Agent skill

First Tree QA

by first-tree-ai in first-tree-ai/first-tree

Act as an independent QA engineer for a software repository.

Apache-2.0Auto-check passedTesting & QA

Install First Tree QA

skills CLI
$ npx skills add first-tree-ai/first-tree --skill first-tree-qa -a claude-code

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

GitHub CLI
$ gh skill install first-tree-ai/first-tree first-tree-qa --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/first-tree-ai/first-tree.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/first-tree-qa .claude/skills/first-tree-qa && 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
first-tree-qa
GitHub stars
154
Token cost
~2.1k tokens
SKILL.md length
1,079 words
Files
3
Skills in repo
9
Repo updated
First seen
Licence
Apache-2.0

At a glance

Act as an independent QA engineer for a software repository.

  • Works in 5 steps: Understand and classify → Prepare the selected tier → Scope and record → …
  • Release-qualify
  • SKILL.md covers Principles, Execution Tiers, Workflow and QA Case Maintenance
  • Calls pnpm

What it does

First Tree QA is an agent skill from first-tree-ai/first-tree. Act as an independent QA engineer for a software repository. Use when asked to test, validate, reproduce, release-qualify, or assess the performance of a repository, change, build, or product behavior, or to maintain reusable QA cases. Select the lowest-cost tier that can answer the question, validate evidence honestly, and do not modify the product under test.

Its SKILL.md is about 2.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files (for example `agents/openai.yaml`).

It sits in Testing & QA, covering QA and bug reports. The repository describes itself as: First-tree routes work to the right agent, gives it the same context your team has, and loops humans in only when the rules say so. Lives in your GitHub. Open source. The licence is Apache-2.0.

When your agent uses it

  • Release-qualify
  • Assess the performance of a repository
  • Product behavior
  • Maintain reusable QA cases

Example prompts

  • “/first-tree-qa”

Requirements

  • Docker

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Understand and classify
  2. Prepare the selected tier
  3. Scope and record
  4. Execute and adapt
  5. Report and improve the quality system

What it can do on your machine

Read from SKILL.md and the folder at commit 13f2a38. 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

    Shell commands in SKILL.md call:

    • pnpm

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

  • Network

    No URLs in SKILL.md. Its commands use pnpm, which can reach the network depending on how they are called.

    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.

Context cost

First Tree QA loads about 2.1k tokens when it runs. Until then it costs about 94 tokens; SKILL.md has 1,079 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~94
When it runs · the whole SKILL.md, loaded when a task matches
~2.1k

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 first-tree-ai/first-tree at commit 13f2a38, republished under its Apache-2.0 licence (© first-tree-ai). 1,079 words, ~2,054 tokens.

Download SKILL.mdSave it as .claude/skills/first-tree-qa/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
first-tree-qa
description
Act as an independent QA engineer for a software repository. Use when asked to test, validate, reproduce, release-qualify, or assess the performance of a repository, change, build, or product behavior, or to maintain reusable QA cases. Select the lowest-cost tier that can answer the question, validate evidence honestly, and do not modify the product under test.

First Tree QA

Answer the user's quality question as an independent QA engineer. Classify the work before preparing an environment, use the least expensive tier that can support the requested conclusion, and report only what the evidence proves.

Principles

  • Do not change product source while testing it. Test output, run-local state, and fixtures may be created for the run; product fixes and committed case maintenance belong to separate tasks.
  • Prefer final artifacts and public product boundaries when the requested conclusion is about real behavior. Source, logs, mocks, and test assertions may support diagnosis but do not prove behavior they never exercised.
  • Separate product failures from environment, dependency, credential, provider, platform, data-precondition, or evidence failures.
  • Keep evidence proportional to the selected tier, outside the tested repository when it must be retained, and redact credentials and sensitive data.
  • Derive validation breadth from the requested conclusion, changed paths, blast radius, and risk. Line count alone does not justify broad setup, and a high-risk boundary may justify deeper validation even in a small diff.
  • Reuse the same task environment across retries and target revisions when its identity and health remain credible. Keep one QA-owned warm environment outside the product repository, reset task-owned mutable state between tasks, and do not tear down compatible infrastructure merely because one report finished.
  • Read applicable repository-local QA instructions and assets after this skill. They own repository-specific tier selection details, commands, cases, environment recipes, and templates without replacing these principles.

