Agent skill

Pp Agentmail

by mvanhorn in mvanhorn/printing-press-library

AgentMail operations with local memory, safe sends, and fleet-wide insight.

Apache-2.0Auto-check: notesProductivity & Automation

Install Pp Agentmail

skills CLI
$ npx skills add mvanhorn/printing-press-library --skill pp-agentmail -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install mvanhorn/printing-press-library pp-agentmail --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/mvanhorn/printing-press-library.git skills-src && mkdir -p .claude/skills && cp -r skills-src/cli-skills/pp-agentmail .claude/skills/pp-agentmail && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
pp-agentmail
GitHub stars
2.1k
Token cost
~8.3k tokens
SKILL.md length
3,383 words
Files
1
Skills in repo
505
Repo updated
First seen
Licence
Apache-2.0

At a glance

AgentMail operations with local memory, safe sends, and fleet-wide insight.

  • Works in 6 steps: recall before any discovery → decision tree → always read warnings → …
  • Phrases: check my AgentMail inboxes
  • SKILL.md covers Prerequisites: Install the CLI, When to Use This CLI, Anti-triggers and Unique Capabilities, plus 7 more sections
  • Calls bash, go and claude; needs AGENTMAIL_API_KEY

What it does

Pp Agentmail is an agent skill from mvanhorn/printing-press-library. AgentMail operations with local memory, safe sends, and fleet-wide insight. Trigger phrases: check my AgentMail inboxes, search AgentMail messages, review a draft before sending, find unresolved AgentMail follow-ups, audit scheduled AgentMail sends, use AgentMail, run AgentMail.

Its SKILL.md is about 8.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Productivity & Automation, covering Email management. The repository describes itself as: Official library of CLIs generated by the CLI Printing Press. Endorsed, tested, and community-contributed. The licence is Apache-2.0.

When your agent uses it

  • Phrases: check my AgentMail inboxes
  • Search AgentMail messages
  • Review a draft before sending
  • Find unresolved AgentMail follow-ups

Example prompts

  • “/pp-agentmail”

Requirements

  • Node.js
  • Pre-approved tools (allowed-tools): Read, Bash

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. recall before any discovery
  2. decision tree
  3. always read warnings
  4. teach & after finalizing your response - always
  5. playbooks - optional flags, automatic synthesis
  6. playbook amend & when your debug response identifies a correction

What it can do on your machine

Read from SKILL.md and the folder at commit 0fdcc7a. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Bash

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • bash
    • go
    • claude
    • npx

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use npx, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • AGENTMAIL_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Pp Agentmail loads about 8.3k tokens when it runs. Until then it costs about 77 tokens; SKILL.md has 3,383 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~77
When it runs · the whole SKILL.md, loaded when a task matches
~8.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Read, Bash

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from mvanhorn/printing-press-library at commit 0fdcc7a, republished under its Apache-2.0 licence (© mvanhorn). 3,383 words, ~8,345 tokens.

Download SKILL.mdSave it as .claude/skills/pp-agentmail/SKILL.md (or your agent's skills folder).
name
pp-agentmail
description
AgentMail operations with local memory, safe sends, and fleet-wide insight. Trigger phrases: `check my AgentMail inboxes`, `search AgentMail messages`, `review a draft before sending`, `find unresolved AgentMail follow-ups`, `audit scheduled AgentMail sends`, `use AgentMail`, `run AgentMail`.
allowed-tools
Read, Bash
author
Som Samantray
license
Apache-2.0
argument-hint
<command> [args] | install cli|mcp
<!-- GENERATED FILE — DO NOT EDIT.
     This file is a verbatim mirror of library/social-and-messaging/agentmail/SKILL.md,
     regenerated post-merge by tools/generate-skills/. Hand-edits here are
     silently overwritten on the next regen. Edit the library/ source instead.
     See the repository agent guide, section "Generated artifacts: registry.json, cli-skills/". -->

AgentMail — Printing Press CLI

Prerequisites: Install the CLI

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

  1. Install via the Printing Press installer. It defaults binaries to $HOME/.local/bin on macOS/Linux and %LOCALAPPDATA%\Programs\PrintingPress\bin on Windows:
    bash
    npx -y @mvanhorn/printing-press-library install agentmail --cli-only
  2. Verify: agentmail-pp-cli --version
  3. Ensure the reported install directory is on $PATH for the agent/runtime that will invoke this skill.

