Pinecone Vector Database
Orchestra-Research/AI-Research-SKILLs
Shows how to use Pinecone, a managed vector database, for production RAG, semantic search and recommendations: indexes, upserts, queries, filters and namespaces.
Every Pinecone API feature, plus local sync, snapshot history, and text-first search no other Pinecone tool has.
$ npx skills add mvanhorn/printing-press-library --skill pp-pinecone -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install mvanhorn/printing-press-library pp-pinecone --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/mvanhorn/printing-press-library.git skills-src && mkdir -p .claude/skills && cp -r skills-src/library/ai/pinecone .claude/skills/pp-pinecone && rm -rf skills-srcUse ~/.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/
Install the "pp-pinecone" agent skill from https://github.com/mvanhorn/printing-press-library/tree/main/library/ai/pinecone into .claude/skills/pp-pinecone/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pp-pinecone", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/mvanhorn/printing-press-library/tree/main/library/ai/pineconeType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add mvanhorn/printing-press-library --skill pp-pinecone -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install mvanhorn/printing-press-library pp-pinecone --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mvanhorn/printing-press-library.git skills-src && mkdir -p .agents/skills && cp -r skills-src/library/ai/pinecone .agents/skills/pp-pinecone && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "pp-pinecone" agent skill from https://github.com/mvanhorn/printing-press-library/tree/main/library/ai/pinecone into .agents/skills/pp-pinecone/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pp-pinecone", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add mvanhorn/printing-press-library --skill pp-pinecone -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install mvanhorn/printing-press-library pp-pinecone --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mvanhorn/printing-press-library.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/library/ai/pinecone .cursor/skills/pp-pinecone && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "pp-pinecone" agent skill from https://github.com/mvanhorn/printing-press-library/tree/main/library/ai/pinecone into .cursor/skills/pp-pinecone/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pp-pinecone", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/mvanhorn/printing-press-library.git --path library/ai/pinecone--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add mvanhorn/printing-press-library --skill pp-pinecone -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install mvanhorn/printing-press-library pp-pinecone --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mvanhorn/printing-press-library.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/library/ai/pinecone .gemini/skills/pp-pinecone && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "pp-pinecone" agent skill from https://github.com/mvanhorn/printing-press-library/tree/main/library/ai/pinecone into .gemini/skills/pp-pinecone/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pp-pinecone", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install mvanhorn/printing-press-library pp-pineconeInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add mvanhorn/printing-press-library --skill pp-pinecone -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/mvanhorn/printing-press-library.git skills-src && mkdir -p .github/skills && cp -r skills-src/library/ai/pinecone .github/skills/pp-pinecone && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "pp-pinecone" agent skill from https://github.com/mvanhorn/printing-press-library/tree/main/library/ai/pinecone into .github/skills/pp-pinecone/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pp-pinecone", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add mvanhorn/printing-press-library --skill pp-pinecone -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install mvanhorn/printing-press-library pp-pinecone --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mvanhorn/printing-press-library.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/library/ai/pinecone .opencode/skills/pp-pinecone && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "pp-pinecone" agent skill from https://github.com/mvanhorn/printing-press-library/tree/main/library/ai/pinecone into .opencode/skills/pp-pinecone/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pp-pinecone", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
pp-pineconeEvery Pinecone API feature, plus local sync, snapshot history, and text-first search no other Pinecone tool has.
Pp Pinecone is an agent skill from mvanhorn/printing-press-library. Every Pinecone API feature, plus local sync, snapshot history, and text-first search no other Pinecone tool has. Trigger phrases: query my pinecone index, upsert vectors into pinecone, search pinecone embeddings, check pinecone index health, what changed in my pinecone index, use pinecone, run pinecone.
Its SKILL.md is about 9.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 364 other files (for example `.golangci.yml`, `.goreleaser.yaml` and `.manuscripts/20260813-214420-550eb813/proofs/2026-08-13-214420-fix-pinecone-pp-cli-build-log.md`).
