featbit-experimentation

featbit-experimentation is a skill for Claude Code, Codex from featbit/featbit-skills. It costs 68 tokens per session (7,139 once invoked), scanned A, original, MIT.

A workflow for planning and evaluating product experiments with FeatBit, a feature-management and experimentation platform. It connects the product goal, hypothesis, rollout, measurements, results, decision, and recorded learning.

In plain words
What is it for?
Use it to shape an experiment, define feature flags and metrics, manage exposure, analyse results, decide whether to continue or roll back, and record learnings.
Why use it?
It gives teams a defined path from an idea to a release decision. It helps prevent premature implementation, unclear measurements, and decisions made from insufficient experiment data.

Skill for Claude CodeCodex

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

Good fit Use it to shape an experiment, define feature flags and metrics, manage exposure, analyse results, decide whether to continue or roll back, and record learnings.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/featbit/featbit-skills/featbit-experimentation
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 featbit/featbit-skills --skill featbit-experimentation
Clone the repo
git clone --depth 1 https://github.com/featbit/featbit-skills

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 featbit-experimentation

README.md
[![agentmods](https://agentmods.dev/badge/skills/featbit/featbit-skills/featbit-experimentation/github.svg)](https://agentmods.dev/skills/featbit/featbit-skills/featbit-experimentation)
Your own site
<a href="https://agentmods.dev/skills/featbit/featbit-skills/featbit-experimentation"><img src="https://agentmods.dev/badge/skills/featbit/featbit-skills/featbit-experimentation/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for featbit-experimentation

Your own site · 80×15
<a href="https://agentmods.dev/skills/featbit/featbit-skills/featbit-experimentation"><img src="https://agentmods.dev/badge/skills/featbit/featbit-skills/featbit-experimentation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 68 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 7,139 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. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 3 findings, up to high

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • high Privilege Escalation · line 56
    Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.
    Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
  • high Privilege Escalation · line 561
    Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.
    Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
  • medium Excessive Agency · line 463
    Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.
    Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
How audits are shown
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 $0.00068 $0.07139
Opus 5.5 $0.00027 $0.02856
Sonnet 5.5 $0.00014 $0.01428
Haiku 4.5 $0.00007 $0.00714

Measured 1mo ago against content hash 9d39943b8e2e, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

featbit-experimentation 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 1mo 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.

skills/featbit-experimentation/SKILL.md · 562 lines

How it starts

The opening of the file, as written. The whole thing — 562 lines — stays where its author put it; the contents beside it link to each section on GitHub.

FeatBit Experimentation

This skill is the single entry point for the FeatBit release-decision loop:

intent -> hypothesis -> implementation -> exposure -> measurement -> interpretation -> decision -> learning -> next intent

It replaces the old multi-skill layout (featbit-release-decision, intent-shaping, hypothesis-design, reversible-exposure-control, measurement-design, experiment-workspace, evidence-analysis, learning-capture, and project-sync). Do not call those split skills from this workflow. Treat their former behavior as internal stages of this skill.

Core Principles

  • Do not let an available tool define the user's problem before the decision type is clear.
  • Do not implement before the business intent and hypothesis are explicit.
  • Do not expose a measurable change before reversibility and rollout rules are defined.
  • Do not measure with multiple primary metrics. One metric decides; guardrails protect.
  • Use bayesian_ab for every run; this is the only supported analysis method. Do not ask the user to choose an analysis method.
  • Do not interpret experiment data before sufficiency, SRM, instrumentation, and window checks pass.
  • Do not close a cycle without recording a reusable learning and a next hypothesis.
  • Persist every stage transition through the configured FeatBit experimentation MCP server.

MCP Contract

The FeatBit API database is the canonical source for experiment state. Read it on entry and write it before moving stages.

Required MCP tools:

Tool Purpose
featbit_experiment_get_experiment Read experiment state, runs, messages, setup mode, and pasted input data
featbit_experiment_update_experiment Write goal, intent, hypothesis, constraints, lastAction, lastLearning
featbit_experiment_set_stage Set lifecycle stage
featbit_experiment_update_metrics Write primary metric and guardrails
featbit_experiment_create_run Create an experiment run
featbit_experiment_update_run Update run setup, status, decision, and learning fields
featbit_experiment_update_run_traffic Configure a run's experiment traffic assignment: analysis method, control/treatment roles, layer id/key, bucket slice, assignment unit, audience filters, allocation plan, and per-variation analysis sampling
featbit_experiment_analyze_run Run server-side analysis and persist inputData / analysisResult
featbit_experiment_list_layers List registered release-decision layers for the experiment environment
featbit_experiment_create_layer Create a registered layer after explicit user approval
featbit_experiment_update_layer Update a registered layer after explicit user approval
featbit_experiment_archive_layer Archive a registered layer after explicit user approval
featbit_experiment_get_feature_flag Read the real FeatBit flag, revision, variations, and targeting for the experiment environment
featbit_experiment_create_feature_flag Create a FeatBit-managed feature flag after the exposure contract is complete and the user explicitly approves
featbit_experiment_update_feature_flag_targeting Update flag targeting/rollout directly, or create a change request when useChangeRequest or reviewers are provided, after explicit user approval
featbit_experiment_toggle_feature_flag Enable or disable the FeatBit flag after targeting is configured, or during pause/rollback execution, after explicit user approval

Read the full file on GitHub · 562 lines

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. 1mo ago Changed · +2 tokens per session 9d39943b8e2e
  2. 1mo ago First seen · 562 lines · 66 tokens per session scan A 0c95bfa8fdac

Subscribe to this mod's changes

featbit-experimentation is a skill published in the GitHub repository featbit/featbit-skills (12 stars, last pushed 1mo ago), licensed MIT. It adds 68 tokens to every session and 7,139 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-08-30.

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