Pp Sarvam

Sarvam AI — Printing Press CLI

Prerequisites: Install the CLI

This skill drives the sarvam-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 sarvam --cli-only
   

2. Verify: sarvam-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 before this CLI has a public-library category, install Node or use the category-specific Go fallback after publish.

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.

Sarvam AI's official SDKs and MCP server are great for code, but nothing offers offline capability: no local history of translations, TTS generations, transcriptions, or chat threads. sarvam-pp-cli adds a local SQLite store, voice auditioning, conversation resume, batch job retry/report, pronunciation spot-checks, subtitle export, and a doc-ai extraction schema library — all with --json, --dry-run, and typed exit codes for agents and scripts.

When to Use This CLI

Use sarvam-pp-cli when you need to translate, transliterate, transcribe, synthesize speech, chat, or extract document fields through Sarvam AI's Indic-language models — especially when you want a record of what you did, offline search across past work, batch orchestration, or scriptable/agent-friendly output. It is the right tool for voice-agent prompt engineering, call-center transcription QA, content localization, and document-intelligence pipelines.

Anti-triggers

Do not use this CLI for:

• Do not use this CLI for the Voice Agents platform (apps.sarvam.ai) — deployments, campaigns, cohorts, analytics, and instant outbound use a separate X-API-Key auth system with org/workspace scoping and are not implemented here.
• Do not use this CLI for real-time WebSocket streaming (realtime STT, TTS WS) — those are AsyncAPI surfaces not covered by the REST CLI.
• Do not use this CLI to replace a full speech-recognition model benchmark — the API is a paid service and the CLI does not add model-evaluation tooling.

Unique Capabilities

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

Speech workflow

• voices preview — Generate one sample sentence across every TTS speaker and hear them all side by side

  Use when choosing a TTS voice for a new language or prompt set without burning manual API calls

  sarvam-pp-cli voices preview --lang hi-IN --sample "नमस्ते, स्वागत है" --speakers shubh,ritu,priya --output ./voices
  

• pron-check — Verify a term's TTS pronunciation via a speech round-trip (TTS then STT)

  Use to confirm a pronunciation dictionary edit took effect before shipping IVR prompts

  sarvam-pp-cli pron-check "SarvamPay" --lang hi-IN
  

Local state that compounds

• chat resume — Continue a past chat thread from local history with full context

  Use to continue an assistant session without losing context, offline from the original thread

  sarvam-pp-cli chat resume 20260814_2d09e061 "what was our conclusion?"
  

• subs — Emit .srt/.vtt subtitles from timestamped transcriptions in local history

  Use to turn a timestamped transcription into subtitles without a throwaway script

  sarvam-pp-cli subs --from last --format srt --output subtitles.srt
  

Job orchestration

• stt-job retry — Re-run only the failed files of a batch STT job with one command

  Use when a batch job partially fails and you need to reprocess just the failures

  sarvam-pp-cli stt-job retry 20260707_9f1c2b3a-4d5e-6f70-8a9b-c0d1e2f3a4b5 --failed-only --dir ./audio/
  

• stt-job report — Per-file digest of a batch STT job with typed exit codes for cron alerting

  Use in cron to alert when a batch transcription job degrades

  sarvam-pp-cli stt-job report 20260707_9f1c2b3a-4d5e-6f70-8a9b-c0d1e2f3a4b5 --json
  

Document intelligence

• docai schema list — Save, list, and diff doc-ai extraction schemas locally

  Use to version extraction schemas so schema changes never silently break extraction runs

  sarvam-pp-cli docai schema list
  

• docai batch — Run a saved extraction schema over a folder of documents with job pacing

  Use for weekly batch document extraction (KYC, invoices) without writing orchestration code

  sarvam-pp-cli docai batch --schema invoice-v1 --dir ./docs/ --out ./results/
  

Command Reference

chat — Manage chat

• sarvam-pp-cli chat — Creates a model response for the given chat conversation. Serves sarvam-105b and sarvam-105b-conversations models.

doc-ai — Manage doc ai

• sarvam-pp-cli doc-ai digitise — Creates and starts a digitisation job from files or pre-uploaded handles.
• sarvam-pp-cli doc-ai download-url — Returns a presigned URL to download the output of a completed doc-ai job.
• sarvam-pp-cli doc-ai extract — Creates and starts an extract job from files or pre-uploaded handles.
• sarvam-pp-cli doc-ai results — Fetches the results of a completed doc-ai job, including extracted fields and annotations with confidence scores.
• sarvam-pp-cli doc-ai status — Polls the status of a doc-ai job until a terminal status (completed, partially_completed, failed, rejected).
• sarvam-pp-cli doc-ai upload — Creates a presigned URL to upload a document for doc-ai processing.

models — Manage models

• sarvam-pp-cli models — Lists the model IDs this deployment currently serves.