If the npx install fails (no Node, offline, etc.), fall back to a direct Go install (requires Go 1.26.6 or newer). This installs into $GOPATH/bin (default $HOME/go/bin), so add that directory to $PATH instead:

bash
go install github.com/mvanhorn/printing-press-library/library/social-and-messaging/agentmail/cmd/agentmail-pp-cli@latest

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 CLI covers AgentMail's inbox, message, thread, draft, webhook, domain, list, metric, key, pod, and organization surfaces. It adds local triage queues, pre-send risk checks, conversation rollups, schedule audits, delivery reconciliation, and fleet health so agents can reason across time and resources instead of replaying isolated API calls.

When to Use This CLI

Use AgentMail when an agent needs to provision inboxes, read or send email, manage conversations, prepare reviewed or scheduled drafts, or operate multiple tenants. Prefer the local operational commands when the decision depends on history across messages, threads, drafts, and fleet resources. Use the hosted AgentMail MCP or SDK directly when you need a resident event stream or application-embedded async control.

Anti-triggers

Do not use this CLI for:

  • Do not use this CLI as a general-purpose human email client or interactive inbox UI.
  • Do not use it to send real mail without an explicit recipient review and idempotency key.
  • Do not use local reports before syncing the relevant resources or treat an empty mirror as proof that the remote API has no data.
  • Do not use this CLI when a long-lived WebSocket event consumer must remain embedded inside another process; use the AgentMail SDK or hosted MCP integration.

Unique Capabilities

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

Local operational memory
  • triage queue — Rank unresolved inbound conversations across inboxes with age, direction, labels, and pending drafts.

    Choose this when an agent needs an actionable unresolved-mail queue instead of raw paginated messages.

    bash
    agentmail-pp-cli triage queue --db /tmp/agentmail.db --since 7d --json --agent
  • thread rollup — Render compact conversation handoff context with participants, counts, latest direction, age, labels, and extracted reply content.

    Choose this when an agent or human needs conversation context without repeated thread and message fetches.

    bash
    agentmail-pp-cli thread rollup thread_demo --db /tmp/agentmail.db --json --agent --select thread_id,latest_direction,message_count,pending_draft
Safe automation
  • send check — Review a draft for deterministic recipient, attachment, schedule, duplicate, and idempotency risks before sending.

    Choose this before releasing a draft when a safe, auditable send decision matters more than simply calling send.

    bash
    agentmail-pp-cli send check draft_demo --db /tmp/agentmail.db --json --agent
  • schedule audit — Find scheduled drafts that are overdue, orphaned, duplicated, or missing review state.

    Choose this before a scheduled send window when stale or duplicate drafts need deterministic review.

    bash
    agentmail-pp-cli schedule audit --db /tmp/agentmail.db --due-within 24h --json --agent
  • delivery reconcile — Reconcile outbound messages with status, thread placement, timestamps, and later inbound activity.

    Choose this after a send batch when an agent must identify stale, failed, or unthreaded outcomes.

    bash
    agentmail-pp-cli delivery reconcile --db /tmp/agentmail.db --since 7d --json --agent
Fleet operations
  • fleet health — Report inbox, domain, webhook, list, metrics, API-key, pod, and organization readiness findings.

