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Blocking Io Guard

OrganizationPopular
bytedance
blocking-io-guard

Ensure async-path backend code that could block the asyncio event loop is protected by a teeth-verified runtime anchor in tests/blocking_io/. Use when changing backend Python under app/, packages/harness/deerflow/, or scripts/, when running a blocking-IO triage round over the whole repo, or when a reviewer/CI asks for blocking-IO coverage. Runs a deterministic scan (changed-lines or full-repo), routes each candidate, drafts/extends an anchor, and proves it fails when the blocking IO regresses.

Overview

Publisherbytedance
Repositorydeer-flow
Skill nameblocking-io-guard
Stars
83.7K
Forks
11.7K
Bundled files
3
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by bytedance on GitHub. Read the source before you install it.

Installation

Install the Blocking Io Guard AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/bytedance/deer-flow/tree/main/.agent/skills/blocking-io-guard
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/bytedance/deer-flow.git /tmp/deer-flow
mkdir -p .claude/skills
cp -r /tmp/deer-flow/.agent/skills/blocking-io-guard .claude/skills/blocking-io-guard
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Blocking Io Guard in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Blocking Io Guard on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Blocking Io Guard is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Blocking-IO Guard Skill

Help a contributor ship backend async changes together with the runtime anchor that lets DeerFlow's blocking-IO CI gate actually see the new code. The dynamic detector only catches blocking IO on paths a test executes — this skill closes that gap, either for your own diff or for a repo-wide triage round.

Read references/good-anchor-rules.md before writing any anchor. Only read references/sop-skeleton.md when generalizing this SOP to another detector domain — it is not needed to execute the steps below.

When to use

  • Your change touches Python under backend/app/, backend/packages/harness/deerflow/, or backend/scripts/ and may run on the async event loop (Mode A). If unsure, run Step 0 — it answers deterministically.
  • You are doing a maintenance triage round over the existing codebase (Mode B).

SOP (router)

Step 0 — Scope (deterministic)

Mode A — your own diff (default, pre-PR). From repo root:

bash
uv run --project backend python scripts/scan_changed_blocking_io.py --base origin/main

Lists blocking-IO candidates your change introduces: findings on lines the diff added, plus findings that are new versus the merge base — the latter catches a new async caller exposing an old sync helper whose blocking line is not in the diff. The diff is <base>...HEAD, so commit your work first — uncommitted lines are not selected.

If the list is empty, this change introduces no blocking-IO surface that the static detector can see in the changed files. One residual blind spot remains: reachability is same-file only, so a new async caller of a sync helper defined in another file is invisible to both selections. If your diff adds an async call into a helper that lives elsewhere, check that helper manually (codegraph or git grep) before stopping.

Mode B — full-repo triage round. From repo root:

bash
make detect-blocking-io

Prints a summary and writes the complete structured finding list to .deer-flow/blocking-io-findings.json. Work HIGH priority first; do not start MEDIUM until every HIGH is dispositioned (fixed, guarded, or recorded NO-ACTION).

Batching policy (PR sizing). One fix unit per PR while any HIGH remains: a fix unit is one root cause — usually a single HIGH, but two HIGHs resolved by the same one-place fix belong together. Once no HIGH remains, MEDIUM/LOW may be batched (about five per round, grouped by module or by disposition) so each PR stays reviewable. A new Blockbuster rule is never batched with anything — it always ships alone (see Step 5).

Both modes emit the same JSON shape per finding: priority, location (path/line/function), blocking_call (category/operation/symbol), event_loop_exposure, reason, code. Priority is a deterministic review ordering, not proof of a bug — Step 1 makes the actual call.

Step 1 — Judge each candidate (router)

Read the code around each candidate and route it:

  • Already offloaded (asyncio.to_thread, run_in_executor, async client) → GUARD: add/extend an anchor that locks the offload so a future edit cannot move it back onto the loop.
  • On the loop, not offloaded → FIX+ANCHOR: offload the production code (your fix), then add an anchor that guards it.
  • Not actually exposed / acceptable (rare: scanner false positive, startup-only code) → NO-ACTION: record one line of why.
  • Cross-file caveat: the scanner's async reachability is same-file only (ASYNC_REACHABLE_SAME_FILE). If the candidate is a sync helper, check for async callers in other files (codegraph or git grep) before deciding NO-ACTION.

Step 2 — Apply the fix, then re-scan (FIX+ANCHOR only)

Offload the blocking call in production code, then re-run the Step 0 scan and confirm the candidate no longer appears. If the offloaded call sits in a finally / cleanup path, keep it best-effort and bounded (swallow-and-log, asyncio.wait_for) so a failing or hung cleanup cannot mask the primary exception. Match by the stable key (path, function, symbol) — line numbers shift after edits, so never compare by line.

  • The finding must disappear. If it still shows, the fix did not remove the blocking pattern (e.g. the call is still a direct call, not offloaded) — go back before touching any test.
  • GUARD / NO-ACTION routes skip this step: a residual finding there is expected (the raw call still exists inside a sync helper with the offload at the caller, or the exposure was judged acceptable).

This is pattern-level feedback in seconds; it complements but never replaces Step 5 — only the runtime gate proves the event loop is actually protected.

Step 3 — Check existing anchors

Look in backend/tests/blocking_io/ for a test that drives the production async entry point reaching this candidate's branch.

  • Covers this branch already → go to Step 5 (re-verify teeth).
  • Covers the entry point but not this branch (e.g. happy path covered, cleanup/404/409 not) → extend that anchor.
  • None → create one from templates/anchor.template.py.

Step 4 — Generate / extend the anchor

Follow references/good-anchor-rules.md. Drive the specific branch (e.g. force the create failure that hits the cleanup shutil.rmtree). Never bypass the blocking surface with a test-only asyncio.to_thread wrapper.

Step 5 — Verify teeth (mandatory; also the anchor-vs-rule discriminator)

  1. Reintroduce the block (GUARD: temporarily revert the offload; FIX+ANCHOR: run against the pre-fix code).
  2. Run cd backend && make test-blocking-io (or target the one test). It must go RED.
  3. Restore the fix. It must go GREEN.

A real block that stays GREEN means Blockbuster has no rule for that primitive — that is the RULE route; see references/good-anchor-rules.md for the admission criteria before adding one.

Step 6 — Deliver

Commit the anchor(s) with your change; make test-blocking-io green. In the PR, note: candidates found, each disposition, the re-scan result (Step 2), and the teeth evidence (red→green). Include the reason for any NO-ACTION. A new Blockbuster rule, if any, goes in its own commit with the evidence from Step 5.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Blocking Io Guard AI skill do?

Ensure async-path backend code that could block the asyncio event loop is protected by a teeth-verified runtime anchor in tests/blocking_io/. Use when changing backend Python under app/, packages/harness/deerflow/, or scripts/, when running a blocking-IO triage round over the whole repo, or when a reviewer/CI asks for blocking-IO coverage. Runs a deterministic scan (changed-lines or full-repo), routes each candidate, drafts/extends an anchor, and proves it fails when the blocking IO regresses.

Why use Blocking Io Guard on TypingMind?

Because you install it once and use it with any model. Blocking Io Guard is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Blocking Io Guard in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/bytedance/deer-flow/tree/main/.agent/skills/blocking-io-guard. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Blocking Io Guard?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Blocking Io Guard?

As many as you like. As long as a model supports skills, you can use Blocking Io Guard with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Blocking Io Guard AI skill free?

Yes. It is published on GitHub by bytedance under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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