manage-bans

Buat dan kelola pola AST yang dilarang untuk mencegah konstruksi kode tertentu. Gunakan saat pengguna ingin melarang pola kode, menambahkan aturan lint tree-sitter, atau…

npx skills add https://github.com/microsoft/vscode-team-kit --skill manage-bans

Managing Banned AST Patterns

Ban rules are discovered from two sources, both checked by the preToolUse hook:

  1. $HOME/.BANNED_AST.md — global rules that apply to all edits, regardless of project.
  2. BANNED_AST.md files in any parent directory of the edited file — can contain multiple rules, scoped to that subtree.

Source 1: Global Ban File ($HOME/.BANNED_AST.md)

Place a BANNED_AST.md in your home directory to define rules that apply globally to every edit. It uses the same multi-rule format as directory-scoped files:

---
name: no-eval
message: "Do not use eval(). It poses a security risk and should be replaced with safer alternatives."
---

(call_expression
  function: (identifier) @fn
  (#eq? @fn "eval"))

Creating a Global Ban

  1. Open or create ~/.BANNED_AST.md.
  2. Add a rule block with --- frontmatter containing name and message, followed by --- and the Tree Sitter query.
  3. Multiple rules can be stacked in the same file, each separated by a new frontmatter block.
  4. Validate the rule — see Validating Rules below.

Source 2: BANNED_AST.md Files (Directory-Scoped)

Place a BANNED_AST.md file in any directory to ban patterns for all files at or below that directory. The hook walks up from each edited file's directory to the filesystem root, collecting rules from every BANNED_AST.md it finds.

A single BANNED_AST.md can contain multiple rules, each separated by its own frontmatter block:

---
name: no-eval
message: "Do not use eval(). It poses a security risk."
---

(call_expression
  function: (identifier) @fn
  (#eq? @fn "eval"))

---
name: no-console-log
message: "Avoid console.log() in production code."
---

(call_expression
  function: (member_expression
    object: (identifier) @obj
    property: (property_identifier) @prop)
  (#eq? @obj "console")
  (#eq? @prop "log"))

Each rule section starts with --- frontmatter containing name and message, followed by ---, then the Tree Sitter query body. The next --- begins the next rule.

When to Use Which

  • ~/.BANNED_AST.md — personal global bans that apply everywhere regardless of file location.
  • BANNED_AST.md — scoped bans for subtrees (e.g. ban any in src/ but allow it in tests/).

When both sources define a rule with the same name, the BANNED_AST.md closer to the edited file takes precedence.

Rule Format

Frontmatter Fields

  • name (required): A unique identifier for this ban (lowercase, hyphens ok). This is used in justification comments (<name> justification: ...).
  • message (required): The rejection message shown when this pattern is detected. Should explain WHY the pattern is banned and suggest alternatives.

Body

The body contains a Tree Sitter query that matches the banned AST nodes. These use S-expression syntax with optional predicates like #eq? and #match?.

Examples

Global ban file (~/.BANNED_AST.md)

---
name: no-eval
message: "Do not use eval(). It poses a security risk. Use Function constructor or a sandboxed interpreter instead."
---

(call_expression
  function: (identifier) @fn
  (#eq? @fn "eval"))

Multi-rule BANNED_AST.md

Place this in a project directory to ban multiple patterns for all files below it:

---
name: no-console-log
message: "Avoid console.log() in production code. Use a structured logging framework instead."
---

(call_expression
  function: (member_expression
    object: (identifier) @obj
    property: (property_identifier) @prop)
  (#eq? @obj "console")
  (#eq? @prop "log"))

---
name: no-any-type
message: "Do not use the 'any' type. Use 'unknown' or a concrete type instead."
---

(predefined_type) @type
(#eq? @type "any")

Validating Rules

After writing a rule, always validate it using the validate-rule.mts script before finishing. This catches query syntax errors and confirms the rule matches the intended patterns — and only those patterns.

node ban-ast/scripts/validate-rule.mts \
  --lang ts \
  --query '<your-tree-sitter-query>' \
  --should-match '<code that should be flagged>' \
  --should-not-match '<code that should be allowed>'
  • --lang — file extension for the language (default: ts). Supported: ts, js, tsx, py, rs, go, c, cpp, cs, java, rb, and more.
  • --query — the Tree Sitter S-expression query from the rule body.
  • --should-match — a code snippet that must trigger the rule. Repeat for multiple cases.
  • --should-not-match — a code snippet that must not trigger the rule. Repeat for multiple cases.