It sits in Databases, covering Vector databases. It works with Pinecone. 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.
6 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 0fdcc7a. It shows what the files ask for, not the result of running them.
Pre-approves these tools, so the agent can use them without asking each time:
ReadBashFrom allowed-tools in the SKILL.md frontmatter.
Shell commands in SKILL.md call:
goclaudenpxjqFrom the folder's file list and the shell code blocks in SKILL.md.
Links to these hosts (documentation or services it may open):
docs.pineconedocs.pinecone.ioFrom URLs in SKILL.md, links to its own repository left out.
Names these keys or tokens, usually read from environment variables:
PINECONE_API_KEYFrom names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Pp Pinecone loads about 9.7k tokens when it runs. Until then it costs about 83 tokens; SKILL.md has 4,154 words of instructions outside code blocks.
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.
The automated check noted patterns worth knowing about, such as sudo or a known installer.
allowed-tools: Read, BashAutomated 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.
The full file from mvanhorn/printing-press-library at commit 0fdcc7a, republished under its Apache-2.0 licence (© mvanhorn). 4,154 words, ~9,683 tokens.
.claude/skills/pp-pinecone/SKILL.md (or your agent's skills folder). This skill also uses 359 other files; get the full folder from GitHub.This skill drives the pinecone-pp-cli binary. You must verify the CLI is installed before invoking any command from this skill. If it is missing, install it first:
$HOME/.local/bin on macOS/Linux and %LOCALAPPDATA%\Programs\PrintingPress\bin on Windows:npx -y @mvanhorn/printing-press-library install pinecone --cli-onlypinecone-pp-cli --version$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/ai/pinecone/cmd/pinecone-pp-cli@latestIf --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 Pinecone CLI manages indexes, vectors, namespaces, backups, imports, inference, and admin — then goes further: sync index and record metadata into a local SQLite database for offline search, capture snapshot history to diff and project growth, and search by natural language without touching vectors. Agent-native --json/--select/csv output and typed exit codes make it scriptable in CI.
Use this CLI when you manage Pinecone indexes and vectors from a terminal or agent: create and scale indexes, ingest and query vectors, run semantic search over text, audit backup coverage, and track index growth over time. It is the right tool for RAG pipelines, embedding workflows, and index fleet administration.
Do not use this CLI for:
These capabilities aren't available in any other tool for this API.
text-query — Search a dense index with a natural-language query — embeds the text via Pinecone Inference, then queries the index — without managing vectors yourself.
Use when an agent needs semantic retrieval from an index but only has a text query, not a precomputed vector.
pinecone-pp-cli text-query travel-chat-embeddings --text "visa on arrival for thailand" --top-k 5 --jsoncascade — Run one semantic query across multiple indexes and merge the ranked results (deduped by vector ID, best score wins) into a single agent-ready list.
Use when an agent needs to search across several indexes (e.g. prod vs staging, multiple tenants) in one shot.
pinecone-pp-cli cascade --indexes travel-chat-embeddings,travel-chat-embeddings-v2 --text "kyoto itinerary" --top-k 3 --jsonsnapshot — Capture a point-in-time state of any index (per-namespace vector counts, dimension, metric, host) into local SQLite, then diff against prior snapshots to see exactly what changed.
Use when an agent needs to answer "what changed in this index since last week?" for drift, growth, or incident review.
pinecone-pp-cli snapshot travel-chat-embeddings --note weeklyusage — Compute vector-count growth and per-namespace distribution shifts from snapshot history, with a projection to a future horizon.
Use when an agent needs to forecast index growth or spot a namespace ballooning before it hits quota.
pinecone-pp-cli usage --index travel-chat-embeddings --since 30d --jsoncoverage — Join backups, backup schedules, and restore jobs into a per-index protection matrix showing which indexes are backed up, scheduled, and restorable.
