cli stale-backups --local-days 1 --offsite-days 3 --json
  • recoverability - One headline KPI: the percentage of fleet agents whose latest recovery point is both fresh and screenshot-verified bootable, with a breakdown of what drags it down.

    Reach for this when leadership asks whether backups are actually recoverable - one defensible number instead of a device-by-device tour.

    bash
    datto-bcdr-cli recoverability --json

Fleet Health

  • client-risk - Per-client risk scorecard that rolls up screenshot failures, stale backups, open alerts, storage pressure, and warranty status into one ranked list of which clients are most at risk.

    Reach for this when someone asks which clients are at risk - it answers the exact business question the per-device portal cannot.

    bash
    datto-bcdr-cli client-risk --top 10 --json
  • alert-triage - Every open alert across the whole fleet in one ranked view, grouped by client and device, instead of pulling alerts one appliance at a time.

    Use for morning alert triage - the whole fleet's open alerts ranked by client without walking the device list.

    bash
    datto-bcdr-cli alert-triage --group-by client --json
  • storage-runway - Rank every appliance by remaining local and offsite storage and flag the devices and clients closest to running out of capacity.

    Use when planning capacity or answering what's our storage runway - surfaces the appliance that fills up before anyone notices.

    bash
    datto-bcdr-cli storage-runway --threshold-pct 85 --json

Coverage Gaps

  • forgotten-assets - List agents that are paused or archived and devices that haven't checked in recently, across the whole fleet, so silently-unprotected machines and dead appliances get caught.

    Reach for this to catch protection someone paused temporarily months ago, or an appliance that quietly went dark - gaps that never generate an alert.

    bash
    datto-bcdr-cli forgotten-assets --offline-days 2 --json
  • agent-versions - Audit agent software versions across the entire fleet and flag every machine running an outdated agent, grouped by client and device.

    Use during patch/maintenance planning or when an agent vulnerability is announced, to find every exposed install in one shot.

    bash
    datto-bcdr-cli agent-versions --outdated --json

Client Reporting

  • client-report - One QBR-ready health report for a single client: devices, agents, screenshot pass rate, stale backups, and open alerts in one bundled view.

    Use when preparing a QBR or answering one client's are-we-protected question - the full single-client story in one command.

    bash
    datto-bcdr-cli client-report "Acme Corp" --json

Command Reference

agent - Protected agents (machines) across the fleet or on one device

  • datto-bcdr-cli agent by-device - List protected agents on a specific device
  • datto-bcdr-cli agent list - List every protected agent across all devices

alert - Open alerts raised by a device

  • datto-bcdr-cli alert <serialNumber> - List open alerts for a device

asset - All assets (agents and shares) on a device, plus single-volume detail

  • datto-bcdr-cli asset get - Get a single asset by its volume name
  • datto-bcdr-cli asset list - List all assets (agents + shares) on a device

device - Datto BCDR appliances (SIRIS / ALTO / NAS / Backup-for-Azure)

  • datto-bcdr-cli device get - Get a single BCDR device by serial number
  • datto-bcdr-cli device list - List all BCDR devices in the partner account

shares - Network shares protected on a device

  • datto-bcdr-cli shares <serialNumber> - List protected shares on a device

vm-restore - Virtualization / VM restore sessions on a device

  • datto-bcdr-cli vm-restore <serialNumber> - List VM restore sessions on a device

Freshness Contract

This printed CLI owns bounded freshness only for registered store-backed read command paths. In --data-source auto mode, those paths check sync_state and may run a bounded refresh before reading local data. --data-source local never refreshes. --data-source live reads the API and does not mutate the local store. Set DATTO_BCDR_NO_AUTO_REFRESH=1 to skip the freshness hook without changing source selection.

Covered paths:

  • datto-bcdr-cli agent
  • datto-bcdr-cli agent list
  • datto-bcdr-cli alert
  • datto-bcdr-cli alert get
  • datto-bcdr-cli alert list
  • datto-bcdr-cli alert search
  • datto-bcdr-cli asset
  • datto-bcdr-cli asset get
  • datto-bcdr-cli asset list
  • datto-bcdr-cli device
  • datto-bcdr-cli device get
  • datto-bcdr-cli device list
  • datto-bcdr-cli shares
  • datto-bcdr-cli shares get
  • datto-bcdr-cli shares list
  • datto-bcdr-cli shares search

When JSON output uses the generated provenance envelope, freshness metadata appears at meta.freshness. Treat it as current-cache freshness for the covered command path, not a guarantee of complete historical backfill or API-specific enrichment.

Finding the right command

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

bash
datto-bcdr-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

Morning recovery-assurance sweep

bash
datto-bcdr-cli screenshots --failed --stale-days 7 --agent

Lists every agent whose bootability screenshot has been failing 7+ days, in agent-native output.

Narrowed fleet export for a report

bash
datto-bcdr-cli agent list --agent --select agentName,os,lastScreenshotAttemptStatus,lastSnapshot

Pulls just the columns a recoverability report needs, skipping the verbose payload.

