Agent skill

Human Gate

by EmeaAppGbb in EmeaAppGbb/spec2cloud

Pause execution and request human approval at defined checkpoints.

MITAuto-check passedDevelopment

Install Human Gate

skills CLI
$ npx skills add EmeaAppGbb/spec2cloud --skill human-gate -a claude-code

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

GitHub CLI
$ gh skill install EmeaAppGbb/spec2cloud human-gate --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/EmeaAppGbb/spec2cloud.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.github/skills/human-gate .claude/skills/human-gate && 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
human-gate
GitHub stars
100
Token cost
~1.5k tokens
SKILL.md length
698 words
Files
1
Skills in repo
39
Repo updated
First seen
Licence
MIT

At a glance

Pause execution and request human approval at defined checkpoints.

  • Works in 6 steps: Summarize extraction findings (test… → Present the testability checklist → Ask the human to check applicable items… → …
  • Tasks that involve Pull requests
  • SKILL.md covers Gate Locations, Testability Assessment Gate, Green Baseline Verification Gate and How to Pause, plus 2 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Human Gate is an agent skill from EmeaAppGbb/spec2cloud. Pause execution and request human approval at defined checkpoints. Present summaries, state next steps, and record approval or rejection. Use at phase exits, after Gherkin generation, after implementation PR review, and after deployment verification.

Its SKILL.md is about 1.5k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Development, covering Pull requests. The licence is MIT.

When your agent uses it

  • Tasks that involve Pull requests

Example prompts

  • “/human-gate”

Requirements

  • Docker

Workflow steps

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

  1. Summarize extraction findings (test discovery, architecture, dev environment)
  2. Present the testability checklist
  3. Ask the human to check applicable items and select a track
  4. If Hybrid is selected, ask which features (by FRD ID) are testable
  5. Record the decision in state.json and audit.log
  6. Advance to the appropriate track

What it can do on your machine

Read from SKILL.md and the folder at commit 8e76618. 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 json).

    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.

Context cost

Human Gate loads about 1.5k tokens when it runs. Until then it costs about 65 tokens; SKILL.md has 698 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~65
When it runs · the whole SKILL.md, loaded when a task matches
~1.5k

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 EmeaAppGbb/spec2cloud at commit 8e76618, republished under its MIT licence (© EmeaAppGbb). 698 words, ~1,520 tokens.

Download SKILL.mdSave it as .claude/skills/human-gate/SKILL.md (or your agent's skills folder).
name
human-gate
description
Pause execution and request human approval at defined checkpoints. Present summaries, state next steps, and record approval or rejection. Use at phase exits, after Gherkin generation, after implementation PR review, and after deployment verification.

Human Gate Protocol

Gate Locations

Human gates exist at these points:

  • Phase 0 exit (shell setup approval)
  • Phase 1a exit (FRD approval)
  • Phase 1b exit (UI/UX approval)
  • Phase 1c exit (increment plan approval)
  • Phase 1d exit (tech stack resolution approval)
  • Phase 2, Step 1 mid-point (Gherkin approval, per increment)
  • Phase 2, Step 3 exit (implementation PR review, per increment)
  • Phase 2, Step 4 exit (deployment verification, per increment)
  • Phase B1 exit (extraction accuracy review — brownfield only)
  • Phase B2a (PRD approval — brownfield only)
  • Phase B2b (FRD approval — brownfield only)
  • Phase B2c (spec refinement approval — brownfield only)
  • Phase B3 entry (testability assessment — brownfield only)
  • Green baseline verification (per feature after Track A baseline — brownfield only)
  • Track B behavioral docs review (per feature — brownfield only)
  • Path selection (choose modernize/rewrite/extend/etc. — brownfield only)

Testability Assessment Gate

Gate type: testability-assessment

When triggered: After Phase B2 (Spec-Enable) completes — PRD and all FRDs are approved.

What to Present

1. Extraction findings relevant to testability:

  • Test discovery results — existing test frameworks, test count, coverage metrics
  • Architecture overview — external dependencies, integration points, service boundaries
  • Dev environment detection — Docker/compose files, env configs, local run scripts

2. Testability checklist for human to assess:

  • Can the application be built and started locally (or in a dev environment)?
  • Are external dependencies reachable, mockable, or fakeable?
  • Can API endpoints be exercised (HTTP calls return responses)?
  • Can the UI be rendered and interacted with (browser automation possible)?
  • Is there a working dev/test environment configuration?
  • Can the existing test suite (if any) be executed?

3. Decision options:

DecisionCriteriaNext step
Track A (Full testability)All or most checklist items checkedProceed with green baseline
Track B (No testability)Few or no items checkedProceed with documentation-only
HybridSome features testable, others notHuman identifies which features are testable

For Hybrid, also collect: list of testable feature names mapped to FRD IDs.

What to Record

state.json — add to brownfield object:

json
{
  "testability": "full | partial | none",
  "track": "A | B | hybrid",
  "testabilityChecklist": {
    "canBuild": true,
    "externalDepsReachable": true,
    "apiExercisable": true,
    "uiRenderable": false,
    "devEnvExists": true,
    "existingTestsRunnable": false
  },
  "featureTracks": {
    "auth": "A",
    "search": "A",
    "reporting": "B"
  }
}

audit.log entry:

[ISO-timestamp] gate=testability-assessment decision={track} testability={level} result=approved
Gate Flow
  1. Summarize extraction findings (test discovery, architecture, dev environment)
  2. Present the testability checklist
  3. Ask the human to check applicable items and select a track
  4. If Hybrid is selected, ask which features (by FRD ID) are testable
  5. Record the decision in state.json and audit.log
  6. Advance to the appropriate track

Green Baseline Verification Gate

Gate type: green-baseline-verification

When triggered: After Track A completes green baseline execution for each feature (brownfield only).

