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hermes-labs-ai/lintlang

Установка

$npx skills add https://github.com/hermes-labs-ai/lintlang/tree/main/.agents/skills/lintlang

Ставит скилл в текущий проект - CLI спросит, для каких агентов. С флагом -g - в домашнюю папку, для всех проектов.

Описание

Use when writing or reviewing AI agent configs, system prompts, or tool definitions (JSON/YAML/Python) and you need to catch ambiguous tool descriptions, missing stop conditions, schema/description mismatches, or embedded prompts before they reach runtime. Deterministic static analysis, no LLM or network calls.

SKILL.md

Исходник на GitHub

LintLang

LintLang statically analyzes the natural-language instructions that control AI agents — system prompts, tool descriptions, and configs — catching ambiguous tools, missing limits, and mixed output formats before they reach an agent at runtime. It is zero-LLM: deterministic pattern and structural checks only, no model calls, no telemetry, no network access.

Use it for

  • Linting tool descriptions before agents start choosing between them (detects pairs like get_user_info / fetch_user_data with no distinguishing term — check H1.6)
  • Checking prompts and configs for missing stop conditions, unbounded retries, and schema/description mismatches
  • Running a zero-LLM CI gate over YAML, JSON, prompt text, and Python source
  • Scanning .py files for embedded prompts and uncalibrated thresholds (detectors P1/P2)
  • Preflighting one present instruction plus explicit typed context before a host sends it to a model

Do not use it for

  • Runtime evaluation of a live agent
  • Dynamic agent testing or behavioral benchmarking
  • Proving an agent is safe in production
  • Retrieving preferences from history, deciding truth, or rewriting/sending prompts on the agent's behalf

Quickstart

python -m pip install lintlang
lintlang scan AGENTS.md

Or without installing, via uv:

uvx lintlang scan AGENTS.md

Scan a fixture with a known finding:

uvx lintlang scan samples/bad_tool_descriptions.yaml

Output shape

  • Repository scan outcomes: ERROR, PASS, REVIEW, or FAIL
  • Structural findings by pattern H1 through H7, plus Python pipeline findings P1 and P2
  • JSON output for CI via --format json
  • Preflight states: ALLOW, NOTICE, HOLD, UNAVAILABLE, or ERROR
  • Preflight evidence uses exact code-point spans and stable PF001-PF005 IDs

Common gotchas

  • LintLang judges structure, not runtime model behavior — a config can pass every LintLang check and still fail at inference time.
  • Configs can be syntactically valid YAML/JSON while still under-specified for their intended use; LintLang flags this as REVIEW, not FAIL.
  • Preflight heuristic findings are notice-only; only exact contract/conflict rules may hold (HOLD).

More

Full docs, CLI reference, and CI integration: https://github.com/hermes-labs-ai/lintlang