Use when an agent audits disaster-recovery posture across an index fleet.
pinecone-pp-cli coverage --jsonprune — Find vectors whose local metadata timestamps are older than a threshold and delete them in batches — dry-run by default, with --apply to commit.
Use when an agent maintains a long-lived memory store and needs to expire stale chunks without a full resync.
pinecone-pp-cli prune travel-chat-embeddings --older-than 90d --applycheck-vectors — Validate a vectors JSON file against the index schema — dimension, duplicate/empty IDs, sparse/dense shape — before you upsert and burn write units on a rejected batch.
Use when an agent ingests data and wants to catch dimension or ID errors before a costly rejected batch.
pinecone-pp-cli check-vectors --index travel-chat-embeddings --file vectors.json --jsonadmin — Manage admin
pinecone-pp-cli admin create-api-key — Create an API key for a project to authenticate Data Plane and Control Plane requests.pinecone-pp-cli admin create-invite — Invite a user to the organization by email and grant their initial role bindings.pinecone-pp-cli admin create-project — Create a new project.pinecone-pp-cli admin create-role-binding — Grant a role to a principal at an organization or project scope.pinecone-pp-cli admin create-service-account — Create a service account with optional initial role bindings; the client secret is returned only once.pinecone-pp-cli admin delete-api-key — Delete an API key from a project.pinecone-pp-cli admin delete-invite — Delete a pending or expired invite and its role bindings; to remove an accepted user, delete the user instead.pinecone-pp-cli admin delete-organization — Delete an organization and all its configuration; delete all its projects first.pinecone-pp-cli admin delete-project — Delete a project and all its configuration; delete its indexes, assistants, backups, and collections first.pinecone-pp-cli admin delete-role-binding — Delete a role binding; permissions are revoked when the deletion completes.pinecone-pp-cli admin delete-service-account — Delete a service account and its role bindings; tokens it minted are revoked within a few seconds.pinecone-pp-cli admin delete-user — Remove a user from the organization and revoke their role bindings; their Pinecone account is not deleted.pinecone-pp-cli admin fetch-api-key — Get an API key's details, excluding its secret.pinecone-pp-cli admin fetch-invite — Get an invite in the caller's organization by ID.pinecone-pp-cli admin fetch-organization — Get an organization's details.pinecone-pp-cli admin fetch-project — Get a project's details.pinecone-pp-cli admin fetch-role-binding — Get a role binding in the caller's organization by ID.pinecone-pp-cli admin fetch-service-account — Get a service account by ID; the client secret is returned only from create and rotate-secret requests.pinecone-pp-cli admin fetch-user — Get a user in the caller's organization by ID.pinecone-pp-cli admin list-invites — List pending and expired invites in the caller's organization.pinecone-pp-cli admin list-organizations — List all organizations associated with an account.pinecone-pp-cli admin list-project-api-keys — List all API keys in a project.pinecone-pp-cli admin list-projects — List all projects in an organization.pinecone-pp-cli admin list-role-bindings — List role bindings in the caller's organization, optionally filtered by principal, resource, and role.pinecone-pp-cli admin list-service-accounts — List service accounts in the caller's organization.pinecone-pp-cli admin list-users — List users in the caller's organization, optionally filtered by email address.pinecone-pp-cli admin resend-invite — Resend the invite email and extend its expiration to 7 days from now; limited to 100 emails per hour per organization.pinecone-pp-cli admin rotate-service-account-secret — Rotate a service account's OAuth client secretpinecone-pp-cli admin update-api-key — Update an API key's name and roles.pinecone-pp-cli admin update-organization — Update an organization's name.pinecone-pp-cli admin update-project — Update a project's name, maximum number of Pods, or customer-managed encryption key (CMEK).pinecone-pp-cli admin update-service-account — Update a service account's name; role bindings are managed through the role-binding endpoints.assistants — Manage assistants
