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BC gh-stack

Manage Cherry Studio stacked branches and pull requests with the GitHub gh-stack extension. Use for planning, creating, inspecting, rebasing, syncing, linking, or recovering a stack of dependent PRs.

The skillemall take

A skill for managing dependent PR stacks in GitHub via the gh-stack extension. Promises help with planning, creating, inspecting, rebasing, and syncing branches—basically everything to keep your commit chain from tangling.

One file, no scripts. Quality score 84, process score 55. No critical findings or lint errors. Not tested on models or in sandbox. Supports many platforms: Claude, Cursor, Copilot, DeepSeek, and others.

Install it if you already know how gh-stack works and just need an AI-friendly command reference. Not a primer for stacked PR newcomers—just a lookup sheet.

CherryHQ/cherry-studio Agent Skills author: CherryHQ AGPL-3.0 1 file body ≈ 1 285 tokens Open the sourcegithub.com↗ analyzed 36 h ago

Manage Cherry Studio stacked branches and pull requests with the GitHub gh-stack extension.

As a process C 55/100 · Has gaps — weak spots: result and completion, failures and branches, running it twice

GeneratorGitHubSoftware developmentAI and agentstype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
B
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
84
Run on models
none yet
Process rating
C
55/100
Has gaps
Result and completion w 14
0
Failures and branches w 10
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 0

    ✓ No critical or high findings

    Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 55/100

    • 0Result and completion. Does not say what the result is
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 12 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (bash, git) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 14 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1285 tokens
    • low The response is described with custom markup (7 tags): a typed call is more reliable

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 199: enough signal without eating the budget
    • +4Structure: 7 headings
    • +3Step-by-step instructions: 14 items
    • +4Has examples (4 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.