thoughtleaders-cli 0.6.54

ThoughtLeaders CLI — query sponsorship data, channels, brands, and intelligence

tl cli

ThoughtLeaders CLI — query sponsorship data, channels, brands, and intelligence from the terminal.

What you can do with it

tl is a thin client over the ThoughtLeaders sponsorship platform. It exposes the same data the internal web app uses — deals, brands, channels, transcripts, view-curves, recommender — to a terminal, and is designed to be driven by AI agents (Claude Code, OpenCode, Gemini, Codex) as well as humans. Typical things teams build on top of it:

For account managers and sales

• Pipeline reporting on the fly. "How many deals did we close in Q1?", "What's my weighted pipeline by sales owner?", "Which proposals are stuck in pending for more than 14 days?" — one raw SQL or one structured command, instead of waiting on a dashboard.
• Brand intelligence in seconds. "What channels does Nike sponsor?", "Which brands sponsor MrBeast?", "What's Holafly's sponsorship history through us vs. through everyone?" — answers are one tl brands history or one tl db es call away.
• Vetting candidates before a pitch. Look up a channel by ID, name, YouTube URL, or @handle; pull its adspots, audience demographics, evergreenness score, and detected sponsor history before drafting the IO.
• Pre-flight before booking. Confirm MSN/TPP membership, integration availability, and persona/plan eligibility for a brand profile with one SQL join.

For media buyers and brand-side analysts

• Find channels that look like a known winner. tl channels similar runs vector similarity over a ~200-dim audience/category profile and ranks candidates by score; tl channels look-alike is the same command under the AM-facing name.
• Discover topical creators without guessing category codes. tl recommender top-channels "<tag>" ranks channels by how strongly they load on a topic, demographic, or format tag — Cooking, Age 18-24, USA share, etc. Browse valid tag names with tl recommender tags.
• Surface the right channels for a brand. tl recommender channels-for-brand Nike runs the brand's ideal-audience vector against the channel index and returns the closest unproposed channels.
• Surface the right brands for a channel. tl recommender brands-for-channel <ref> runs the inverse search — brands most likely to sponsor a given channel, ranked.

For data, finance, and reporting

• Ad-hoc SQL against the production schema. tl db pg accepts any read-only SELECT (sanitised, capped at 500 rows per page) with the full set of aggregates, window functions, joins, and JSONB operators. tl schema pg [<table>] prints the live column catalogue. tl db fb and tl db es expose Firebolt (time-series view-curves, subscriber growth) and Elasticsearch (transcripts, brand mentions, current channel/video metrics).
• Exports for the spreadsheet on the other side of the conversation. --csv exports stream straight to a file; --md produces tables you can paste into Slack or a brief; --toon produces a token-efficient encoding for LLM round-trips.
• Saved reports as a contract. tl reports run <id> re-runs a campaign config someone set up in the web app, so a report that lives in a Slack thread can be a one-command rerun next week.

For AI agents

• Built-in skills install for free. tl setup claude / opencode / gemini / codex drop ready-made skill files into the right agent directories so the agent answers natural-language questions like "how many deals did we close last quarter?" by composing the right tl db pg|fb|es calls itself.
• Discoverable surface. tl describe lists every resource and its credit cost; tl describe show <resource> lists fields and filters; tl <command> --help is detailed enough that an agent can plan without external documentation.
• Predictable output shapes. Every command's --json envelope follows the same {results, total, limit, offset, usage, _breadcrumbs} contract, so an agent can pipe one command's IDs into the next without bespoke parsing.

Requirements

• Python 3.12+
• jq
• ripgrep
• duckdb

For automated installs on MacOS, prefer installing Homebrew and use the pipx package manager, but ask the user if they have the admin access (sudo) password first. If not, proceed by using uv and installing everything locally.

Install

As a developer

git clone https://github.com/ThoughtLeaders-io/thoughtleaders-cli.git
cd thoughtleaders-cli
python -m venv .venv
pip install -e .

As a user

# Recommended:
pipx install thoughtleaders-cli
# or
uv tool install thoughtleaders-cli
# or (avoid this — plain `pip` will install into your current environment instead of a fresh venv)
pip install thoughtleaders-cli

Then set up:

tl auth login          # authenticate with ThoughtLeaders (OAuth2 browser flow, device code, or API key)
tl setup claude        # install Claude Code plugin (optional)
tl setup opencode      # install OpenCode skill (optional)
tl setup gemini        # install Gemini CLI skill (optional)
tl setup codex         # install Codex CLI skill (optional)

tl auth login offers three options:

1. OAuth2 in a local browser (default) — opens a URL on this machine.
2. Device code — for headless environments; complete the flow on another device.
3. API key — paste a pre-issued APIKey from Django admin. The CLI verifies it via /whoami and stores it tagged so every request sends X-TL-Auth: API-KEY.