pinecone-pp-cli assistants create — Create an assistant.pinecone-pp-cli assistants delete — Delete an existing assistant. For guidance and examples, see [Manage assistants](https://docs.pinecone.pinecone-pp-cli assistants get — Get the status of an assistant. For guidance and examples, see [Manage assistants](https://docs.pinecone.pinecone-pp-cli assistants list — List of all assistants in a project. For guidance and examples, see [Manage assistants](https://docs.pinecone.pinecone-pp-cli assistants update — Update an existing assistant. You can modify the assistant's instructions.backup-schedules — Manage backup schedules
pinecone-pp-cli backup-schedules delete — Permanently remove a backup schedule.pinecone-pp-cli backup-schedules describe — Get a single backup schedule by ID.pinecone-pp-cli backup-schedules update — Update frequency, retention, or enabled state for a backup schedule.backups — Manage backups
pinecone-pp-cli backups delete — Delete a backup.pinecone-pp-cli backups describe — Get a description of a backup.pinecone-pp-cli backups list-project — List backups for all indexes in a projectbulk — Manage bulk
pinecone-pp-cli bulk cancel-import — Cancel an import operation if it is not yet finished. It has no effect if the operation is already finished.pinecone-pp-cli bulk describe-import — Return details of a specific import operation. For guidance and examples, see [Import data](https://docs.pinecone.pinecone-pp-cli bulk list-imports — List all recent and ongoing import operations. By default, list_imports returns up to 100 imports per page.pinecone-pp-cli bulk start-import — Start an asynchronous import of vectors from object storage into an index.chat — Manage chat
pinecone-pp-cli chat <assistant_name> — Chat with an assistant and get back citations in structured form.collections — Manage collections
pinecone-pp-cli collections create — Create a Pinecone collection. Serverless indexes do not support collections.pinecone-pp-cli collections delete — Delete an existing collection. Serverless indexes do not support collections.pinecone-pp-cli collections describe — Get a description of a collection. Serverless indexes do not support collections.pinecone-pp-cli collections list — List all collections in a project. Serverless indexes do not support collections.describe-index-stats — Manage describe index stats
pinecone-pp-cli describe-index-stats — Return statistics about the contents of an index, including the vector count per namespace, the number of dimensionsembed — Manage embed
pinecone-pp-cli embed — Generate vector embeddings for input data. This endpoint uses Pinecone's [hosted embedding models](https://docs.files — Manage files
pinecone-pp-cli files delete — Delete an uploaded file from an assistant.pinecone-pp-cli files describe — [Get the current status and metadata of a file](https://docs.pinecone.pinecone-pp-cli files list — List all files in an assistant, with an option to filter files with metadata.pinecone-pp-cli files upload — Upload a file to the specified assistant. An identifier will be generated.pinecone-pp-cli files upsert — Create or replace a file in the specified assistant.indexes — Manage indexes
pinecone-pp-cli indexes configure-index — Configure an existing index. For guidance and examples, see [Manage indexes](https://docs.pinecone.pinecone-pp-cli indexes create-index — Create a Pinecone index.pinecone-pp-cli indexes create-index-for-model — Create an index with integrated embedding.pinecone-pp-cli indexes delete-index — Delete an existing index.pinecone-pp-cli indexes describe-index — Get a description of an index.pinecone-pp-cli indexes list — List all indexes in a project.metrics — Endpoints for accessing database metrics.
pinecone-pp-cli metrics <project_id> — Get endpoints for Prometheus scraping.models — Manage models
pinecone-pp-cli models get — Get a description of a model hosted by Pinecone.pinecone-pp-cli models list — List the embedding and reranking models hosted by Pinecone.namespaces — Manage namespaces
pinecone-pp-cli namespaces create — Create a namespace in a serverless index. For guidance and examples, see [Manage namespaces](https://docs.pinecone.pinecone-pp-cli namespaces delete — Delete a namespace from a serverless index.pinecone-pp-cli namespaces describe — Describe a namespace in a serverless index, including the total number of vectors in the namespace.pinecone-pp-cli namespaces list-operation — List all namespaces in a serverless index.oauth — Authentication using the OAuth2 protocol.