Client-by-client risk briefing

bash
datto-bcdr-cli client-risk --top 10 --json

Ranked per-client risk scorecard ready to pipe into a status update.

Capacity planning

bash
datto-bcdr-cli storage-runway --threshold-pct 85 --json

Flags every appliance over 85% on local or offsite storage.

Fleet recoverability KPI

bash
datto-bcdr-cli recoverability --json

One defensible number - the % of agents whose latest recovery point is fresh AND screenshot-verified bootable - for the morning report or leadership ask.

Fleet-wide alert triage

bash
datto-bcdr-cli alert-triage --group-by client --json

All open alerts across every appliance grouped by client, replacing a per-serial walk of the device list.

Auth Setup

Datto BCDR uses HTTP Basic auth with a partner-generated key pair. Generate a public/secret key in the Datto Partner Portal under Admin > Integrations, then export DATTO_BCDR_PUBLIC_KEY and DATTO_BCDR_SECRET_KEY. The CLI base64-encodes public:secret into the Authorization header on every request.

Run datto-bcdr-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)

  • --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
    datto-bcdr-cli agent list --agent --select agentName,localIp,os
  • 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

  • Read-only - do not use this CLI for create, update, delete, publish, comment, upvote, invite, order, send, or other mutating requests

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 DATTO_BCDR_HOME=<dir> to relocate all four path kinds under one root.

  • Use per-kind env vars only when a specific kind must diverge: DATTO_BCDR_CONFIG_DIR, DATTO_BCDR_DATA_DIR, DATTO_BCDR_STATE_DIR, DATTO_BCDR_CACHE_DIR.

  • Resolution order is per-kind env var, --home, DATTO_BCDR_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 datto-bcdr-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": {
        "datto-bcdr": {
          "command": "datto-bcdr-mcp",
          "env": {
            "DATTO_BCDR_HOME": "/srv/datto-bcdr"
          }
        }
      }
    }

Fleet precedence: an inherited per-kind env var overrides an explicit --home for that kind. Use DATTO_BCDR_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 DATTO_BCDR_HOME, or doctor will not find credentials left under the former root.

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=$(cat /path/to/question.txt)
datto-bcdr-cli recall "$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>", "datto-bcdr-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 `datto-bcdr-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; datto-bcdr-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 datto-bcdr-cli sync --resources agent,alert,asset,device,shares 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=$(cat /path/to/question.txt)
datto-bcdr-cli teach --query "$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=$(cat /path/to/question.txt)
datto-bcdr-cli teach \
  --query "$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=$(cat /path/to/question.txt)
datto-bcdr-cli teach-playbook \
  --query "$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)
datto-bcdr-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

datto-bcdr-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.
  • DATTO_BCDR_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:

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

Entries are stored locally as feedback.jsonl under the resolved data dir. They are never POSTed unless DATTO_BCDR_FEEDBACK_ENDPOINT is set AND either --send is passed or DATTO_BCDR_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> (tmp + rename). 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)

Unknown schemes are refused with a structured error naming the supported set. Webhook failures return non-zero and log the URL + 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.

datto-bcdr-cli profile save briefing --json
datto-bcdr-cli --profile briefing agent list
datto-bcdr-cli profile list --json
datto-bcdr-cli profile show briefing
datto-bcdr-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.

Exit Codes

CodeMeaning
0Success
2Usage error (wrong arguments)
3Resource not found
4Authentication required
5API error (upstream issue)
7Rate limited (wait and retry)
10Config error

Argument Parsing

Parse $ARGUMENTS:

  1. Empty, help, or --help → show datto-bcdr-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

  1. Install the MCP binary (run the install script from the Prerequisites section, or see mcp-install.md for per-agent wire-up).
  2. Register with Claude Code:
    bash
    claude mcp add datto-bcdr-mcp -- datto-bcdr-mcp
  3. Verify: claude mcp list

Direct Use

  1. Check if installed: which datto-bcdr-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
    datto-bcdr-cli <command> [subcommand] [args] --agent
  4. If ambiguous, drill into subcommand help: datto-bcdr-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 Datto Bcdr AI skill do?

Sync your whole Datto BCDR fleet into local SQLite and answer the questions the per-appliance Partner Portal can't: which backups failed screenshot verification, which are stale, and which clients are at risk. Trigger phrases: `which datto backups failed screenshot verification`, `datto bcdr fleet health`, `which clients are at risk in datto`, `find stale datto backups`, `datto storage runway`, `use datto-bcdr`, `run datto-bcdr-cli`, `datto recoverability score`, `client backup report for qbr`, `triage datto alerts fleet-wide`.

Why use Datto Bcdr on TypingMind?

Because you install it once and use it with any model. Datto Bcdr 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 Datto Bcdr in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Servosity/msp-skills/tree/main/skills/datto-bcdr. 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 Datto Bcdr?

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 Datto Bcdr?

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

Is the Datto Bcdr 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.

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