Review traction experiment results and recommend whether to double down, iterate, repeat Bullseye, or pivot.

MITAuto-check passedTesting & QA

Install Traction Review

skills CLI
$ npx skills add hashgraph-online/awesome-codex-plugins --skill traction-review -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install hashgraph-online/awesome-codex-plugins traction-review --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/hashgraph-online/awesome-codex-plugins.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/LVTD-LLC/skills/skills/traction-review .claude/skills/traction-review && rm -rf skills-src

Use ~/.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/

Facts

Skill name
traction-review
GitHub stars
1.3k
Token cost
~543 tokens
SKILL.md length
164 words
Files
1
Skills in repo
716
Repo updated
First seen
Licence
MIT

At a glance

Review traction experiment results and recommend whether to double down, iterate, repeat Bullseye, or pivot.

  • Works in 6 steps: Restate the traction goal and current… → Compare results against the prewritten… → Evaluate customer quality, not only… → …
  • Analyzing growth test data
  • SKILL.md covers Source Traceability, Review Workflow, Output Format and Quality Bar
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Traction Review is an agent skill from hashgraph-online/awesome-codex-plugins. Review traction experiment results and recommend whether to double down, iterate, repeat Bullseye, or pivot. Use when analyzing growth test data, comparing channel performance, deciding if a channel moved the needle, or reassessing startup traction strategy.

Its SKILL.md is about 540 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts. Compatibility notes: Codex, Claude Code, and other Agent Skills-compatible clients.

It sits in Testing & QA, covering Test data and fixtures. The repository describes itself as: A curated list of awesome OpenAI Codex / ChatGPT plugins, skills, and resources. The 1 Codex Marketplace. See live plugins at: https://hol.org/plugins/best-codex-plugins. The licence is MIT.

When your agent uses it

  • Analyzing growth test data
  • Comparing channel performance
  • Deciding if a channel moved the needle
  • Reassessing startup traction strategy

Example prompts

  • “/traction-review”

Requirements

  • Compatibility (from SKILL.md): Codex, Claude Code, and other Agent Skills-compatible clients.

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

  1. Restate the traction goal and current growth stage.
  2. Compare results against the prewritten success and kill thresholds.
  3. Evaluate customer quality, not only volume.
  4. Decide whether the result moved the needle for the current stage.
  5. Identify bright spots: small customer groups with unusually strong
  6. Recommend one decision

What it can do on your machine

Read from SKILL.md and the folder at commit 3e1456a. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Codex, Claude Code, and other Agent Skills-compatible clients.

    From compatibility in the SKILL.md frontmatter.

Context cost

Traction Review loads about 543 tokens when it runs. Until then it costs about 69 tokens; SKILL.md has 164 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~69
When it runs · the whole SKILL.md, loaded when a task matches
~543

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.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated 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.

SKILL.md

The full file from hashgraph-online/awesome-codex-plugins at commit 3e1456a, republished under its MIT licence (© hashgraph-online). 164 words, ~543 tokens.

Download SKILL.mdSave it as .claude/skills/traction-review/SKILL.md (or your agent's skills folder).
name
traction-review
description
Review traction experiment results and recommend whether to double down, iterate, repeat Bullseye, or pivot. Use when analyzing growth test data, comparing channel performance, deciding if a channel moved the needle, or reassessing startup traction strategy.
compatibility
Codex, Claude Code, and other Agent Skills-compatible clients.
license
MIT
metadata.version
0.1.0
metadata.displayName
Traction Review
metadata.category
Marketing
metadata.tags
traction,growth,experiments,analytics,strategy

Traction Review

Use this skill after traction tests have run. The goal is to turn results into a decision: focus, iterate, repeat Bullseye, or reconsider the product or market.

Source Traceability

Primary source: Traction, chapters 2-5. Authoring notes: converted lines 394-902.