pinecone-pp-cli oauth — Obtain an access token for a service account using the OAuth2 client credentials flow.operations — Manage operations
pinecone-pp-cli operations describe — Get the status of an operation.pinecone-pp-cli operations list — List all operations for an assistant.query — Manage query
pinecone-pp-cli query — Search a namespace using a query vector.records — Manage records
pinecone-pp-cli records search-namespace — Search a namespace with a query text, query vector, or record ID and return the most similar recordspinecone-pp-cli records upsert-namespace — Upsert text into a namespace.rerank — Manage rerank
pinecone-pp-cli rerank — Rerank results according to their relevance to a query. For guidance and examples, see [Rerank results](https://docs.restore-jobs — Manage restore jobs
pinecone-pp-cli restore-jobs describe — Get a description of a restore job.pinecone-pp-cli restore-jobs list — List all restore jobs for a project.vectors — Manage vectors
pinecone-pp-cli vectors delete — Delete records by id or by metadata from a single namespace. For guidance and examples, see [Delete data](https://docs.pinecone-pp-cli vectors fetch — Look up and return records by ID from a single namespace. The returned records include the vector data and/or metadata.pinecone-pp-cli vectors fetch-by-metadata — Look up and return records by metadata from a single namespace.pinecone-pp-cli vectors list — List the IDs of records in a single namespace of a serverless index.pinecone-pp-cli vectors update — Update records by ID or by metadata in a namespace.pinecone-pp-cli vectors upsert — Upsert records into a namespace.When you know what you want to do but not which command does it, ask the CLI directly:
pinecone-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.
pinecone-pp-cli text-query travel-chat-embeddings --text "what did the group decide about kyoto" --top-k 5 --select matches.id,matches.metadata.sender,matches.scoreAsk a natural-language question and get scored, deduped hits with metadata — no embedding step
pinecone-pp-cli snapshot travel-chat-embeddings --note weekly && pinecone-pp-cli snapshot diff --index travel-chat-embeddings --since 7dRecord index state weekly, then see exactly what changed: vector counts, namespaces, config drift
pinecone-pp-cli prune travel-chat-embeddings --older-than 90dPreview which vectors would be deleted by age before committing with --apply
pinecone-pp-cli cascade --indexes travel-chat-embeddings,travel-chat-embeddings-v2 --text "trip itinerary" --top-k 3 --jsonSearch prod and staging indexes in one call and get a single merged, deduped ranked list
pinecone-pp-cli check-vectors --index travel-chat-embeddings --file vectors.json --jsonCatch dimension and ID errors in a batch before it burns write units on a rejected upsert
pinecone-pp-cli indexes list --json --select name,host,dimension,spec.serverless.region | jq '.results[] | {name, host, dimension, region: .spec.serverless.region}'Narrow a large response with --select and pipe to jq for a compact inventory
Pinecone uses an API key sent as the Api-Key header, with the required X-Pinecone-Api-Version header set to the API version (2026-04). Set PINECONE_API_KEY=<key> or run pinecone-pp-cli auth set-token. Data-plane commands target the per-index host, which the CLI resolves automatically from indexes describe-index when you pass --index, or via PINECONE_INDEX_HOST.
Run pinecone-pp-cli doctor to verify setup.
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:
pinecone-pp-cli assistants list --x-pinecone-api-version example-value --agent --select created_at,host,instructionsPreviewable — --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
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.
Agents should treat the CLI's path resolver as part of the runtime contract:
Use --home <dir> for one invocation, or set PINECONE_HOME=<dir> to relocate all four path kinds under one root.
Use per-kind env vars only when a specific kind must diverge: PINECONE_CONFIG_DIR, PINECONE_DATA_DIR, PINECONE_STATE_DIR, PINECONE_CACHE_DIR.