    Choose this for a preflight fleet review before agents depend on multiple inboxes or tenants.

    bash
    agentmail-pp-cli fleet health --db /tmp/agentmail.db --json --agent

Command Reference

agent — Manage agent

  • agentmail-pp-cli agent sign-up — Create a new agent organization with an inbox and API key. This endpoint is for signing up for the first time.
  • agentmail-pp-cli agent verify — Verify an agent organization using the 6-digit OTP sent to the human's email during sign-up.

api-keys — Manage api keys

  • agentmail-pp-cli api-keys create — CLI: bash agentmail api-keys create --name 'My Key'
  • agentmail-pp-cli api-keys create-public-key — Register a public P-256 JWK using an existing AgentMail bearer API key with api_key_create.
  • agentmail-pp-cli api-keys delete — CLI: bash agentmail api-keys delete --api-key-id <api_key_id>
  • agentmail-pp-cli api-keys list — CLI: bash agentmail api-keys list
  • agentmail-pp-cli api-keys list-public-keys — List only public-key credentials visible to the bearer caller's scope.
  • agentmail-pp-cli api-keys revoke-all-agent-id-sign-in-keys — Invalidate every current public-key credential in the caller's organization by advancing its AgentID key generation.
  • agentmail-pp-cli api-keys revoke-public-key — Permanently revoke one public-key credential. This hard-deletes the credential; repeating the request returns not found.
  • agentmail-pp-cli api-keys update-public-key-name — Rename the credential. All security-relevant fields are immutable. Requires api_key_update.

domains — Manage domains

  • agentmail-pp-cli domains create — CLI: bash agentmail domains create --domain example.com
  • agentmail-pp-cli domains delete — CLI: bash agentmail domains delete --domain-id <domain_id>
  • agentmail-pp-cli domains get — CLI: bash agentmail domains get --domain-id <domain_id>
  • agentmail-pp-cli domains list — CLI: bash agentmail domains list
  • agentmail-pp-cli domains update — CLI: bash agentmail domains update --domain-id <domain_id>

drafts — Manage drafts

  • agentmail-pp-cli drafts get — CLI: bash agentmail drafts get --draft-id <draft_id>
  • agentmail-pp-cli drafts list — CLI: bash agentmail drafts list

inboxes — Manage inboxes

  • agentmail-pp-cli inboxes create — CLI: bash agentmail inboxes create --display-name 'My Agent' --username myagent --domain agentmail.to
  • agentmail-pp-cli inboxes delete — CLI: bash agentmail inboxes delete --inbox-id <inbox_id>
  • agentmail-pp-cli inboxes get — CLI: bash agentmail inboxes get --inbox-id <inbox_id>
  • agentmail-pp-cli inboxes list — CLI: bash agentmail inboxes list
  • agentmail-pp-cli inboxes update — CLI: bash agentmail inboxes update --inbox-id <inbox_id> --display-name 'Updated Name'

lists — Manage lists

  • agentmail-pp-cli lists create — CLI: bash agentmail lists create --direction <direction> --type <type> --entry user@example.com
  • agentmail-pp-cli lists delete — CLI: bash agentmail lists delete --direction <direction> --type <type> --entry <entry>
  • agentmail-pp-cli lists get — CLI: bash agentmail lists get --direction <direction> --type <type> --entry <entry>
  • agentmail-pp-cli lists list — CLI: bash agentmail lists list --direction <direction> --type <type>

metrics — Manage metrics

  • agentmail-pp-cli metrics query-events — Counts of email events (sent, delivered, bounced, etc.) over time for the organization.
  • agentmail-pp-cli metrics query-usage — Cumulative usage series for the organization.

organizations — Manage organizations

  • agentmail-pp-cli organizations — Returns the organization for the authenticated API key (usage limits, counts, and billing metadata).

pods — Manage pods

  • agentmail-pp-cli pods create — CLI: bash agentmail pods create --client-id my-pod
  • agentmail-pp-cli pods delete — CLI: bash agentmail pods delete --pod-id <pod_id>
  • agentmail-pp-cli pods get — CLI: bash agentmail pods get --pod-id <pod_id>
  • agentmail-pp-cli pods list — CLI: bash agentmail pods list

reference-auth — Manage reference auth

  • agentmail-pp-cli reference-auth — Returns the identity and scope of the authenticated credential.

