Pp Clarify

Clarify — Printing Press CLI

Prerequisites: Install the CLI

This skill drives the clarify-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:

   npx -y @mvanhorn/printing-press-library install clarify --cli-only
   

2. Verify: clarify-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:

go install github.com/mvanhorn/printing-press-library/library/sales-and-crm/clarify/cmd/clarify-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.

Clarify auto-builds your CRM from email, calendar, and meetings, but its only programmatic surfaces are a hosted MCP server and raw curl. This CLI covers all 75 API operations with the api-key auth scheme and JSON:API envelope handled natively, keeps a local SQLite mirror with transcript full-text search, and adds commands like prep, brief, followup, and dossier that no Clarify surface offers.

When to Use This CLI

Use this CLI whenever a task touches Clarify CRM data from a terminal or agent: querying or updating people, companies, deals, meetings, and tasks; bulk imports; pulling meeting transcripts; or answering pipeline questions (stale deals, velocity, follow-up gaps) that Clarify's own API cannot express. It is the offline-capable alternative to Clarify's hosted MCP server.

Anti-triggers

Do not use this CLI for:

• Do not use this CLI to send email or run outreach sequences; Clarify is the CRM of record, not a sending tool.
• Do not use it for other CRMs (Salesforce, HubSpot, Close) — it only speaks to api.clarify.ai.
• Do not use it to transcribe new meetings; it retrieves transcripts Clarify has already produced.
• Do not use dynamic-list SQL commands to run arbitrary analytics upstream; use the local search and analytics commands against the mirror instead.

Unique Capabilities

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

Rituals the CRM knows but cannot run

• prep — One command before a call: the meeting's attendees, their company, open deals, and transcript excerpts from past meetings with that company.

  Reach for this when the task is preparing for one specific upcoming meeting rather than fetching raw records. Requires a synced local mirror (sync --resources resources --path-context object= first).

  clarify-pp-cli prep --next --agent
  

• brief — Start-of-day overview: today's meetings joined to their companies, open deals, and yesterday's record activity, on one screen.

  Use this for a whole-day overview; use prep for a single meeting. Requires a synced local mirror (sync --resources resources --path-context object= first).

  clarify-pp-cli brief --json
  

• followup — The dropped-ball list: meetings with no subsequent activity, comment, or task on the linked deal or company.

  Run it after a busy week to find meetings that never got a follow-up; --no-deal also surfaces companies with meetings but no open deal. Requires a synced local mirror (sync --resources resources --path-context object= first).

  clarify-pp-cli followup --since 7d --json
  

Pipeline analytics the API does not have

• stale — Open deals with no activity in N days, grouped by pipeline stage.

  The Monday pipeline-review question answered in one command instead of a CSV export. Requires a synced local mirror (sync --resources resources --path-context object= first).

  clarify-pp-cli stale --days 14 --json
  

• velocity — Per-stage dwell time and stage-to-stage conversion counts, accrued from a local stage-history table across repeated runs (the first run reports the current stage distribution).

  Answers 'how long do deals sit in each stage' without exporting anything to a spreadsheet. Requires a synced local mirror; dwell and conversion analytics build up as you re-run sync and velocity over time.

  clarify-pp-cli velocity --json
  

• dupes — Finds likely duplicate people or companies by shared email, domain, or normalized name, and prints ready-to-run merge commands.

  Weekly hygiene sweep for auto-built CRM data; each finding comes with the exact merge invocation to fix it. Requires a synced local mirror (sync --resources resources --path-context object= first).

  clarify-pp-cli dupes --type person --json
  

Agent-native plumbing

• dossier — A complete background bundle on any record: fields, relationships, activities, comments, and related meetings with transcript references, in one compact payload.

  The one-call answer to 'tell me everything about this person/company/deal' — use prep instead when the subject is a specific upcoming meeting. Requires a synced local mirror.

  clarify-pp-cli dossier 5f8b7d2e-9c4a-4e1b-8f3d-2a6c9e0b4d71 --agent --select record,related
  

Command Reference

campaigns — Manage campaigns

comments — Manage comments

• clarify-pp-cli comments create — Creates a comment on a record.
• clarify-pp-cli comments delete — Permanently deletes a comment.
• clarify-pp-cli comments get — Returns a single comment by its ID.
• clarify-pp-cli comments update — Replaces the body of an existing comment. Only the comment’s author may edit it. Returns the updated comment.

layouts — Manage layouts

• clarify-pp-cli layouts get-by-id — Returns a single layout by its ID.
• clarify-pp-cli layouts update — Replaces the layout’s tree and returns the updated layout.

lists — Manage lists

• clarify-pp-cli lists <workspace> — Returns every list across all object types in the workspace as a paginated JSON:API collection.

meetings — Manage meetings

objects — Manage objects

schemas — Manage schemas

• clarify-pp-cli schemas create-custom-object — Creates a new custom object type in the workspace and returns its generated JSON Schema.
• clarify-pp-cli schemas delete-custom-object — Deletes a custom object type and all of its records.
• clarify-pp-cli schemas get — Returns every object schema in the workspace as a cursor-paginated list of JSON:API resources.
• clarify-pp-cli schemas patch-enum-field-values — Adds or removes options on enum (single- and multi-select) fields for one object type.
• clarify-pp-cli schemas update-entity — Replaces the full JSON Schema for an object type.