Quick Start

# Login
tl auth login

# Show information about the logged-in user
tl whoami

# Sold sponsorships for Nike in Q1 — write the SQL directly.
# `publish_status = 3` is sold; brand is reached via the
# profile → profile_brands → brand chain.
tl db pg "SELECT al.id, al.weighted_price, al.purchase_date
          FROM thoughtleaders_adlink al
          JOIN thoughtleaders_profile p           ON p.id  = al.creator_profile_id
          JOIN thoughtleaders_profile_brands pb   ON pb.profile_id = p.id
          JOIN thoughtleaders_brand b             ON b.id  = pb.brand_id
          WHERE al.publish_status = 3
            AND b.name = 'Nike'
            AND al.purchase_date >= '2026-01-01'
            AND al.purchase_date <  '2026-04-01'
          ORDER BY al.purchase_date DESC
          LIMIT 500 OFFSET 0"

# Show a specific sponsorship by ID
tl sponsorships show 12345

# Resolve a free-form string to a single channel — accepts names,
# slugs, YouTube channel URLs, @handles, raw channel IDs, or video URLs.
# Default output is a pretty `id  name` line; --json / --csv / --md / --toon
# return machine-readable shapes. Ambiguous matches print candidates.
tl channels find "MrBeast"
tl channels find https://www.youtube.com/@MrBeast
tl channels find https://www.youtube.com/watch?v=dQw4w9WgXcQ --json

# Same for brands — matches name, slug, website domain, or any keyword.
tl brands find Nike
tl brands find nike.com

# Show channel detail — accepts numeric ID or channel name.
tl channels show 12345
tl channels show "Economics Explained"

# Find similar channels (recommender, 25 credits, Intelligence plan).
# msn: tri-state (default yes): yes = MSN only, no = non-MSN only, both = no filter.
# tpp: tri-state (default both): yes = TPP only, no = non-TPP only, both = no filter.
tl channels similar 12345 --limit 10
tl channels similar "Tremending girls" min-score:0.85 --limit 5

# Recommender — discovery by category/demographic tag (Intelligence plan).
# `tags` is free; everything else costs 25 credits flat.
tl recommender tags                              # List every tag (free)
tl recommender tags cooking                      # Search tag names by substring
tl recommender top-channels "Cooking" msn:yes --limit 50   # Top channels for a tag
tl recommender top-profiles "Cooking" mbn:yes --limit 30   # Top brand profiles (one brand → potentially multiple profiles)
tl recommender top-brands "Cooking" --limit 30             # Top brands (deduped from profiles)
tl recommender inspect-channel 12345             # Per-tag breakdown of a channel's vector
tl recommender inspect-brand Nike                # Per-tag breakdown of a brand's ideal profile
tl recommender channels-for-profile 842          # Channels closest to a specific brand profile
tl recommender channels-for-brand Nike msn:yes   # Same, but takes a brand ref (uses its newest profile with a vector)
tl recommender brands-for-channel 12345          # Brands most likely to sponsor a channel

# Brand intelligence
tl brands show Nike
tl brands history Nike                # Detected sponsorships from ES
tl brands history-stats Nike          # Aggregate roll-up (totals, first/last seen, top channels)

# Search videos and transcripts via Elasticsearch
tl db es '{"size":20,"query":{"term":{"channel.id":12345}},"_source":["title","views"]}'
tl db es '{"size":50,"query":{"term":{"sponsored_brand_mentions":"5612"}}}'

# Historical view-curves (Firebolt — channel_id required by index)
tl db fb "SELECT id, age, view_count FROM article_metrics
          WHERE channel_id = 12345 AND id IN ('abc', 'def')
          ORDER BY id, age"

# Run a saved report
tl reports                            # list saved reports
tl reports run 42

# Comments — available on sponsorships, channels, brands, and uploads
tl sponsorships comment-list 12345
tl sponsorships comment-add 12345 "Looks good"
tl channels comment-add 7890 "Strong recent winners"

# Check credits
tl balance

# Health check — auth, connectivity, version, latency, and required external tools.
# Run this first when something feels off; it surfaces token expiry,
# missing `jq`/`rg`/`duckdb`, and slow endpoints in one snapshot.
tl doctor

Credits

Every data query costs credits based on the type and number of results. Use tl describe to see credit rates and tl balance to check your balance.

tl describe                                # All resources + credit costs
tl describe show sponsorships --filters    # Available filters for sponsorships
tl balance                                 # Your credit balance

tl db pg is priced per-query: a base rate plus a surcharge for every priced table and column referenced. Sensitive fields (demographics, channel outreach emails) cost more. Run tl describe show db --json to see the live surcharge map, and check usage.credit_rate in the response envelope after a query to see what your query was actually charged.

Terminology

ThoughtLeaders has its internal terminology that's exposed throughout this tool.