threads — Manage threads

  • agentmail-pp-cli threads delete — Permanently deletes a thread and all of its messages.
  • agentmail-pp-cli threads get — CLI: bash agentmail threads get --thread-id <thread_id>
  • agentmail-pp-cli threads list — Lists threads, most recent first. Pass senders, recipients, or subject to filter by substring.
  • agentmail-pp-cli threads search — Full-text search across threads in the organization, ranked by relevance.
  • agentmail-pp-cli threads update — Updates thread labels. Cannot add or remove system labels (sent, received, bounced, etc.).

webhooks — Manage webhooks

  • agentmail-pp-cli webhooks create — CLI: bash agentmail webhooks create --url https://example.com/webhook --event-type message.received
  • agentmail-pp-cli webhooks delete — CLI: bash agentmail webhooks delete --webhook-id <webhook_id>
  • agentmail-pp-cli webhooks get — CLI: bash agentmail webhooks get --webhook-id <webhook_id>
  • agentmail-pp-cli webhooks list — CLI: bash agentmail webhooks list
  • agentmail-pp-cli webhooks update — Update inbox or pod subscriptions
Finding the right command

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

bash
agentmail-pp-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.

Recipes

Find overdue inbound work
bash
agentmail-pp-cli triage queue --db /tmp/agentmail.db --since 7d --json --agent

Produce an action-ranked queue from synchronized inbox, thread, message, label, and draft state.

Narrow a large search result
bash
agentmail-pp-cli inboxes messages search inb_demo --query "invoice overdue" --agent --select messages.message_id,messages.subject,messages.from

Keep only high-value fields when a relevance-ranked message response is large.

Review a draft before sending
bash
agentmail-pp-cli send check draft_demo --db /tmp/agentmail.db --json --agent

Expose deterministic recipient, schedule, duplicate, and idempotency risks before an irreversible send.

Audit scheduled sends
bash
agentmail-pp-cli schedule audit --db /tmp/agentmail.db --due-within 24h --json

Find overdue, orphaned, duplicated, or unreviewed scheduled drafts.

Reconcile recent delivery
bash
agentmail-pp-cli delivery reconcile --db /tmp/agentmail.db --since 7d --json --agent

Correlate outbound outcomes with later inbound activity and thread placement.

Auth Setup

Set AGENTMAIL_API_KEY to a bearer token from AgentMail. Configured credentials are never printed; newly created API-key and signup secrets are returned only by the upstream create response and are not persisted in the local mirror. Use drafts, --dry-run, and Idempotency-Key for controlled writes; verify the human OTP during first-time agent signup.

Run agentmail-pp-cli doctor to verify setup.

Agent Mode

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

  • 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
    agentmail-pp-cli api-keys list --agent
  • 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 the response is live, local, or dry-run. 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 AGENTMAIL_HOME=<dir> to relocate all four path kinds under one root.

  • Use per-kind env vars only when a specific kind must diverge: AGENTMAIL_CONFIG_DIR, AGENTMAIL_DATA_DIR, AGENTMAIL_STATE_DIR, AGENTMAIL_CACHE_DIR.

  • Resolution order is per-kind env var, --home, AGENTMAIL_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 agentmail-pp-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": {
        "agentmail": {
          "command": "agentmail-pp-mcp",
          "env": {
            "AGENTMAIL_HOME": "/srv/agentmail"
          }
        }
      }
    }

Fleet precedence: an inherited per-kind env var overrides an explicit --home for that kind. Use AGENTMAIL_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 AGENTMAIL_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, run:

bash
agentmail-pp-cli recall "<user's question>" --agent

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>", "agentmail-pp-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 `agentmail-pp-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; agentmail-pp-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.

