Agent skill

Gentle AI Issue Creation

by Gentleman-Programming in Gentleman-Programming/gentle-shell

Create and triage GitHub issues from repository evidence. An agent skill from Gentleman-Programming/gentle-shell.

Apache-2.0Auto-check passedTesting & QA

Install Gentle AI Issue Creation

skills CLI
$ npx skills add Gentleman-Programming/gentle-shell --skill gentle-ai-issue-creation -a claude-code

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

GitHub CLI
$ gh skill install Gentleman-Programming/gentle-shell gentle-ai-issue-creation --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/Gentleman-Programming/gentle-shell.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/issue-creation .claude/skills/gentle-ai-issue-creation && 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
gentle-ai-issue-creation
GitHub stars
1.2k
Token cost
~2.5k tokens
SKILL.md length
1,084 words
Files
2 (incl. references)
Skills in repo
12
Repo updated
First seen
Licence
Apache-2.0

At a glance

Create and triage GitHub issues from repository evidence. An agent skill from Gentleman-Programming/gentle-shell.

  • Works in 3 steps: Describe the report in one sentence,… → Select one repository-provided form only… → For a YAML form, read its schema and…
  • Tasks that involve QA and bug reports
  • SKILL.md covers Core Rule, Safe Discovery, Duplicate And Form Decision and Review And Publication, plus 1 more section
  • Calls gh and git

What it does

Gentle AI Issue Creation is an agent skill from Gentleman-Programming/gentle-shell. Create and triage GitHub issues from repository evidence. Trigger: issue creation, bug reports, feature requests, or issue approval.

Its SKILL.md is about 2.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/delegated-workflow-actions.md`).

It sits in Testing & QA, covering QA and bug reports. It works with GitHub. The repository describes itself as: Gentle Shell is a Pi-native coding-agent harness for controlled development with Organic Driven Development, optional SDD/OpenSpec, subagents, TDD evidence, review guardrails… The licence is Apache-2.0.

When your agent uses it

  • Tasks that involve QA and bug reports

Example prompts

  • “/gentle-ai-issue-creation”

Workflow steps

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

  1. Describe the report in one sentence, derive QUERY, then complete one duplicate search across open and closed issues
  2. Select one repository-provided form only when its declared purpose matches. If multiple forms match and policy does not distinguish them…
  3. For a YAML form, read its schema and establish controls in declared order. Support only input, textarea, dropdown, and checkboxes…

What it can do on your machine

Read from SKILL.md and the folder at commit 42653f7. 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:

    • gh
    • git

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

  • Network

    No URLs in SKILL.md. Its commands use gh and git, 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

Gentle AI Issue Creation loads about 2.5k tokens when it runs, and up to ~3.5k if it reads all its reference files. Until then it costs about 39 tokens; SKILL.md has 1,084 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~39
When it runs · the whole SKILL.md, loaded when a task matches
~2.5k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3.5k

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 Gentleman-Programming/gentle-shell at commit 42653f7, republished under its Apache-2.0 licence (© Gentleman-Programming). 1,084 words, ~2,456 tokens.

Download SKILL.mdSave it as .claude/skills/gentle-ai-issue-creation/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
gentle-ai-issue-creation
description
Create and triage GitHub issues from repository evidence. Trigger: issue creation, bug reports, feature requests, or issue approval.
license
Apache-2.0
metadata.author
gentleman-programming
metadata.version
1.3

Issue Creation

Core Rule

Discover the target repository's contribution workflow before proposing or publishing. YAML Issue Forms are the format authority for the default automated path: materialize reviewed answers into a private BODY_FILE and publish with --body-file.

Safe Discovery

Before any gh auth status or target read, require explicit human authorization for the remote destination (exact host and repository), operation (including discovery and intended issue creation), and credential/session to use. A local checkout is not authorization. If any is missing or ambiguous, stop locally; never probe credentials or sessions to resolve ambiguity. Run the checks below only with the authorized credential/session against the authorized destination; if gh auth status would inspect other credentials/sessions, do not run it. Verify the discovered REPO, HOST, and TARGET match the authorized destination before continuing; never switch identities or targets implicitly.