• Brands — Usually companies, sometimes individual products. Brands are the sponsors.
• Channels — Usually YouTube channels, sometimes podcasts. Channels are creators, they are being sponsored.
• Sponsorships — Either possible or realised business relationships between brands and channels, stored in thoughtleaders_adlink. There are several specific sub-types differentiated by the row's publish_status:
  • Deals — Contractually agreed-upon sponsorships (sold; publish_status = 3). They can be in a production pipeline or already published.
  • Matches — Possible brand-channel pairings (publish_status = 7); ThoughtLeaders thinks they could work.
  • Proposals — Matches that have been proposed to both sides (publish_status = 0).
• Adspots — types of ads a channel carries (e.g. mention, dedicated video, product placement). Returned by tl channels show; each carries price/cost and a computed CPM.
• AdLink — engineering / DB name for the row that backs a sponsorship. Treat as interchangeable with "sponsorship"; the table is thoughtleaders_adlink.
• MSN (Media Selling Network) — the ~12k YouTube channels that have opted in to receive sponsorship offers. A channel is in MSN if channel.media_selling_network_join_date IS NOT NULL.
• TPP (ThoughtLeaders Partner Program) — TL's closest-partner channels, a strict subset of MSN. A channel is TPP if channel.is_tl_channel = TRUE. Prefer TPP channels when booking — fastest response, easiest to close.
• MBN (Media Buying Network) — the brand-side counterpart to MSN: profiles that have opted in to receive proposed sponsorships (profile.media_buying_network_join_date IS NOT NULL).

Sponsorships are the centre of attention in ThoughtLeaders — all other analytics and operations serve to produce or optimise sponsorships. Note that the term "Sponsorship" is wide and encompasses pre-deal stages. The funnel is large at the Sponsorship end and narrowest at the Deal end.

Integrations

The same set of natural-language skills is published for every supported agent. Running tl update after an upgrade re-syncs every agent whose binary is on PATH.

Claude Code

tl setup claude

Registers the ThoughtLeaders marketplace, installs the plugin, and copies skills to ~/.claude/ for short /tl invocation. If the claude binary isn't on PATH, it still installs the standalone skills and prints manual instructions for the plugin.

Talk naturally in Claude Code:

/tl Which channels did we sponsor in Q1?
/tl sold sponsorships for Nike in Q1
/tl show me pending proposals with send dates in April
/tl what channels does Nike sponsor?
/tl find me Cooking creators in the US with mobile-heavy audiences
/tl check my balance

Resource-specific slash commands:

/tl-sponsorships pending with send dates in April
/tl-reports run my Q1 pipeline
/tl-balance

OpenCode, Gemini, Codex

tl setup opencode      # copies skills to ~/.config/opencode/skills/
tl setup gemini        # copies skills to ~/.agents/skills/
tl setup codex         # copies skills to ~/.agents/skills/ (same target as gemini)

Each agent discovers the skill automatically and uses it when you ask about sponsorships, deals, channels, brands, or intelligence. Gemini and Codex share the ~/.agents/skills/ install target.

Skills shipped with the CLI

The plugin ships several focused skills (installed by all the tl setup * commands):

• tl — the data-analyst skill. Defaults to raw database queries via tl db pg|fb|es for anything non-trivial; uses the structured tl <resource> show / find / similar / history commands for single-record lookups and the special cases they were built for (similarity search, ID resolution, sponsorship history). Comes with full schema references for Postgres, Elasticsearch, and Firebolt under references/.
• tl-report-builder — builds TL reports (channels / brands / sponsorships / videos) from natural-language requests. Produces an in-chat preview by default; saves a real campaign when the user is explicit ("save", "create the report").
• tl-import / bulk-import — superuser-only; bulk-add or exclude lists of channels, brands, videos, or sponsorships against a report.

Output Formats

By default, output is a styled table in the terminal and JSON when piped.

tl sponsorships show 12345 --json | jq '.results'
tl db pg "SELECT id, channel_name FROM thoughtleaders_channel WHERE is_tl_channel = TRUE
          LIMIT 200 OFFSET 0" --csv > tpp.csv
tl channels show "MrBeast" --md      # markdown table for Slack / docs
tl channels show "MrBeast" --toon    # token-efficient encoding for LLMs

TOON (Token-Oriented Object Notation) is a compact text format designed to encode structured data with fewer tokens than JSON when feeding output back into an LLM. See the TOON format repository for the specification.

Documentation

• Calling the HTTP API directly — curl and Python recipes for the whoami, balance, db pg|fb|es, and schema pg|fb|es endpoints, authenticated with an API key.
• tl describe — discover available resources, fields, filters, and credit costs from the CLI itself
• tl schema pg|fb|es — live schema for the underlying stores
• tl <command> --help — detailed help for any command
• tl doctor — diagnostic snapshot of auth, connectivity, version, latency, and required external tools

Notes

• Tested with Claude Code, OpenCode (including the nemotron-cascade-2-30b-a3b-i1 local model), Gemini CLI, and Codex CLI.