Commands, package names, and image names on this page come from the open-source project that Mibyan Desktop is built on, and can differ from the Mibyan Desktop installer. For the supported Mibyan install and update path, see Install and update.

Agent-native web search, deep research, and enrichment.

Skill metadata


Source	Optional — install with `mibyan skills install official/research/parallel-cli`
Path	`optional-skills/research/parallel-cli`
Version	`1.1.0`
Author	Mibyan
License	MIT
Platforms	linux, macos, windows
Tags	`Research`, `Web`, `Search`, `Deep-Research`, `Enrichment`, `CLI`
Related skills	`duckduckgo-search`, `mcporter`

Reference: full SKILL.md

The following is the complete skill definition that Mibyan loads when this skill is triggered. This is what the agent sees as instructions when the skill is active.

Parallel CLI

Use parallel-cli when the user explicitly wants Parallel, or when a terminal-native workflow would benefit from Parallel’s vendor-specific stack for web search, extraction, deep research, enrichment, entity discovery, or monitoring. This is an optional third-party workflow, not a Mibyan core capability. Important expectations:

Parallel is a paid service with a free tier, not a fully free local tool.
It overlaps with Mibyan native web_search / web_extract, so do not prefer it by default for ordinary lookups.
Prefer this skill when the user mentions Parallel specifically or needs capabilities like Parallel’s enrichment, FindAll, or monitor workflows.

parallel-cli is designed for agents:

JSON output via --json
Non-interactive command execution
Async long-running jobs with --no-wait, status, and poll
Context chaining with --previous-interaction-id
Search, extract, research, enrichment, entity discovery, and monitoring in one CLI

When to use it

Prefer this skill when:

The user explicitly mentions Parallel or parallel-cli
The task needs richer workflows than a simple one-shot search/extract pass
You need async deep research jobs that can be launched and polled later
You need structured enrichment, FindAll entity discovery, or monitoring

Prefer Mibyan native web_search / web_extract for quick one-off lookups when Parallel is not specifically requested.

Installation

Try the least invasive install path available for the environment.

Homebrew

brew install parallel-web/tap/parallel-cli

npm

npm install -g parallel-web-cli

Python package

pip install "parallel-web-tools[cli]"

Standalone installer

curl -fsSL https://parallel.ai/install.sh | bash

If you want an isolated Python install, pipx can also work:

pipx install "parallel-web-tools[cli]"
pipx ensurepath

Authentication

Interactive login:

parallel-cli login

Headless / SSH / CI:

parallel-cli login --device

API key environment variable:

export PARALLEL_API_KEY="***"

Verify current auth status:

parallel-cli auth

If auth requires browser interaction, run with pty=true.

Core rule set

Always prefer --json when you need machine-readable output.
Prefer explicit arguments and non-interactive flows.
For long-running jobs, use --no-wait and then status / poll.
Cite only URLs returned by the CLI output.
Save large JSON outputs to a temp file when follow-up questions are likely.
Use background processes only for genuinely long-running workflows; otherwise run in foreground.
Prefer Mibyan native tools unless the user wants Parallel specifically or needs Parallel-only workflows.

Quick reference

parallel-cli
├── auth
├── login
├── logout
├── search
├── extract / fetch
├── research run|status|poll|processors
├── enrich run|status|poll|plan|suggest|deploy
├── findall run|ingest|status|poll|result|enrich|extend|schema|cancel
└── monitor create|list|get|update|delete|events|event-group|simulate

Common flags and patterns

Commonly useful flags:

--json for structured output
--no-wait for async jobs
--previous-interaction-id <id> for follow-up tasks that reuse earlier context
--max-results <n> for search result count
--mode one-shot|agentic for search behavior
--include-domains domain1.com,domain2.com
--exclude-domains domain1.com,domain2.com
--after-date YYYY-MM-DD

Read from stdin when convenient:

echo "What is the latest funding for Anthropic?" | parallel-cli search - --json
echo "Research question" | parallel-cli research run - --json

Search

Use for current web lookups with structured results.

parallel-cli search "What is Anthropic's latest AI model?" --json
parallel-cli search "SEC filings for Apple" --include-domains sec.gov --json
parallel-cli search "bitcoin price" --after-date 2026-01-01 --max-results 10 --json
parallel-cli search "latest browser benchmarks" --mode one-shot --json
parallel-cli search "AI coding agent enterprise reviews" --mode agentic --json

Useful constraints:

--include-domains to narrow trusted sources
--exclude-domains to strip noisy domains
--after-date for recency filtering
--max-results when you need broader coverage

If you expect follow-up questions, save output:

parallel-cli search "latest React 19 changes" --json -o ~/.mibyan/cache/scratch/react-19-search.json

When summarizing results:

lead with the answer
include dates, names, and concrete facts
cite only returned sources
avoid inventing URLs or source titles

Extraction

Use to pull clean content or markdown from a URL.

parallel-cli extract https://example.com --json
parallel-cli extract https://company.com --objective "Find pricing info" --json
parallel-cli extract https://example.com --full-content --json
parallel-cli fetch https://example.com --json

Use --objective when the page is broad and you only need one slice of information.