Resolution order is per-kind env var, --home, PINECONE_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 pinecone-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": {
"pinecone": {
"command": "pinecone-pp-mcp",
"env": {
"PINECONE_HOME": "/srv/pinecone"
}
}
}
}Fleet precedence: an inherited per-kind env var overrides an explicit --home for that kind. Use PINECONE_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 PINECONE_HOME, or doctor will not find credentials left under the former root.
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.
recall before any discoveryBefore list/search/drill commands on a new user question, run:
pinecone-pp-cli recall "<user's question>" --agentThe 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>", "pinecone-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.
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 `pinecone-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; pinecone-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.
warningslow_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 pinecone-pp-cli sync to refresh entity lookups.no_learnings_for_query_family: the table had no rows above the Jaccard floor. Pure cold start.teach & after finalizing your response - alwaysTeaching 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:
pinecone-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.
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.
pinecone-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).
pinecone-pp-cli teach-playbook \
--query "<user's question>" \
--playbook-file ~/playbooks/<shape>.json \
--notes-file ~/playbooks/<shape>-notes.mdPlaybook 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.
playbook amend & when your debug response identifies a correctionIf 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.
pinecone-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:
{meta, results}, payload nested two levels deeper than the docs claim).What does NOT belong in notes:
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).
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:
If a correction is only meaningful with user-specific context, it belongs in a personal note, not in the playbook amend.
pinecone-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.
--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.PINECONE_NO_LEARN=true in the environment globally disables the pipeline.When you (or the agent) notice something off about this CLI, record it:
pinecone-pp-cli feedback "the --since flag is inclusive but docs say exclusive"
pinecone-pp-cli feedback --stdin < notes.txt
pinecone-pp-cli feedback list --json --limit 10Entries are stored locally as feedback.jsonl under the resolved data dir. They are never POSTed unless PINECONE_FEEDBACK_ENDPOINT is set AND either --send is passed or PINECONE_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.
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:
| Sink | Effect |
|---|---|
stdout | Default; 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.
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.
pinecone-pp-cli profile save briefing --json
pinecone-pp-cli --profile briefing assistants list --x-pinecone-api-version example-value
pinecone-pp-cli profile list --json
pinecone-pp-cli profile show briefing
pinecone-pp-cli profile delete briefing --yesExplicit 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.
| Code | Meaning |
|---|---|
| 0 | Success |
| 2 | Usage error (wrong arguments) |
| 3 | Resource not found |
| 4 | Authentication required |
| 5 | API error (upstream issue) |
| 7 | Rate limited (wait and retry) |
| 10 | Config error |
Parse $ARGUMENTS:
help, or --help → show pinecone-pp-cli --help outputinstall → ends with mcp → MCP installation; otherwise → see Prerequisites above--agent)go install github.com/mvanhorn/printing-press-library/library/ai/pinecone/cmd/pinecone-pp-mcp@latestclaude mcp add pinecone-pp-mcp -- pinecone-pp-mcpclaude mcp listwhich pinecone-pp-cli
If not found, offer to install (see Prerequisites at the top of this skill).--agent flag:pinecone-pp-cli <command> [subcommand] [args] --agentpinecone-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
SKILL.md and 359 other files in library/ai/pinecone of mvanhorn/printing-press-library.
Open the folder on GitHubat commit 0fdcc7a
Pp Pinecone next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Pp Pinecone this skillmvanhorn/printing-press-library | 2.1k | — | ~9.7k | Automated safety check: Notes | Apache-2.0 | |
| Pinecone Vector DatabaseOrchestra-Research/AI-Research-SKILLs | 13k | 6 repos | ~2k | Automated safety check: Pass | MIT | |
| Using Vector Databasesancoleman/ai-design-components | 526 | 1 repos | ~3.5k | Automated safety check: Pass | MIT | |
| PineconeLuciole-Studio/Misaka-Agent | 139 | 1 repos | ~2.1k | Automated safety check: Pass | MIT | |
| PineconeAlexAI-MCP/hermes-CCC | 135 | — | ~954 | Automated safety check: Pass | MIT | |
| Cognee Community Packagestopoteretes/cognee | 32k | — | ~1.2k | Automated safety check: Pass | Apache-2.0 |
Orchestra-Research/AI-Research-SKILLs
Shows how to use Pinecone, a managed vector database, for production RAG, semantic search and recommendations: indexes, upserts, queries, filters and namespaces.
ancoleman/ai-design-components
Vector database implementation for AI/ML applications, semantic search, and RAG systems.