speech-to-text — Manage speech to text

• sarvam-pp-cli speech-to-text speech-to-text — Transcribes speech to text in multiple Indian languages and English. Accepts an audio file via multipart form-data.
• sarvam-pp-cli speech-to-text stt-job-download — Returns presigned download URLs for the output files of a completed batch speech-to-text job.
• sarvam-pp-cli speech-to-text stt-job-initiate — Creates a new speech-to-text bulk job and returns a job UUID and storage details for processing multiple audio files.
• sarvam-pp-cli speech-to-text stt-job-start — Starts processing a speech-to-text bulk job after all audio files have been uploaded.
• sarvam-pp-cli speech-to-text stt-job-status — Returns the status of a batch speech-to-text job including per-file details and download information.
• sarvam-pp-cli speech-to-text stt-job-upload — Generates presigned upload URLs for audio files that will be processed in a speech-to-text bulk job.

text-lid — Manage text lid

• sarvam-pp-cli text-lid — Identifies the language (e.g. hi-IN) and script (e.g.

text-to-speech — Manage text to speech

• sarvam-pp-cli text-to-speech create-pronunciation-dictionary — Uploads a .json file to create a new pronunciation dictionary. Only supported by bulbul:v3.
• sarvam-pp-cli text-to-speech delete-pronunciation-dictionary — Deletes a pronunciation dictionary by ID.
• sarvam-pp-cli text-to-speech get-pronunciation-dictionary — Fetches a single pronunciation dictionary by ID.
• sarvam-pp-cli text-to-speech list-pronunciation-dictionaries — Lists all pronunciation dictionaries for the user. Dictionaries define custom word pronunciations used by bulbul:v3 TTS.
• sarvam-pp-cli text-to-speech stream — Converts the input text into a streamed spoken audio response using the specified output codec (e.g. MP3).
• sarvam-pp-cli text-to-speech text-to-speech — Converts text into spoken audio.
• sarvam-pp-cli text-to-speech update-pronunciation-dictionary — Updates an existing pronunciation dictionary with a new JSON file.

translate — Manage translate

• sarvam-pp-cli translate — Converts text from one language to another while preserving meaning. Supports 22 Indic languages plus English.

transliterate — Manage transliterate

• sarvam-pp-cli transliterate — Transliterates text from one script to another (e.g.

Finding the right command

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

sarvam-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

Translate a support reply

sarvam-pp-cli translate --input "Your EMI of Rs. 3000 is pending" --source-language-code en-IN --target-language-code hi-IN --mode formal

Translate a single message with formal tone for customer communications

Audition voices for a new prompt set

sarvam-pp-cli voices preview --lang hi-IN --sample "नमस्ते, स्वागत है" --speakers shubh,ritu,priya

Hear 3 voices on the same sample before committing to a speaker

Subtitle a promo video

sarvam-pp-cli subs --from last --format srt --output subtitles.srt

Turn the last timestamped transcription into SRT subtitles

Reprocess failed batch files

sarvam-pp-cli stt-job retry 20260707_9f1c2b3a-4d5e-6f70-8a9b-c0d1e2f3a4b5 --failed-only --dir ./audio/

Re-run only the files that failed in a batch transcription job

Check a pronunciation dictionary

sarvam-pp-cli pron-check "SarvamPay" --lang hi-IN --dict p_5cb7faa6

Verify a custom pronunciation actually changed how the term sounds

Extract fields from a folder of documents

sarvam-pp-cli docai batch --schema invoice-v1 --dir ./docs/ --out ./results/

Run a saved extraction schema over every document with pacing

Continue a past chat session

sarvam-pp-cli chat resume 20260814_2d09e061 "what was our conclusion?"

Pick up an assistant conversation where it left off, with full context

Auth Setup

Authentication uses the Sarvam AI API subscription key (sk_ format). Set it with export SARVAM_API_KEY=sk_... or sarvam-pp-cli auth set-token. The key goes in the api-subscription-key header (or Authorization: Bearer). Note: an invalid key returns HTTP 403 with invalid_api_key_error, not 401 — treat 403 as the auth-failure signal. The separate Voice Agents platform (apps.sarvam.ai) uses a different X-API-Key system and is out of scope for this CLI.

Run sarvam-pp-cli doctor to verify setup.

Agent Mode

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

• 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:

  sarvam-pp-cli models --agent --select created,id,object
  

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

• Use per-kind env vars only when a specific kind must diverge: SARVAM_CONFIG_DIR, SARVAM_DATA_DIR, SARVAM_STATE_DIR, SARVAM_CACHE_DIR.

• Resolution order is per-kind env var, --home, SARVAM_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 sarvam-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": {
      "sarvam": {
        "command": "sarvam-pp-mcp",
        "env": {
          "SARVAM_HOME": "/srv/sarvam"
        }
      }
    }
  }
  

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

sarvam-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>", "sarvam-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 `sarvam-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; sarvam-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 sarvam-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:

sarvam-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.
sarvam-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).
sarvam-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.

sarvam-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

sarvam-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.
• SARVAM_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:

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

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

sarvam-pp-cli profile save briefing --json
sarvam-pp-cli --profile briefing models
sarvam-pp-cli profile list --json
sarvam-pp-cli profile show briefing
sarvam-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.

Async Jobs

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

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

Exit Codes

Argument Parsing

Parse $ARGUMENTS:

1. Empty, help, or --help → show sarvam-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/ai/sarvam/cmd/sarvam-pp-mcp@latest
   

2. Register with Claude Code:

   claude mcp add sarvam-pp-mcp -- sarvam-pp-mcp
   

3. Verify: claude mcp list

Direct Use

1. Check if installed: which sarvam-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:

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

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