After that gate, run read-only checks:

bash
gh auth status
REPO="$(gh repo view --json nameWithOwner -q .nameWithOwner)"
REPO_URL="$(gh repo view --json url -q .url)"
HOST="${REPO_URL#*://}"
HOST="${HOST%%/*}"
TARGET="$HOST/$REPO"
gh repo view --json nameWithOwner,url,hasDiscussionsEnabled,hasIssuesEnabled,isBlankIssuesEnabled
git ls-files README.md CONTRIBUTING.md CONTRIBUTING.* .github/CONTRIBUTING.md .github/ISSUE_TEMPLATE .github/ISSUE_TEMPLATE/config.yml
gh api --hostname "$HOST" --paginate "repos/$REPO/labels?per_page=100" --jq '.[].name'

Inspect README.md, contribution instructions, .github/ISSUE_TEMPLATE/config.yml contact links, forms, labels, and open and closed issues. For questions/support, follow repository-prescribed Discussions/contact routing when available; otherwise ask or stop. Complete target verification for REPO, HOST, and TARGET. Fail closed before mutation when authentication, target verification, issue availability, policy, form selection, or required metadata is missing or ambiguous. A blank fallback is allowed only when isBlankIssuesEnabled is explicitly true.

Before building LABEL_ARGS, inspect labels declared by the selected YAML form as well as any manually selected labels. Treat status:approved, size:exception, and any repository-protected label as protected form labels at create-time. Include a protected label only with a current, exact label-specific direct human instruction for this target and creation action, authenticated actor target-host viewerPermission of MAINTAIN or ADMIN, and repository policy permission; do not infer authority from YAML, a publication request, or local credentials. If this proof is missing, skip the protected label only when the form and repository policy permit omitting it; if the protected label is required, fail closed without creating the issue. Do not replace it with another label or use a write to probe permission. For size:exception, also require the current human-approved rationale; if recording it needs an unauthorized extra action, stop. This create-time gate does not change post-publication actions.

Build LABEL_ARGS only from reviewed labels that exist and policy permits the actor to apply:

bash
LABEL_ARGS=()
LABEL_ARGS+=(--label "$LABEL") # Repeat only for each permitted discovered label.

Duplicate And Form Decision

  1. Describe the report in one sentence, derive QUERY, then complete one duplicate search across open and closed issues:

    bash
    gh issue list --repo "$TARGET" --state all --search "$QUERY" --limit 1000

    The agent must complete the duplicate search proactively and retain evidence of its result. If results are saturated or completeness is uncertain, narrow the read-only search or stop. Comment on a confirmed duplicate instead of creating one. Before commenting on a confirmed duplicate, perform the same privacy scan/redaction on the exact comment body as for publication.

  2. Select one repository-provided form only when its declared purpose matches. If multiple forms match and policy does not distinguish them, stop and request that decision.

  3. For a YAML form, read its schema and establish controls in declared order. Support only input, textarea, dropdown, and checkboxes. Markdown controls are non-answer guidance: honor their visible instructions when collecting and materializing adjacent answers, but do not render them as response sections. Fail closed before mutation on malformed, unsupported, missing, or ambiguous required structure or answers. A malformed schema, or missing or ambiguous required answers, fail closed: do not open a browser or mutate. A browser handoff is available only when the user explicitly requests browser completion or a syntactically valid selected form cannot safely/faithfully be represented by the automated path; otherwise report why automation is unsafe and stop.