Show full SKILL.md (1,402 more words)Show less
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 agentmail-pp-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:

bash
agentmail-pp-cli teach --query "<user's question>" --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.
agentmail-pp-cli teach \
  --query "<user's question>" \
  --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).
agentmail-pp-cli teach-playbook \
  --query "<user's question>" \
  --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.

bash
agentmail-pp-cli playbook amend \
  --query "<exact recall query string>" \
  --add-note "<your concrete correction>"
# (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

agentmail-pp-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.
  • AGENTMAIL_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:

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

Entries are stored locally as feedback.jsonl under the resolved data dir. They are never POSTed unless AGENTMAIL_FEEDBACK_ENDPOINT is set AND either --send is passed or AGENTMAIL_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)
webhook:<url>POST the output body to the URL (application/json or application/x-ndjson when --compact)

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.

agentmail-pp-cli profile save briefing --json
agentmail-pp-cli --profile briefing api-keys list
agentmail-pp-cli profile list --json
agentmail-pp-cli profile show briefing
agentmail-pp-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 agentmail-pp-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 server:
    bash
    go install github.com/mvanhorn/printing-press-library/library/social-and-messaging/agentmail/cmd/agentmail-pp-mcp@latest
  2. Register with Claude Code:
    bash
    claude mcp add agentmail-pp-mcp -- agentmail-pp-mcp
  3. Verify: claude mcp list

Direct Use

  1. Check if installed: which agentmail-pp-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
    agentmail-pp-cli <command> [subcommand] [args] --agent
  4. If ambiguous, drill into subcommand help: agentmail-pp-cli <command> --help.

© mvanhorn, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in cli-skills/pp-agentmail of mvanhorn/printing-press-library.

Open the folder on GitHubat commit 0fdcc7a

Compare with similar skills

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Questions about Pp Agentmail

What does Pp Agentmail do?

AgentMail operations with local memory, safe sends, and fleet-wide insight. Pp Agentmail is an agent skill from mvanhorn/printing-press-library. AgentMail operations with local memory, safe sends, and fleet-wide insight.

When should I use Pp Agentmail?

Pp Agentmail fits situations like: phrases: check my AgentMail inboxes; search AgentMail messages; review a draft before sending; find unresolved AgentMail follow-ups.

How do I install Pp Agentmail in Claude Code?

Run `npx skills add mvanhorn/printing-press-library --skill pp-agentmail -a claude-code`. Or copy the skill folder (cli-skills/pp-agentmail in mvanhorn/printing-press-library) into .claude/skills/pp-agentmail in your project. Claude Code loads it when a task matches its description.

How do I install Pp Agentmail in Codex?

Run `npx skills add mvanhorn/printing-press-library --skill pp-agentmail -a codex`. Or copy the skill folder (cli-skills/pp-agentmail in mvanhorn/printing-press-library) into .agents/skills/pp-agentmail in your project. Codex loads it when a task matches its description.

Can I use Pp Agentmail in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add mvanhorn/printing-press-library --skill pp-agentmail -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/pp-agentmail, .gemini/skills/pp-agentmail, .github/skills/pp-agentmail and .opencode/skills/pp-agentmail in your project.

What does Pp Agentmail need to run?

Going by SKILL.md and its folder, Pp Agentmail needs the command-line tools its instructions call (bash, go, claude and npx) and credentials named AGENTMAIL_API_KEY. Our summary lists: Node.js. Its frontmatter pre-approves these tools: Read, Bash.

Does Pp Agentmail access the network?

SKILL.md contains no URLs. Its commands use npx, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Pp Agentmail safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Pp Agentmail use?

Pp Agentmail is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Pp Agentmail use?

About 8.3k tokens (SKILL.md is roughly 33k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Pp Agentmail?

Skills that share tags, products or a category with Pp Agentmail: Process Inbox (telegramdesktop/tdesktop, 33k stars), Continue (telegramdesktop/tdesktop, 33k stars), Garden Inbox (paperclipai/paperclip, 99k stars) and Career-Ops Gmail Lead Plugin (career-ops-hq/career-ops, 74k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Pp Agentmail?

mvanhorn (a GitHub user) maintains it in mvanhorn/printing-press-library, which has 2,053 GitHub stars. The repository holds 505 skills in this directory. The repository was last updated on October 7, 2026.

Source: mvanhorn/printing-press-library on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.