Review Workflow

  1. Restate the traction goal and current growth stage.
  2. Compare results against the prewritten success and kill thresholds.
  3. Evaluate customer quality, not only volume.
  4. Decide whether the result moved the needle for the current stage.
  5. Identify bright spots: small customer groups with unusually strong engagement, conversion, retention, or willingness to pay.
  6. Recommend one decision:
    • double down on the core channel,
    • run inner-ring optimization,
    • run another middle-ring test,
    • repeat Bullseye with new information,
    • revisit product, market, or positioning.

Output Format

markdown
# Traction Review

## Goal And Context
- Goal:
- Stage:
- Test period:

## Results
| Channel/Strategy | Cost | Reach | Conversion | Customer Fit | Notes |
|------------------|------|-------|------------|--------------|-------|

## Decision
[Double down, iterate, repeat Bullseye, or revisit product/market.]

## Reasoning
- What moved the needle:
- What failed:
- Bright spots:
- Risks:

## Next Actions
1. [Action]
2. [Action]
3. [Action]

Quality Bar

  • Do not declare success from vanity metrics.
  • Do not pivot before checking bright spots and engagement evidence.
  • Do not keep secondary channels alive just because they somewhat worked.
  • Reassess the Critical Path after meaningful evidence.

© hashgraph-online, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in plugins/LVTD-LLC/skills/skills/traction-review of hashgraph-online/awesome-codex-plugins.

Open the folder on GitHubat commit 3e1456a

Compare with similar skills

Traction Review 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.

Traction Review compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Traction Review this skillhashgraph-online/awesome-codex-plugins1.3k—~543Automated safety check: PassMIT
Fs Fixtureprivatenumber/fs-fixture100—~1.2kAutomated safety check: PassMIT
Dev Tenant APInightscout/nocturne139—~1.4kAutomated safety check: PassNone
Rsibench Data Factoryevolvent-ai/RSIBench-Data171—~640Automated safety check: NotesNone
Eval Designagentscope-ai/OpenJudge871—~2.8kAutomated safety check: WarnApache-2.0
Data GenerationRed-Hat-AI-Innovation-Team/sdg_hub164—~381Automated safety check: PassApache-2.0

Similar skills

  • Fs Fixture

    privatenumber/fs-fixture

    Create disposable file system test fixtures from objects, templates, or empty directories with automatic cleanup.

    100 GitHub stars~1.2k tokensUpdated 1 mo ago
    Testing & QAAuto-check passed
  • Dev Tenant API

    nightscout/nocturne

    Interact with Nocturne's local dev-only API: seed a loginable tenant preloaded with realistic sample data, obtain a browser session (loginLink) or bearer token headlessly, export/re-seed the dev…

    139 GitHub stars~1.4k tokensUpdated yesterday
    Testing & QAAuto-check passed
  • Rsibench Data Factory

    evolvent-ai/RSIBench-Data

    Use inside RSIBench-Data when testing whether an automation agent can improve a target model on a configured benchmark through synthetic Tinker SFT data, Tinker sampling, and E2B-based Harbor…

    171 GitHub stars~640 tokensUpdated 1 mo ago
    Testing & QAAuto-check: notes
  • Eval Design

    agentscope-ai/OpenJudge

    A skill your agent uses when the user needs to design evaluation datasets, create test cases, stratify samples, generate adversarial examples, extract eval dimensions from traces/specs, or build a…

    871 GitHub stars~2.8k tokensUpdated 29 days ago
    Testing & QAAuto-check: warnings
  • Data Generation

    Red-Hat-AI-Innovation-Team/sdg_hub

    A skill your agent uses when the user wants to run synthetic data generation via scripts — detect environment, execute a flow, and present results.

    164 GitHub stars~381 tokensUpdated yesterday
    Testing & QAAuto-check passed
  • Official

    Use this skill before answering or editing whenever an MSTest v1/v2 project is being upgraded or repaired for v3.

    5.6k GitHub starsUsed in 1 repo~5.5k tokens
    Testing & QAAuto-check passed

More from hashgraph-online/awesome-codex-plugins

All 716 skills in this repo
  • Anime Reaction Gif

    hashgraph-online/awesome-codex-plugins

    Create original anime-style reaction stickers as looping GIFs and MP4 previews, using generated character pose sheets and timed key poses.

