automate-perplexity

A command that opens Perplexity, an AI research service, in a Chrome browser and runs a query using the current project's context.

In plain words
What is it for?
Use it to investigate a technical topic, analyze a project, or research current information with context from the project and connected Dropbox or GitHub sources.
Why use it?
It avoids copying project details into a separate research session or setting up an API key. The command gathers context and sends the research question through your logged-in browser session.

Command

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add commands/intellegix/intellegix-code-agent-toolkit/automate-perplexity
Clone the repo
git clone --depth 1 https://github.com/intellegix/intellegix-code-agent-toolkit
Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 2,540 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00000 $0.02540
Opus 5 $0.00000 $0.01270
Sonnet 5 $0.00000 $0.00508
Haiku 4.5 $0.00000 $0.00254

Measured 2d ago against content hash fb7b6ca82b20, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

automate-perplexity scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

commands/automate-perplexity.md · 194 lines

How it starts

The opening of the file, as written. The whole thing — 194 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/automate-perplexity — Unified Perplexity Automation

Run a Perplexity query by typing /control in the browser — a custom Comet command that activates browser control mode with Dropbox/GitHub context. Single entry point for all Perplexity research.

No API keys required — uses Perplexity login session via Chrome browser automation. $0/query.

CRITICAL: Do NOT ask the user questions before completing Step 0 and Step 1. Compile context silently, build the query, and execute. Only ask questions if $ARGUMENTS is empty AND you cannot determine a useful research focus from the compiled context.

Input

$ARGUMENTS = The research question or topic to investigate. If empty, defaults to a general project analysis.

Workflow

Step 0: Compile Session Context — MANDATORY, SILENT

Before doing ANYTHING else, compile the current session state. Do NOT ask the user any questions during this step — proceed silently and autonomously.

  1. Read project memory: Read the project's MEMORY.md from the auto-memory directory to understand what's been worked on, recent patterns, and known issues
  2. Recent commits: Run git log --oneline -10 to see recent work
  3. Uncommitted work: Run git diff --stat to see what's in progress
  4. Active tasks: Check TaskList for any active/pending tasks
  5. Synthesize: Form a 1-paragraph internal "current state" summary — do NOT output this to the user, just hold it in context for Step 1

Do NOT present findings. Do NOT ask questions. Proceed directly to Step 0.5.

Step 0.5: Explore Codebase — MANDATORY, SILENT

After compiling session context (Step 0), explore the actual codebase:

  1. Find key files: Use Glob for main source files (*.py, *.ts, *.js) in project root and src/
  2. Read recently modified: Run git diff --name-only HEAD~5 HEAD, read up to 10 files (first 100 lines each)
  3. Read structural files: README.md, pyproject.toml, package.json if they exist
  4. Synthesize: Form internal "codebase summary" — key files, purposes, connections

Read the full file on GitHub · 194 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 194 lines · 0 tokens per session scan A fb7b6ca82b20

Subscribe to this mod's changes

automate-perplexity is a command published in the GitHub repository intellegix/intellegix-code-agent-toolkit (57 stars, last pushed 8d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,540 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.