outlit

outlit is a skill for Claude Code, Codex from OutlitAI/outlit-sdk. It costs 39 tokens per session (1,217 once invoked), scanned A, original, Apache-2.0.

A set of tools for answering customer questions from product activity, conversations, billing, and web signals in Outlit, a customer-data platform.

In plain words
What is it for?
Use it to investigate customers, users, account health, churn risk, revenue, activation, timelines, and supporting evidence.
Why use it?
It gives the agent a shared customer timeline and facts instead of relying on guesses or scattered records.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to investigate customers, users, account health, churn risk, revenue, activation, timelines, and supporting evidence.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/outlitai/outlit-sdk/outlit
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add OutlitAI/outlit-sdk --skill outlit
Clone the repo
git clone --depth 1 https://github.com/OutlitAI/outlit-sdk

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for outlit

README.md
[![agentmods](https://agentmods.dev/badge/skills/outlitai/outlit-sdk/outlit.svg)](https://agentmods.dev/skills/outlitai/outlit-sdk/outlit)
Your own site
<a href="https://agentmods.dev/skills/outlitai/outlit-sdk/outlit"><img src="https://agentmods.dev/badge/skills/outlitai/outlit-sdk/outlit.svg" alt="Measured on agentmods" height="20"></a>
Per session 39 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,217 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00039 $0.01217
Opus 5 $0.00019 $0.00609
Sonnet 5 $0.00008 $0.00243
Haiku 4.5 $0.00004 $0.00122

Measured 4d ago against content hash cdcf3de1c041, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

outlit scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

packages/pi/skills/outlit/SKILL.md · 85 lines

How it starts

The opening of the file, as written. The whole thing — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Outlit

Use Outlit tools to ground customer intelligence work in customer data. Outlit joins product activity, conversations, billing, and web signals into a unified customer context graph and timeline for agents.

Use the registered outlit_* tools as the interface. Do not tell the user to install the Outlit CLI or configure MCP from inside Pi unless they explicitly ask about a different agent environment.

Do not invent customer state when Outlit can answer it. Call out sparse or messy data instead of overstating confidence.

Tool Choice

  • Use outlit_list_customers to discover customers for portfolio, segment, risk, revenue, trial, or account-health questions.
  • Use outlit_list_users for user-level questions or when a customer answer depends on individual users.
  • Use outlit_get_customer before deep analysis of a named customer or account.
  • Use outlit_get_timeline when order, recency, activity sequence, meetings, messages, product usage, support, or billing chronology matters.
  • Use outlit_list_facts to browse structured account facts, known signals, open issues, health indicators, relationship notes, activation, billing, or renewal context. Narrow with status, sourceTypes, and factTypes when you know what evidence class you need.
  • Use outlit_get_fact when you already have a fact id and need the canonical fact payload.
  • Use outlit_search_customer_context for fuzzy or thematic questions such as pricing concern, blocked integration, not using, renewal, champion left, negative sentiment, expansion, implementation, or support escalation.
  • Use outlit_list_sources to discover the source artifacts available for a customer before retrieving one in full.
  • Use outlit_get_source when a fact or search result needs stronger evidence from the underlying source artifact.
  • Use outlit_get_customer_features for exact, customer-level Feature observations. Treat unavailable coverage as unknown, not zero usage.
  • Use outlit_list_features to inspect the confirmed workspace taxonomy, historical evidence, eligible sources, and event candidates.
  • Use outlit_create_feature only when the user explicitly asks to configure one confirmed product capability from one exact event rule. Outlit creates the supporting usage metrics internally.
  • Use outlit_archive_feature only when the user explicitly asks to archive a feature and supplies the current opaque id and revision. The MVP has no restore operation and rejects archiving the final active feature.

Read the full file on GitHub · 85 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 4d ago Changed cdcf3de1c041
  2. 8d ago First seen · 85 lines · 39 tokens per session scan A a5b4250bd24a

Subscribe to this mod's changes

outlit is a skill published in the GitHub repository OutlitAI/outlit-sdk (6 stars, last pushed 2d ago), licensed Apache-2.0. It adds 39 tokens to every session and 1,217 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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