    1.3k GitHub stars~922 tokensUpdated today
    Auto-check passed
  • Calibredb

    hashgraph-online/awesome-codex-plugins

    Manage and query Calibre libraries with the calibredb CLI (local paths or Calibre Content server URLs).

    1.3k GitHub stars~1k tokensUpdated today
    Auto-check passed
  • Rust API Test Harness

    hashgraph-online/awesome-codex-plugins

    A skill your agent uses when adding, changing, testing, or debugging Rust HTTP APIs and services, especially when Codex needs black-box integration tests, random-port app startup, real database test…

    1.3k GitHub stars~1.7k tokensUpdated today
    Auto-check passed
  • Art

    hashgraph-online/awesome-codex-plugins

    Make a studio's game look like something at build time — a cover from a real frame of the game (free), painted covers, backdrops, textures and character plates from image models through the…

    1.3k GitHub stars~2.4k tokensUpdated today
    Auto-check passed
  • Calle

    hashgraph-online/awesome-codex-plugins

    Use CALL-E from Codex through the calle CLI. An agent skill from hashgraph-online/awesome-codex-plugins.

    1.3k GitHub stars~2.9k tokensUpdated today
    Auto-check passed
  • Game Balance Economy

    hashgraph-online/awesome-codex-plugins

    Balance game difficulty, resources, rewards, probability, progression, economies, and dominant strategies.

    1.3k GitHub stars~618 tokensUpdated today
    Auto-check passed

Categories

Questions about Traction Review

What does Traction Review do?

Review traction experiment results and recommend whether to double down, iterate, repeat Bullseye, or pivot. Traction Review is an agent skill from hashgraph-online/awesome-codex-plugins. Review traction experiment results and recommend whether to double down, iterate, repeat Bullseye, or pivot.

When should I use Traction Review?

Traction Review fits situations like: analyzing growth test data; comparing channel performance; deciding if a channel moved the needle; reassessing startup traction strategy.

How do I install Traction Review in Claude Code?

Run `npx skills add hashgraph-online/awesome-codex-plugins --skill traction-review -a claude-code`. Or copy the skill folder (plugins/LVTD-LLC/skills/skills/traction-review in hashgraph-online/awesome-codex-plugins) into .claude/skills/traction-review in your project. Claude Code loads it when a task matches its description.

How do I install Traction Review in Codex?

Run `npx skills add hashgraph-online/awesome-codex-plugins --skill traction-review -a codex`. Or copy the skill folder (plugins/LVTD-LLC/skills/skills/traction-review in hashgraph-online/awesome-codex-plugins) into .agents/skills/traction-review in your project. Codex loads it when a task matches its description.

Can I use Traction Review in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add hashgraph-online/awesome-codex-plugins --skill traction-review -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/traction-review, .gemini/skills/traction-review, .github/skills/traction-review and .opencode/skills/traction-review in your project.

What does Traction Review need to run?

SKILL.md names no scripts, command-line tools or credentials: Traction Review is instructions for the agent only. Compatibility (from SKILL.md): Codex, Claude Code, and other Agent Skills-compatible clients..

Does Traction Review access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Traction Review safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Traction Review use?

Traction Review is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Traction Review use?

About 543 tokens (SKILL.md is roughly 2.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Traction Review?

Skills that share tags, products or a category with Traction Review: Fs Fixture (privatenumber/fs-fixture, 100 stars), Dev Tenant API (nightscout/nocturne, 139 stars), Rsibench Data Factory (evolvent-ai/RSIBench-Data, 171 stars) and Eval Design (agentscope-ai/OpenJudge, 871 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Traction Review?

hashgraph-online (a GitHub organization) maintains it in hashgraph-online/awesome-codex-plugins, which has 1,267 GitHub stars. The repository holds 716 skills in this directory. The repository was last updated on October 10, 2026.

Source: hashgraph-online/awesome-codex-plugins on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.