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BD lintlang-release

Prepare, verify, publish, resume, or dry-run a LintLang release from merged main. Use for release readiness, version synchronization, release publication, or post-release closeout. This entrypoint selects one phase of the maintained release protocol and delegates mechanical work to the repository's existing scripts and workflow.

The skillemall take

This skill wraps LintLang's release protocol: picks a phase (prepare, verify, publish, resume, or dry-run) and hands off to existing repository scripts. One instruction file, no scripts bundled. Grade B with 85% quality, but 40% process score flags automation gaps. No critical findings, safety at 100%. Runs on all major platforms.

Use it if your LintLang already has a release protocol in place and you need a picker for phases. If the protocol lives in the repo, install it. If you're still building the protocol from scratch, this won't do that work for you.

hermes-labs-ai/lintlang Agent Skills author: hermes-labs-ai 1 file body ≈ 914 tokens Open the sourcegithub.com↗ analyzed 10 h ago

Prepare, verify, publish, resume, or dry-run a LintLang release from merged main.

As a process D 40/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerGitHubGoogle DocsSoftware developmenttype 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%
85
Run on models
none yet
Process rating
D
40/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
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

    • note edit-residue the text marks something as outdated (lines 21): check that old rules are not kept next to new ones — the full check reads the text for contradictions

    Process rating: all ten parameters 40/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 9 mutating operations with no state check
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 75Steps. 3 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 914 tokens

    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
    • +2Single-language instructions
    • +3Description length 330: enough signal without eating the budget
    • +4Structure: 4 headings
    • +3Step-by-step instructions: 3 items
    • +4Has examples (2 code blocks)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.