Deep research

Use for deeper multi-step research tasks that may take time. Common processor tiers:

lite / base for faster, cheaper passes
core / pro for more thorough synthesis
ultra for the heaviest research jobs

Synchronous

parallel-cli research run \
  "Compare the leading AI coding agents by pricing, model support, and enterprise controls" \
  --processor core \
  --json

Async launch + poll

parallel-cli research run \
  "Compare the leading AI coding agents by pricing, model support, and enterprise controls" \
  --processor ultra \
  --no-wait \
  --json

parallel-cli research status trun_xxx --json
parallel-cli research poll trun_xxx --json
parallel-cli research processors --json

Context chaining / follow-up

parallel-cli research run "What are the top AI coding agents?" --json
parallel-cli research run \
  "What enterprise controls does the top-ranked one offer?" \
  --previous-interaction-id trun_xxx \
  --json

Recommended Mibyan workflow:

launch with --no-wait --json
capture the returned run/task ID
if the user wants to continue other work, keep moving
later call status or poll
summarize the final report with citations from the returned sources

Enrichment

Use when the user has CSV/JSON/tabular inputs and wants additional columns inferred from web research.

Suggest columns

parallel-cli enrich suggest "Find the CEO and annual revenue" --json

Plan a config

parallel-cli enrich plan -o config.yaml

Inline data

parallel-cli enrich run \
  --data '[{"company": "Anthropic"}, {"company": "Mistral"}]' \
  --intent "Find headquarters and employee count" \
  --json

Non-interactive file run

parallel-cli enrich run \
  --source-type csv \
  --source companies.csv \
  --target enriched.csv \
  --source-columns '[{"name": "company", "description": "Company name"}]' \
  --intent "Find the CEO and annual revenue"

YAML config run

parallel-cli enrich run config.yaml

Status / polling

parallel-cli enrich status <task_group_id> --json
parallel-cli enrich poll <task_group_id> --json

Use explicit JSON arrays for column definitions when operating non-interactively. Validate the output file before reporting success.

FindAll

Use for web-scale entity discovery when the user wants a discovered dataset rather than a short answer.

parallel-cli findall run "Find AI coding agent startups with enterprise offerings" --json
parallel-cli findall run "AI startups in healthcare" -n 25 --json
parallel-cli findall status <run_id> --json
parallel-cli findall poll <run_id> --json
parallel-cli findall result <run_id> --json
parallel-cli findall schema <run_id> --json

This is a better fit than ordinary search when the user wants a discovered set of entities that can be reviewed, filtered, or enriched later.

Monitor

Use for ongoing change detection over time.

parallel-cli monitor list --json
parallel-cli monitor get <monitor_id> --json
parallel-cli monitor events <monitor_id> --json
parallel-cli monitor delete <monitor_id> --json

Creation is usually the sensitive part because cadence and delivery matter:

parallel-cli monitor create --help

Use this when the user wants recurring tracking of a page or source rather than a one-time fetch.

Recommended Mibyan usage patterns

Fast answer with citations

Run parallel-cli search ... --json
Parse titles, URLs, dates, excerpts
Summarize with inline citations from the returned URLs only

URL investigation

Run parallel-cli extract URL --json
If needed, rerun with --objective or --full-content
Quote or summarize the extracted markdown

Long research workflow

Run parallel-cli research run ... --no-wait --json
Store the returned ID
Continue other work or periodically poll
Summarize the final report with citations

Structured enrichment workflow

Inspect the input file and columns
Use enrich suggest or provide explicit enriched columns
Run enrich run
Poll for completion if needed
Validate the output file before reporting success

Error handling and exit codes

The CLI documents these exit codes:

0 success
2 bad input
3 auth error
4 API error
5 timeout

If you hit auth errors:

check parallel-cli auth
confirm PARALLEL_API_KEY or run parallel-cli login / parallel-cli login --device
verify parallel-cli is on PATH

Maintenance

Check current auth / install state:

parallel-cli auth
parallel-cli --help

Update commands:

parallel-cli update
pip install --upgrade parallel-web-tools
parallel-cli config auto-update-check off

Pitfalls

Do not omit --json unless the user explicitly wants human-formatted output.
Do not cite sources not present in the CLI output.
login may require PTY/browser interaction.
Prefer foreground execution for short tasks; do not overuse background processes.
For large result sets, save JSON to ~/.mibyan/cache/scratch/*.json (the Mibyan scratch dir) instead of stuffing everything into context.
Do not silently choose Parallel when Mibyan native tools are already sufficient.
Remember this is a vendor workflow that usually requires account auth and paid usage beyond the free tier.

​Skill metadata

​Reference: full SKILL.md

​Parallel CLI

​When to use it

​Installation

​Homebrew

​npm

​Python package

​Standalone installer

​Authentication

​Core rule set

​Quick reference

​Common flags and patterns

​Search

​Extraction

​Deep research

​Synchronous

​Async launch + poll

​Context chaining / follow-up

​Enrichment

​Suggest columns

​Plan a config

​Inline data

​Non-interactive file run

​YAML config run

​Status / polling

​FindAll

​Monitor

​Recommended Mibyan usage patterns

​Fast answer with citations

​URL investigation

​Long research workflow

​Structured enrichment workflow

​Error handling and exit codes

​Maintenance

​Pitfalls

Skill metadata

Reference: full SKILL.md

Parallel CLI

When to use it

Installation

Homebrew

npm

Python package

Standalone installer

Authentication

Core rule set

Quick reference

Common flags and patterns

Search

Extraction

Deep research

Synchronous

Async launch + poll

Context chaining / follow-up

Enrichment

Suggest columns

Plan a config

Inline data

Non-interactive file run

YAML config run

Status / polling

FindAll

Monitor

Recommended Mibyan usage patterns

Fast answer with citations

URL investigation

Long research workflow

Structured enrichment workflow

Error handling and exit codes

Maintenance

Pitfalls