Http Toolkit Intercept logo

Http Toolkit Intercept

Organization
Factory-AI
http-toolkit-intercept

Intercept and debug HTTP traffic from any CLI, service, or script using HTTP Toolkit. Use when you need to inspect LLM API calls, backend requests, auth flows, or debug network-level issues across any language or runtime.

Overview

PublisherFactory-AI
Repositoryfactory-plugins
Skill namehttp-toolkit-intercept
Stars
111
Forks
15
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Factory-AI on GitHub. Read the source before you install it.

Installation

Install the Http Toolkit Intercept 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.
  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/Factory-AI/factory-plugins.git /tmp/factory-plugins
mkdir -p .claude/skills
cp -r /tmp/factory-plugins/plugins/debugging/skills/http-toolkit-intercept .claude/skills/http-toolkit-intercept
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Http Toolkit Intercept 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 Http Toolkit Intercept 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 Http Toolkit Intercept 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.

HTTP Toolkit Intercept

Use this skill when you need authoritative evidence of what your program sent to a remote API and what it received back while verifying a code change. Works across any runtime — Node.js, Bun, Deno, Python, Go, Ruby, Java/JVM, .NET, PHP, Rust, shell scripts, etc. — as long as the process respects a proxy.

The reliable pattern is:

  1. Start HTTP Toolkit correctly.
  2. Run the program through the proxy in a mode that produces a machine-readable log (e.g. --output-format json, debug logging, or structured stdout).
  3. Export outbound HTTP requests from HTTP Toolkit.
  4. Pair the outbound HTTP export with the inbound program session log.

Do not rely on TUI screenshots alone when the question is about request payloads, auth headers, or wire-level behavior.

Prerequisites / Known-Good Launch

Start HTTP Toolkit

On Linux/headless environments, plain httptoolkit often fails due to sandbox/X11 issues. Prefer:

bash
xvfb-run --auto-servernum httptoolkit --no-sandbox

If a stale server is already running on ports 45456/45457, stop it first:

bash
pkill -f "HTTP Toolkit Server|httptoolkit|xvfb-run --auto-servernum httptoolkit" || true

Verify the proxy is reachable

bash
# HTTP Toolkit's admin API lives on port 45456/45457 by default.
# Treat 200/401/403 as "reachable"; only connection failure means the server is dead.
curl -s -o /dev/null -w "%{http_code}\n" http://127.0.0.1:45456/config

Quick Start

1. Launch your program with the proxy env vars set

The canonical env vars most runtimes honor:

bash
HTTP_PROXY="http://127.0.0.1:8000" \
HTTPS_PROXY="http://127.0.0.1:8000" \
ALL_PROXY="http://127.0.0.1:8000" \
NO_PROXY="" \
<your-program> <args>

Some runtimes require an extra env var or flag — see the "Runtime proxy matrix" below.

If the HTTP Toolkit CA is not trusted by your runtime, TLS verification will fail. See "TLS Safety" below. Disabling TLS verification is appropriate only for controlled local debugging.

2. Capture the inbound session log

If your program supports a machine-readable output mode (e.g. --output-format stream-json, --json, --log-level debug, structured stdout, or writing to a file), pipe it to a file:

bash
<your-program> exec --output-format stream-json "your input" \
  > /tmp/session-stdout.log 2> /tmp/session-stderr.log

3. Export outbound HTTP from HTTP Toolkit

Either:

  • Use the HTTP Toolkit GUI export (File → Export → JSON / HAR), or
  • Hit the admin API directly. The exact endpoint depends on your HTTP Toolkit version; inspect DevTools in the HTTP Toolkit UI to see the requests it makes.

4. Cross-reference the two streams

  • Outbound HTTP (from HTTP Toolkit): authoritative for request bodies, headers, auth tokens, retry timing.
  • Inbound session log (from the program): authoritative for how your code reacted to the responses.

Together they answer: "what did we send?" and "what did we do with the response?"

What finally worked for payload verification

The critical correct pathways that proved reliable were:

  1. Use a non-interactive / exec mode, not the TUI, when verifying payloads

    • Interactive TUIs are slower and much harder to analyze.
    • Use whatever your program has for scripting (--output-format, --json, --headless, --batch).
  2. Treat outbound and inbound as separate evidence sources

    • HTTP Toolkit gives outbound HTTP requests.
    • The program's session log gives inbound assistant/tool/application behavior.
    • You need both to answer: "what did the remote return?" and "what did the program actually do with it?"
  3. Set the proxy env vars your specific runtime honors

    • HTTP_PROXY/HTTPS_PROXY cover most runtimes, but some (e.g. Bun, Java) require extra vars or flags.
    • See the "Runtime proxy matrix" below.
  4. Disable TLS verification only for controlled local debugging when needed

    • If the HTTP Toolkit CA is not trusted locally, use the runtime-specific escape hatch to skip verification.
    • Prefer trusting the CA in your OS / language trust store instead.
    • Never disable TLS in production repros.
  5. Keep runs bounded

    • Long network-heavy sessions can take time.
    • If you only need to prove request shape, export after the relevant request is observed — you do not always need to wait for full completion.