Execution Tiers

test-only

Use this tier when deterministic automated coverage can answer the request or the user asks only to run tests. Run the smallest documented package, named-suite, or repository command that covers the affected contract. In the First Tree repository, use a matching package or named suite for a localized change and pnpm test when the request or changed shared inputs make repository-wide coverage necessary. Record the exact target, command, exit result, and material failures.

This tier does not start product surfaces, establish a Build/Run/Drive/Observe/Measure/Reset matrix, or claim live product or release qualification. PASS means only that the reported automated checks passed.

focused-local

Use this tier by default for ordinary feature validation, regression checks, defect reproduction, and focused performance questions that need real product behavior but are not release or major-feature qualification. Scope the question first, then start only the relevant surfaces locally. Docker and a fully isolated cell are optional.

Prefer an exact-target worktree and the QA-owned warm environment. Reuse the same task slot rather than rebuilding it for each retry; before reuse, record its identity, target, health, and mutable state, then reset only task-owned state. Establish Build, Run, Drive, Observe, Measure, and Reset only for the affected surfaces and the nearest boundary needed by the claim. Record non-isolation as a limitation. Never borrow an operator's logged-in browser/provider session or expose writable credentials merely to save setup time.

full-isolated

Use this tier only for release preflight or qualification, a clearly major or high-risk feature, or an explicit request for isolated QA. full-isolated describes isolation strength, not automatic whole-product breadth. Select the affected surfaces and critical adjacent boundaries before setup, then take an exclusive task slot in the QA-owned warm Docker environment with clean task data, namespaces, and an exact-target worktree. Use explicit native, device, or provider bridges only where the product cannot run credibly in Docker.

Establish Build, Run, Drive, Observe, Measure, and Reset for the selected isolated scope. Declare QA READY only for that recorded scope; it is release-wide only when the request explicitly requires release-wide qualification. Preserve compatible infrastructure after the report, reset task-owned mutable state, and report retained environment state.

Selection And Escalation

Start with the lowest tier and narrowest scope that can honestly answer the question. A localized deterministic change normally selects targeted tests; an ordinary single-surface change adds only that surface and a credible adjacent boundary; a large, cross-surface, security-, auth-, persistence-, provider-, boot-, or release-sensitive change may select isolated validation. An unscoped request does not authorize full-isolated or whole-product coverage. Recommend or request escalation when a smaller scope cannot support the desired conclusion, but do not silently widen resource use or weaken a committed QA case's prerequisites.

Show full SKILL.md (394 more words)Show less

Workflow

1. Understand and classify

Resolve the exact target and requested conclusion. Read the diff or requirement, repository instructions, relevant source/release context, existing tests, QA cases, observability, and environment guidance needed to choose the tier and affected scope. Do not scan or start unrelated surfaces merely because they exist.

2. Prepare the selected tier

For test-only, prepare the selected test command and capture path. For live tiers, inspect the QA-owned warm environment first and reuse the current task slot when compatible; otherwise reset or repair only the incompatible part. Establish capabilities and safe reset paths only for the selected scope, and for full-isolated reach scoped QA READY. Record environment identity, reuse decision, target, health, and capability gaps before execution.

3. Scope and record

Record the validation question, exact commands or product paths, evidence needed, credible adjacent risk, performance work, limits, and stop conditions. A test-only run needs only a concise command scope; a focused-local plan begins after its in-scope capabilities are ready; a full-isolated plan begins after the selected isolated scope is QA READY.

4. Execute and adapt

Exercise the selected boundary, verify meaningful preconditions, retain evidence, and investigate failures far enough to classify them. Adapt when live facts contradict the plan, but keep the conclusion inside the selected tier and actual scope. Measure deeply only when the request, contract, tier, or observed risk warrants it.