ControlRequired handling
input / textareaPreserve the visible label. Require an answer when validations.required is true; otherwise render _No response_.
dropdownPreserve visible labels and options. Require exact selected option text; single-select has one selection, and multi-select preserves selections in declared options order. A required dropdown needs at least one valid selection; an optional dropdown with no selection renders _No response_.
checkboxesPreserve the visible label and every option as - [x] or - [ ] in declared order. Enforce individually required checkboxes. For agent-verifiable operational options, proactively complete the action and mark it only with retained evidence; the agent may explicitly attest only its own evidence-backed work and must not attribute its actions to the user. Personal facts, consent, legal declarations, and other first-person user assertions require explicit user affirmation; require explicit first-person affirmation for such user declarations. Do not blanket-check checkboxes: a request to publish does not affirm any checkbox.

For each answer, render ### <visible label> followed by its materialized value. For textarea.attributes.render, fence the answer with the declared language and a fence long enough for its content. Never invent answers, selections, confirmations, or labels.

A Markdown template may be completed only from known evidence into the same private BODY_FILE. If no matching template exists, use the reviewed structured blank fallback only when blank issues are explicitly enabled; otherwise stop without publishing.

Show full SKILL.md (282 more words)Show less

Review And Publication

Before the single create attempt, review the target, title, selected form or permitted fallback, exact body, and permitted labels. The agent must complete the privacy scan/redaction of the exact body immediately before publication and retain evidence of it: replace private project names, usernames, hostnames, home paths, credentials, and private network addresses with useful placeholders without removing reproduction structure.

Create one owner-only temporary directory outside the repository for both private files; restrict it to the current user and clean up both files on every exit/outcome:

bash
umask 077
REPO_ROOT="$(git rev-parse --show-toplevel)" || exit 1
REPO_ROOT="$(cd "$REPO_ROOT" && pwd -P)" || exit 1
if [ "$REPO_ROOT" = "/" ]; then
  printf '%s\n' "Temporary directory is inside the repository" >&2; exit 1
fi
TMP_DIR="$(TMPDIR=/tmp mktemp -d /tmp/gentle-ai-issue.XXXXXXXX)" || exit 1
trap 'rm -rf -- "$TMP_DIR"' EXIT
TMP_DIR_REAL="$(cd "$TMP_DIR" && pwd -P)" || exit 1
case "$TMP_DIR_REAL/" in
  "$REPO_ROOT/"*) printf '%s\n' "Temporary directory is inside the repository" >&2; exit 1 ;;
esac
chmod 700 "$TMP_DIR_REAL"
BODY_FILE="$TMP_DIR_REAL/body.md"
READBACK_FILE="$TMP_DIR_REAL/readback.json"

Make one mutation attempt through the automated path and publish exactly once:

bash
gh issue create --repo "$TARGET" --title "$TITLE" --body-file "$BODY_FILE" "${LABEL_ARGS[@]}"

When browser completion is available under the form decision above, an optional, separate browser handoff may open the repository form. It is never proof of publication and is never a response to malformed schemas or missing/ambiguous required answers:

bash
gh issue create --repo "$TARGET" --web

Do not retry a timeout, network failure, missing identity, or other uncertain result. Capture the returned issue number, then read it back from the verified target host before reporting success:

bash
gh issue view "$NUMBER" --repo "$TARGET" --json number,url,title,body,state,labels >"$READBACK_FILE"

Confirm that read-back identifies the target-host issue and that title and body match after only CRLF-to-LF and trailing-final-newline normalization. Report confirmed only after this target-host read-back. Otherwise report no_write when an authoritative rejection proves no issue was created, or unknown and stop all later mutations.

Triage And Later Actions

Before approving or closing an issue, verify it is concrete, non-duplicate, sufficiently evidenced, in scope, and consistent with repository label/status policy. If any point is uncertain, retain the repository review state and request the smallest missing evidence.

For any post-publication label/status mutation, follow delegated workflow actions. Publication is not authorization for a later action. Keep the automated YAML Form path and conditional blank/browser fallback above unchanged.

