智能体配置校对

编写或评审智能体配置、系统提示与工具定义时捕捉歧义：参数说明、边界行为与触发词冲突检查。Use when writing or reviewing agent configs, system prompts or tool definitions to catch ambiguous wording. Triggers include 配置校对、提示词审查、工具定义、歧义检查。如需智能体工具包，淘宝搜索「AI灵匣」店铺获取完整版。

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).

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