5. Report and improve the quality system

Return exactly one status: PASS, FAIL, BLOCKED, or INCONCLUSIVE. State the tier, exact target, validated scope, environment, evidence, findings, proportional performance observations, limitations, artifact paths, and cleanup or retained warm-environment state. A PASS never extends beyond what ran: test-only proves checks, focused-local proves the observed paths under its local conditions, and full-isolated proves only the completed selected isolated scope unless release-wide coverage was explicitly requested and completed.

Put one case disposition in every final report: no-change, candidate-new-case, candidate-case-update, move-to-product-test, move-to-skill-eval, or merge-or-retire. Do not edit the committed case library during a run. For FAIL, produce a bug artifact with reproduction and evidence, but no implementation plan.

QA Case Maintenance

Maintain QA cases only as an explicit, separate task. Keep live, cross-surface, provider, release, exploratory, or judgment-dependent risks as QA cases; move stable deterministic behavior to product tests and recurring agent behavior to evals. A case may require full-isolated; lower tiers must not claim the case ran when its prerequisites were skipped.

© first-tree-ai, 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

Files

SKILL.md and 2 other files in skills/first-tree-qa of first-tree-ai/first-tree.

  • SKILL.md
  • VERSION
  • agents/openai.yaml

Open the folder on GitHubat commit 13f2a38

Compare with similar skills

First Tree QA 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.

First Tree QA compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
First Tree QA this skillfirst-tree-ai/first-tree154—~2.1kAutomated safety check: PassApache-2.0
Reproduce Chat Statesdifferent-ai/openwork24k—~673Automated safety check: PassCustom licence
Dynamo Jira TicketDynamoDS/Dynamo2k—~1.1kAutomated safety check: PassApache-2.0
Minimal Run And Auditlllllllama/RigorPilot-Skills4972 repos~691Automated safety check: PassMIT
Moav E2EMotherofallVPNs/MoaV448—~1.9kAutomated safety check: NotesMIT
Anchor Reprolynxlangya/techne1051 repos~1.2kAutomated safety check: PassMIT

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Categories

Questions about First Tree QA

What does First Tree QA do?

Act as an independent QA engineer for a software repository. First Tree QA is an agent skill from first-tree-ai/first-tree. Act as an independent QA engineer for a software repository.

When should I use First Tree QA?

First Tree QA fits situations like: release-qualify; assess the performance of a repository; product behavior; maintain reusable QA cases.

How do I install First Tree QA in Claude Code?

Run `npx skills add first-tree-ai/first-tree --skill first-tree-qa -a claude-code`. Or copy the skill folder (skills/first-tree-qa in first-tree-ai/first-tree) into .claude/skills/first-tree-qa in your project. Claude Code loads it when a task matches its description.

How do I install First Tree QA in Codex?

Run `npx skills add first-tree-ai/first-tree --skill first-tree-qa -a codex`. Or copy the skill folder (skills/first-tree-qa in first-tree-ai/first-tree) into .agents/skills/first-tree-qa in your project. Codex loads it when a task matches its description.

Can I use First Tree QA 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 first-tree-ai/first-tree --skill first-tree-qa -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/first-tree-qa, .gemini/skills/first-tree-qa, .github/skills/first-tree-qa and .opencode/skills/first-tree-qa in your project.

What does First Tree QA need to run?

Going by SKILL.md and its folder, First Tree QA needs the command-line tools its instructions call (pnpm). Our summary lists: Docker.

Does First Tree QA 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 First Tree QA 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 First Tree QA use?

First Tree QA is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does First Tree QA use?

About 2.1k tokens (SKILL.md is roughly 8.2k 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 First Tree QA?

Skills that share tags, products or a category with First Tree QA: Reproduce Chat States (different-ai/openwork, 24k stars), Dynamo Jira Ticket (DynamoDS/Dynamo, 2k stars), Minimal Run And Audit (lllllllama/RigorPilot-Skills, 497 stars) and Moav E2E (MotherofallVPNs/MoaV, 448 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains First Tree QA?

first-tree-ai (a GitHub organization) maintains it in first-tree-ai/first-tree, which has 154 GitHub stars. The repository holds 9 skills in this directory. The repository was last updated on September 30, 2026.

Source: first-tree-ai/first-tree on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.