What to Present
  • Feature name and FRD ID
  • Test suite execution results (pass/fail counts, failures if any)
  • Baseline coverage summary
  • Any tests that were skipped or could not run
Show full SKILL.md (278 more words)Show less
Decision
  • Accept baseline — Tests pass, proceed to increment delivery for this feature
  • Reject baseline — Tests fail or coverage insufficient, iterate on baseline setup
  • Reclassify feature — Move feature from Track A to Track B (not testable after all)
What to Record

audit.log entry:

[ISO-timestamp] gate=green-baseline-verification feature={frd-id} tests_passed={n} tests_failed={n} result={accepted|rejected|reclassified}

How to Pause

When you reach a human gate:

  1. Summarize what was done. Present a concise summary:

    • Phase 0: List all generated/verified files and scaffolding
    • Phase 1a: List all FRDs with their key decisions
    • Phase 1b: List screen map, design system, and prototype links per FRD
    • Phase 1c: List the increment plan with ordering, scope, and dependencies
    • Phase 1d: List tech stack decisions, infrastructure plan, created skills
    • Step 1 (per increment): List Gherkin scenario counts, e2e flow coverage
    • Step 3 (per increment): Link to the PR, list test results (pass/fail counts)
    • Step 4 (per increment): Deployment URL, smoke test results, docs status
    • Testability assessment (brownfield): Extraction findings, checklist, track recommendation
    • Green baseline verification (brownfield): Test results, coverage, per-feature status
  2. State what's next. Tell the human what the next phase will do.

  3. Ask for approval. Explicitly ask: "Approve to proceed to Phase X, or provide feedback to iterate."

  4. Wait. Do not proceed until the human responds.

Recording Approval

When the human approves:

  1. Set humanGates.<gate-name> to true in state.json
  2. Log the approval in audit.log
  3. Advance currentPhase to the next phase
  4. Continue the Ralph loop

On Rejection

When the human rejects or provides feedback:

  1. Log the rejection and feedback in audit.log
  2. Do not advance the phase
  3. Incorporate the feedback into the current phase
  4. Re-execute the relevant tasks with the feedback
  5. When done, present for approval again

© EmeaAppGbb, 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 .github/skills/human-gate of EmeaAppGbb/spec2cloud.

Open the folder on GitHubat commit 8e76618

Compare with similar skills

Human Gate 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.

Human Gate compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Human Gate this skillEmeaAppGbb/spec2cloud100—~1.5kAutomated safety check: PassMIT
PR Babysitteropeninterpreter/openinterpreter69k3 repos~4.2kAutomated safety check: PassApache-2.0
Babysit PR To Pass CIsgl-project/sglang37k2 repos~3kAutomated safety check: PassApache-2.0
Ansible Development Contextansible/ansible71k—~427Automated safety check: PassGPL-3.0
Ansible Backport Creatoransible/ansible71k—~1.2kAutomated safety check: PassGPL-3.0
Cap Feature Building WorkflowCapSoftware/Cap23k—~2.5kAutomated safety check: WarnCustom licence

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Questions about Human Gate

What does Human Gate do?

Pause execution and request human approval at defined checkpoints. Human Gate is an agent skill from EmeaAppGbb/spec2cloud. Pause execution and request human approval at defined checkpoints.

When should I use Human Gate?

Human Gate fits situations like: tasks that involve Pull requests.

How do I install Human Gate in Claude Code?

Run `npx skills add EmeaAppGbb/spec2cloud --skill human-gate -a claude-code`. Or copy the skill folder (.github/skills/human-gate in EmeaAppGbb/spec2cloud) into .claude/skills/human-gate in your project. Claude Code loads it when a task matches its description.

How do I install Human Gate in Codex?

Run `npx skills add EmeaAppGbb/spec2cloud --skill human-gate -a codex`. Or copy the skill folder (.github/skills/human-gate in EmeaAppGbb/spec2cloud) into .agents/skills/human-gate in your project. Codex loads it when a task matches its description.

Can I use Human Gate 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 EmeaAppGbb/spec2cloud --skill human-gate -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/human-gate, .gemini/skills/human-gate, .github/skills/human-gate and .opencode/skills/human-gate in your project.

What does Human Gate need to run?

SKILL.md names no scripts, command-line tools or credentials: Human Gate is instructions for the agent only. Our summary lists: Docker.

Does Human Gate 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 Human Gate 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 Human Gate use?

Human Gate is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Human Gate use?

About 1.5k tokens (SKILL.md is roughly 6.1k 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 Human Gate?

Skills that share tags, products or a category with Human Gate: PR Babysitter (openinterpreter/openinterpreter, 69k stars), Babysit PR To Pass CI (sgl-project/sglang, 37k stars), Ansible Development Context (ansible/ansible, 71k stars) and Ansible Backport Creator (ansible/ansible, 71k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Human Gate?

EmeaAppGbb (a GitHub organization) maintains it in EmeaAppGbb/spec2cloud, which has 100 GitHub stars. The repository holds 39 skills in this directory. The repository was last updated on April 16, 2026.

Source: EmeaAppGbb/spec2cloud on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.