Key Facts

  • Proxy env vars are runtime-specific — know which ones your runtime honors
  • HTTP Toolkit admin API is request-oriented — outbound HTTP comes from HTTP Toolkit, inbound behavior comes from the program log
  • TLS is verified by default — runtime-specific skip flags are local-dev escape hatches only

Runtime proxy matrix

RuntimeProxy env vars / flagsTLS bypass (local dev only)
Node.jsHTTP_PROXY, HTTPS_PROXY, NO_PROXY (most libraries); some HTTP clients need --proxy flags or explicit agent: optionNODE_TLS_REJECT_UNAUTHORIZED=0 or NODE_EXTRA_CA_CERTS=/path/to/ca.pem
BunBUN_CONFIG_PROXY (mandatory — plain HTTP_PROXY/HTTPS_PROXY are silently ignored for Bun's own fetch)NODE_TLS_REJECT_UNAUTHORIZED=0
DenoHTTP_PROXY, HTTPS_PROXY--unsafely-ignore-certificate-errors
Python (requests, httpx)HTTP_PROXY, HTTPS_PROXY, ALL_PROXYREQUESTS_CA_BUNDLE / SSL_CERT_FILE pointing at the HTTP Toolkit CA, or verify=False in code
Python (urllib)Same env varsSSL_CERT_FILE or disable context verification
Go (net/http)HTTP_PROXY, HTTPS_PROXY, NO_PROXY (honored via http.ProxyFromEnvironment)SSL_CERT_FILE or InsecureSkipVerify: true in the transport
RubyHTTP_PROXY, HTTPS_PROXYSSL_CERT_FILE, or OpenSSL::SSL::VERIFY_NONE
Java / JVM-Dhttp.proxyHost=127.0.0.1 -Dhttp.proxyPort=8000 -Dhttps.proxyHost=127.0.0.1 -Dhttps.proxyPort=8000; env vars are not honored by the JVMImport CA into a truststore and pass -Djavax.net.ssl.trustStore=...
.NET / C#HTTP_PROXY, HTTPS_PROXY, ALL_PROXY (on recent runtimes) or configure HttpClient explicitlyTrust CA in OS store, or HttpClientHandler.ServerCertificateCustomValidationCallback
PHP (curl / cli)HTTP_PROXY, HTTPS_PROXY (or per-call CURLOPT_PROXY)CURLOPT_SSL_VERIFYPEER => false
Rust (reqwest)HTTP_PROXY, HTTPS_PROXYdanger_accept_invalid_certs(true) on the client
curl / shellHTTP_PROXY, HTTPS_PROXY env vars or -x/--proxy flag-k / --insecure
Docker containersPass env vars through with -e HTTP_PROXY=...; use host.docker.internal (Mac/Win) or --network=host (Linux) so the container can reach the proxyMount the CA into the image and install it, or use runtime-specific bypass flags

Prefer trusting the HTTP Toolkit CA in your runtime/OS trust store over disabling verification. Bypass flags should be local-dev only.

TLS Safety Guardrails

  • Keep TLS verification enabled whenever possible.
  • Prefer trusting the HTTP Toolkit CA in your local trust store instead of disabling verification.
  • Use runtime-specific TLS-bypass flags only for controlled local debugging in development.
  • Never disable TLS when intercepting production traffic.

Inspecting Captured Logs

Filter to relevant events only

If your program emits newline-delimited JSON, use jq:

bash
jq -c 'select(.type == "tool_call" or .type == "message")' /tmp/session-stdout.log

Adjust the filter to match your program's event schema. For plain-text logs, use rg/grep patterns.

Match outbound HTTP to inbound events

Sort both streams by timestamp, then interleave them. The sequence usually looks like:

outbound POST /api/endpoint        (from HTTP Toolkit)
inbound  event received             (from program log)
inbound  follow-up action           (from program log)
outbound POST /api/endpoint        (next request)

If a program log shows an outbound request that HTTP Toolkit didn't capture, that's a proxy-config bug.

Troubleshooting

ProblemCauseFix
Program hangs, no events after startupProxy env vars not reaching the process, or TLS verification blockingRe-run with the right env vars for your runtime (see matrix); if necessary, enable the runtime's TLS bypass in dev
ECONNRESET / connection reset on every requestRuntime-specific proxy env var missing (e.g. BUN_CONFIG_PROXY for Bun, JVM -D flags for Java)Use the correct runtime-specific proxy config
TLS cert errors via proxyMITM CA not trusted by this runtimeTrust HTTP Toolkit CA in the runtime / OS store, or enable the runtime's TLS bypass in dev only
HTTP Toolkit API 403s on /configAuth-gated config endpointTreat 200/401/403 as reachable; only connection failure means the server is dead
Export has outbound data but no matching inbound eventsDidn't capture the program logAdd > /tmp/session.log redirection to the launch
HTTP Toolkit misses the first requestStarted capturing after the process launchedStart HTTP Toolkit first, THEN launch the program
Container / VM can't reach 127.0.0.1:8000Loopback is container-localUse host.docker.internal (Docker Desktop) or --network=host (Linux)
Program ignores env vars entirelyRuntime doesn't honor env vars (e.g. JVM)Use runtime-specific flags (-Dhttp.proxyHost=... for JVM, etc.)

Frequently asked questions

What does the Http Toolkit Intercept AI skill do?

Intercept and debug HTTP traffic from any CLI, service, or script using HTTP Toolkit. Use when you need to inspect LLM API calls, backend requests, auth flows, or debug network-level issues across any language or runtime.

Why use Http Toolkit Intercept on TypingMind?

Because you install it once and use it with any model. Http Toolkit Intercept 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 Http Toolkit Intercept in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Factory-AI/factory-plugins/tree/master/plugins/debugging/skills/http-toolkit-intercept. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Http Toolkit Intercept?

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 Http Toolkit Intercept?

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

Is the Http Toolkit Intercept AI skill free?

It is published on GitHub by Factory-AI. Check the repository for licensing terms. 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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