Luciole-Studio/Misaka-Agent
Managed vector DB for production RAG and search. An agent skill from Luciole-Studio/Misaka-Agent.
AlexAI-MCP/hermes-CCC
Managed vector database for production RAG — serverless and pod-based deployment, hybrid search, namespaces, and metadata filtering.
topoteretes/cognee
Guide to using and contributing cognee community packages: database adapters, data-source connectors, custom tasks and retrievers, and Keywords AI observability.
wshobson/agents
Build retrieval-augmented generation systems: pick a vector database and embedding model, choose retrieval and reranking strategies, and start from a LangGraph pipeline.
mvanhorn/printing-press-library
Desktop automation through the real Rust agent-desktop CLI, published in Printing Press through a small bridge.
mvanhorn/printing-press-library
Search, browse, and download Google Fonts from the terminal via the gfonts CLI.
mvanhorn/printing-press-library
The free, offline Trigger phrases: search 1688 for, find a factory on 1688 for, wholesale price on 1688 for, who is the cheapest supplier on 1688 for, compare 1688 suppliers for, use 1688, run 1688.
mvanhorn/printing-press-library
Inspect known Activity Japan plan IDs or URLs, compare dated prices and sessions, check language-sitemap coverage, and hand off to canonical booking pages.
mvanhorn/printing-press-library
Every Admin By Request portal action, plus a local SQLite mirror of audit, events, inventory and requests for ad-hoc...
mvanhorn/printing-press-library
macOS screen capture, window recording, GIF conversion, and agent evidence bundles from the terminal.
Works with
Categories
Every Pinecone API feature, plus local sync, snapshot history, and text-first search no other Pinecone tool has. Pp Pinecone is an agent skill from mvanhorn/printing-press-library. Every Pinecone API feature, plus local sync, snapshot history, and text-first search no other Pinecone tool has.
Pp Pinecone fits situations like: phrases: query my pinecone index; upsert vectors into pinecone; search pinecone embeddings; check pinecone index health.
Run `npx skills add mvanhorn/printing-press-library --skill pp-pinecone -a claude-code`. Or copy the skill folder (library/ai/pinecone in mvanhorn/printing-press-library) into .claude/skills/pp-pinecone in your project. Claude Code loads it when a task matches its description.
Run `npx skills add mvanhorn/printing-press-library --skill pp-pinecone -a codex`. Or copy the skill folder (library/ai/pinecone in mvanhorn/printing-press-library) into .agents/skills/pp-pinecone in your project. Codex loads it when a task matches its description.
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-pinecone -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-pinecone, .gemini/skills/pp-pinecone, .github/skills/pp-pinecone and .opencode/skills/pp-pinecone in your project.
Going by SKILL.md and its folder, Pp Pinecone needs the command-line tools its instructions call (go, claude, npx and jq) and credentials named PINECONE_API_KEY. Our summary lists: Node.js. Its frontmatter pre-approves these tools: Read, Bash.
SKILL.md names 2 domains. As links in the text: docs.pinecone and docs.pinecone.io. This is read from the text; nothing was executed.
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.
Pp Pinecone 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.
About 9.7k tokens (SKILL.md is roughly 39k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Pp Pinecone: Pinecone Vector Database (Orchestra-Research/AI-Research-SKILLs, 13k stars), Using Vector Databases (ancoleman/ai-design-components, 526 stars), Pinecone (Luciole-Studio/Misaka-Agent, 139 stars) and Pinecone (AlexAI-MCP/hermes-CCC, 135 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
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.