settings — Manage settings

• clarify-pp-cli settings delete-workspace — Removes the stored value of a workspace setting so it falls back to its default.
• clarify-pp-cli settings read-all-workspace — Returns every workspace setting keyed by name, with defaults applied for settings the workspace has not overridden.
• clarify-pp-cli settings read-workspace — Returns the value of a single workspace setting; the default value when the workspace has not overridden it.
• clarify-pp-cli settings write-workspace — Sets the value of a workspace setting by key.

users — Manage users

• clarify-pp-cli users get — Returns the workspace’s users as a paginated JSON:API list. Each user includes their roles.
• clarify-pp-cli users get-workspaces — Returns a single workspace user as a JSON:API resource, including their roles and the time they were last active.

workflows — Manage workflows

• clarify-pp-cli workflows create — Creates a workflow from a trigger and a set of blocks.
• clarify-pp-cli workflows delete — Deletes a workflow. The deletion is applied asynchronously and the response body is empty. This cannot be undone.
• clarify-pp-cli workflows get — Returns the workspace’s workflows as an offset-paginated list of JSON:API resources.
• clarify-pp-cli workflows get-workspaces — Returns a single workflow as a JSON:API resource
• clarify-pp-cli workflows update — Applies a partial update to a workflow: only the fields present in attributes are changed.

Finding the right command

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

clarify-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

Prep for your next call

clarify-pp-cli prep --next --agent

Attendees, their company, open deals, and past-transcript excerpts in one compact payload.

Find the week's dropped balls

clarify-pp-cli followup --since 7d --json

Meetings with no follow-up activity, comment, or task on the linked deal or company.

Narrow a big deal query for an agent

clarify-pp-cli objects resources get my-workspace deal --agent --select data.attributes.name,data.attributes.amount,data.attributes.stage

JSON:API responses are deep; --select with dotted paths keeps only the fields the agent needs.

Upsert a lead by email

clarify-pp-cli objects records create my-workspace person --match-on email_addresses --data-type person --data-attributes '{"name":{"first_name":"Jane","last_name":"Doe"},"email_addresses":{"items":["jane@example.com"]}}' --dry-run

match_on turns the insert into an upsert against the person unique field; drop --dry-run to send it.

Weekly dupe sweep

clarify-pp-cli dupes --type company --json

Likely duplicates by shared domain or normalized name, each with a ready-to-run merge command.

Auth Setup

Clarify authenticates with an API key sent as Authorization: api-key <key> — not a Bearer token. Create a Personal key in Clarify under Settings, API Keys, then set CLARIFY_API_KEY to the raw key; the CLI adds the api-key scheme prefix for you. Every request is scoped to a workspace slug (visible in your Clarify login URL); set it once with CLARIFY_WORKSPACE or the config file.

Run clarify-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:

  clarify-pp-cli comments get mock-value mock-value --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:

{
  "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 CLARIFY_HOME=<dir> to relocate all four path kinds under one root.

• Use per-kind env vars only when a specific kind must diverge: CLARIFY_CONFIG_DIR, CLARIFY_DATA_DIR, CLARIFY_STATE_DIR, CLARIFY_CACHE_DIR.

• Resolution order is per-kind env var, --home, CLARIFY_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 clarify-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:

  {
    "mcpServers": {
      "clarify": {
        "command": "clarify-pp-mcp",
        "env": {
          "CLARIFY_HOME": "/srv/clarify"
        }
      }
    }
  }
  

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

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

The response envelope:

{
  "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>", "clarify-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 `clarify-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; clarify-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.

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 clarify-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:

clarify-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:

# Common case: record both the resource learning AND the playbook in one call.
clarify-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).
clarify-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.

clarify-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

clarify-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.
• CLARIFY_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:

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

Entries are stored locally as feedback.jsonl under the resolved data dir. They are never POSTed unless CLARIFY_FEEDBACK_ENDPOINT is set AND either --send is passed or CLARIFY_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:

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.

clarify-pp-cli profile save briefing --json
clarify-pp-cli --profile briefing comments get mock-value mock-value
clarify-pp-cli profile list --json
clarify-pp-cli profile show briefing
clarify-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

Argument Parsing

Parse $ARGUMENTS:

1. Empty, help, or --help → show clarify-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:

   go install github.com/mvanhorn/printing-press-library/library/sales-and-crm/clarify/cmd/clarify-pp-mcp@latest
   

2. Register with Claude Code:

   claude mcp add clarify-pp-mcp -- clarify-pp-mcp
   

3. Verify: claude mcp list

Direct Use

1. Check if installed: which clarify-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:

   clarify-pp-cli <command> [subcommand] [args] --agent
   

4. If ambiguous, drill into subcommand help: clarify-pp-cli <command> --help.