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BC lintlang-audit

Audit a named AI agent config, system prompt, tool definition, or instruction file (YAML, JSON, Markdown, text, or Python) with the released LintLang CLI in GitHub Copilot CLI. Use when the user asks to audit, lint, scan, or review such a file for ambiguous tool descriptions, missing stop conditions, schema mismatches, or embedded prompts. Deterministic static analysis with no model or network call during a scan.

The skillemall take

The skill audits AI agent configs in Copilot CLI—checks schemas, tool descriptions, stop conditions. One file, 1687 tokens, no network calls or model runs. Grade B: quality 85, process 63. No critical errors found, but only static analysis, no live testing against actual configs.

For those who audit prompts and tool definitions regularly, it saves time on manual YAML and JSON review. Works across all major platforms. The catch: without model runs, it's unclear if it catches real logic problems or just syntax. Install if you need a first-pass filter before serious testing.

hermes-labs-ai/lintlang Agent Skills author: hermes-labs-ai 1 file body ≈ 1 687 tokens Open the sourcegithub.com↗ analyzed 10 h ago

Audit a named AI agent config, system prompt, tool definition, or instruction file (YAML, JSON, Markdown, text, or Python) with the released LintLang CLI in…

As a process C 63/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

AnalyzerGitHubAI and agentsSoftware 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
C
63/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
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 63/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 3 mutating operations with no state check
    • 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
    • 100Steps. 20 steps
    • 100When it triggers. States when to use and when not to
    • 100Failures and branches. 2 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1687 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 416: enough signal without eating the budget
    • +4Structure: 6 headings
    • +3Step-by-step instructions: 20 items
    • +4Has examples (3 code blocks)
    • +1License stated

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