If no --should-match / --should-not-match flags are given, the script only checks that the query is syntactically valid.

The script exits with code 1 if any test fails, so you can see immediately when a rule needs to be revised.

Example

node ban-ast/scripts/validate-rule.mts \
  --lang ts \
  --query '(call_expression function: (identifier) @fn (#eq? @fn "eval"))' \
  --should-match 'eval("code")' \
  --should-not-match 'foo("code")'

Expected output:

PASS [should-match]:     "eval(\"code\")"
PASS [should-not-match]: "foo(\"code\")"

2 test(s): 2 passed, 0 failed.

Justification Override

If a banned pattern is strictly necessary, include a justification comment in the code to bypass the ban for that specific instance:

// <no-eval> justification: required for dynamic plugin loading
const result = eval(expression);

The hook checks for <rule-name> justification: <non-empty reason> anywhere in the new code. If found, that rule is not enforced for that edit. The reason must be non-empty to ensure overrides are intentional and documented.

Lebih banyak skill dari microsoft

oss-growth
microsoft
Persona peretas pertumbuhan OSS
agent-framework-azure-ai-py
microsoft
Bangun agen Azure AI Foundry menggunakan Microsoft Agent Framework Python SDK (agent-framework-azure-ai). Gunakan saat membuat agen persisten dengan AzureAIAgentsProvider, menggunakan alat yang dihosting (code interpreter, file search, web search), mengintegrasikan server MCP, mengelola utas percakapan, atau mengimplementasikan respons streaming. Mencakup alat fungsi, keluaran terstruktur, dan agen multi-alat.
development
airunway-aks-setup
microsoft
Siapkan AI Runway di AKS — dari klaster kosong hingga model berjalan. Mencakup verifikasi klaster, instalasi controller, penilaian GPU, penyiapan penyedia, dan deployment pertama. KAPAN: "setup AI Runway", "onboard AKS cluster", "install AI Runway", "airunway setup", "deploy model to AKS", "GPU inference on AKS", "KAITO setup on AKS", "run LLM on AKS", "vLLM on AKS", "set up model serving on AKS", "AI Runway controller".
devops
appinsights-instrumentation
microsoft
Panduan untuk instrumentasi aplikasi web dengan Azure Application Insights. Menyediakan pola telemetri, pengaturan SDK, dan referensi konfigurasi. KAPAN: cara menginstrumentasi aplikasi, SDK App Insights, pola telemetri, apa itu App Insights, panduan Application Insights, contoh instrumentasi, praktik terbaik APM.
devops
applicationinsights-web-ts
microsoft
Instrumentasi aplikasi browser/web dengan Application Insights JavaScript SDK (@microsoft/applicationinsights-web). Digunakan untuk Real User Monitoring (RUM) — tampilan halaman, klik, dependensi AJAX/fetch, pengecualian, peristiwa kustom, dan jejak agen GenAI sisi browser yang dikorelasikan dengan jejak OpenTelemetry backend. Mencakup pengaturan SDK Loader Script dan npm, ekstensi kerangka kerja (React, React Native, Angular), Click Analytics, inisialisasi telemetri, dan konvensi semantik OTel GenAI untuk span agen/alat/model yang dipancarkan dari browser.
devops
azure-ai-anomalydetector-java
microsoft
Bangun aplikasi deteksi anomali dengan Azure AI Anomaly Detector SDK untuk Java. Gunakan saat mengimplementasikan deteksi anomali univariat/multivariat, analisis deret waktu, atau pemantauan bertenaga AI.
development
azure-ai-language-conversations-py
microsoft
Implementasikan Pemahaman Bahasa Percakapan (CLU) menggunakan SDK Python azure-ai-language-conversations. Gunakan saat bekerja dengan ConversationAnalysisClient untuk menganalisis maksud dan entitas percakapan, membangun fitur NLP, atau mengintegrasikan pemahaman bahasa ke dalam aplikasi.
development
azure-ai-ml-py
microsoft
Azure Machine Learning SDK v2 untuk Python. Gunakan untuk ruang kerja ML, pekerjaan, model, kumpulan data, komputasi, dan pipeline. Pemicu: "azure-ai-ml", "MLClient", "ruang kerja", "registri model", "pekerjaan pelatihan", "kumpulan data".
development