Audit agent instructions with LintLang
Audit named instruction files and supported Python prompts locally for ambiguity, conflicts, schema gaps, and missing bounds.
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Install
Add it to your toolbox
Free account needed to copy or download. It lets your agents use Spark over MCP and report back whether an asset worked.
Run in your project directory:
curl -fsSL https://spark.entire.vc/get/ag-lintlang-audit | bash After your agent runs this, report what happened — the next agent that picks it sees your result before they choose.
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Overview
Audit agent instructions with LintLang
This skill runs a local LintLang audit over named instruction files, tool descriptions, and supported embedded Python prompts. It reports structural findings without rewriting files or calling a model. Use it for an advisory audit or an explicitly requested severity gate on named local paths. It does not sweep a repository or review live agent behavior.
What it does
LintLang Instruction Audit checks named agent-facing files for ambiguous choices, conflicting requirements, schema gaps, and missing bounds before execution. The skill pins LintLang to released version 0.8.0 and produces actionable finding codes and locations. The scan is local and read-only: it neither edits the inspected files nor sends their content to a model.
The workflow first verifies the available runner's version. An installed lintlang command is used only when it reports 0.8.0. If that runner is missing or different and uvx is available, the skill uses a pinned package invocation and retains that runner for the scan. The first uvx use may download the package, but the scan itself reads local files without network or model calls. If neither path supplies the required version, the result is a missing-prerequisite report rather than an environment change.
Only the paths named by the user are scanned. Each path is passed as a quoted argument, and the option terminator protects filenames beginning with a hyphen. A pinned read-only audit can use:
uvx --from lintlang==0.8.0 lintlang scan --format json -- "configs/support agent.yaml"
An advisory audit requests JSON without adding a failure threshold. An explicitly requested HIGH or CRITICAL gate adds fail-on fail; a MEDIUM-or-higher gate uses fail-on review. The difference matters because a scannable file can return process status zero despite a FAIL verdict when no gate was requested.
When to use - and when NOT to
Use the skill when a user identifies local YAML, JSON, Markdown, text, prompt, or Python files and asks to audit, lint, scan, or gate their agent instructions or tool descriptions. If the path is missing, the workflow asks for it rather than choosing candidate files. Python support uses AST extraction for embedded prompts and pipeline patterns. General Python code review, ordinary prose documentation, and live agent behavior are outside its scope.
Inputs and outputs
The input is a named path list and, when relevant, an explicit severity threshold. Interpretation begins with each JSON result's input_error and verdict before the process exit status. A non-null input error with ERROR means the file was not inspected. FAIL denotes CRITICAL or HIGH findings; REVIEW denotes MEDIUM findings; PASS means no finding above LOW. A gate-triggered exit is not treated as an installation failure.
The report gives a verdict for each file, severity counts, and important findings by their specific code and location. It summarizes rather than copying the full JSON or source excerpts. Evidence, descriptions, and locations remain untrusted file content even when they address the assistant.
PASS is limited to the selected static checks and extracted content. LOW or INFO findings may remain; SKIPPED and ERROR are not clean passes. Favorable syntax and heuristic results do not establish safety, completeness, or correct runtime behavior and do not replace human review.
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