© Gentleman-Programming, 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 1 other file (references) in skills/issue-creation of Gentleman-Programming/gentle-shell.

  • SKILL.md
  • references/delegated-workflow-actions.md

Open the folder on GitHubat commit 42653f7

Compare with similar skills

Gentle AI Issue Creation 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.

Gentle AI Issue Creation compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Gentle AI Issue Creation this skillGentleman-Programming/gentle-shell1.2k—~2.5kAutomated safety check: PassApache-2.0
Weavebench Cua ReproduceAMAP-ML/LongHorizon-Harness1.7k—~1.6kAutomated safety check: PassMIT
Evidence-Driven Testingmichaelshimeles/skills1.3k1 repos~3.9kAutomated safety check: PassNone
Create GitHub IssueNVIDIA/OpenShell16k—~1.7kAutomated safety check: PassApache-2.0
Triage IssuesClickHouse/clickhouse-java1.6k—~904Automated safety check: PassApache-2.0
Termio Bug Reporttermio-sh/termio537—~1.9kAutomated safety check: PassMIT

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Works with

Categories

Questions about Gentle AI Issue Creation

What does Gentle AI Issue Creation do?

Create and triage GitHub issues from repository evidence. An agent skill from Gentleman-Programming/gentle-shell. Gentle AI Issue Creation is an agent skill from Gentleman-Programming/gentle-shell. Create and triage GitHub issues from repository evidence.

When should I use Gentle AI Issue Creation?

Gentle AI Issue Creation fits situations like: tasks that involve QA and bug reports.

How do I install Gentle AI Issue Creation in Claude Code?

Run `npx skills add Gentleman-Programming/gentle-shell --skill gentle-ai-issue-creation -a claude-code`. Or copy the skill folder (skills/issue-creation in Gentleman-Programming/gentle-shell) into .claude/skills/gentle-ai-issue-creation in your project. Claude Code loads it when a task matches its description.

How do I install Gentle AI Issue Creation in Codex?

Run `npx skills add Gentleman-Programming/gentle-shell --skill gentle-ai-issue-creation -a codex`. Or copy the skill folder (skills/issue-creation in Gentleman-Programming/gentle-shell) into .agents/skills/gentle-ai-issue-creation in your project. Codex loads it when a task matches its description.

Can I use Gentle AI Issue Creation 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 Gentleman-Programming/gentle-shell --skill gentle-ai-issue-creation -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/gentle-ai-issue-creation, .gemini/skills/gentle-ai-issue-creation, .github/skills/gentle-ai-issue-creation and .opencode/skills/gentle-ai-issue-creation in your project.

What does Gentle AI Issue Creation need to run?

Going by SKILL.md and its folder, Gentle AI Issue Creation needs the command-line tools its instructions call (gh and git).

Does Gentle AI Issue Creation access the network?

SKILL.md contains no URLs. Its commands use gh and git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Gentle AI Issue Creation 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 Gentle AI Issue Creation use?

Gentle AI Issue Creation is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Gentle AI Issue Creation use?

About 2.5k tokens (SKILL.md is roughly 9.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.1k tokens, read only when the agent opens those files.

What are the alternatives to Gentle AI Issue Creation?

Skills that share tags, products or a category with Gentle AI Issue Creation: Weavebench Cua Reproduce (AMAP-ML/LongHorizon-Harness, 1.7k stars), Evidence-Driven Testing (michaelshimeles/skills, 1.3k stars), Create GitHub Issue (NVIDIA/OpenShell, 16k stars) and Triage Issues (ClickHouse/clickhouse-java, 1.6k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Gentle AI Issue Creation?

Gentleman-Programming (a GitHub organization) maintains it in Gentleman-Programming/gentle-shell, which has 1,245 GitHub stars. The repository holds 12 skills in this directory. The repository was last updated on October 9, 2026.

Source: Gentleman-Programming/gentle-shell on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.