research-pipeline-workflow

research-pipeline-workflow is a skill for Claude Code from Swih/mistral-mcp. It costs 58 tokens per session (1,372 once invoked), scanned A, original, MIT.

A guided research process that runs a multi-step Mistral Workflow, a hosted sequence of research tasks. It pauses at checkpoints so you can review, change, or add to the working hypotheses and sources.

In plain words
What is it for?
Use it for literature reviews, competitor research, and technical deep dives that need hypothesis checks and additional sources during the process.
Why use it?
It keeps long research tasks reviewable instead of leaving all assumptions and source choices hidden until the end.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the mistral-mcp plugin — 11 skills shipped together

Good fit Use it for literature reviews, competitor research, and technical deep dives that need hypothesis checks and additional sources during the process.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/swih/mistral-mcp/research-pipeline-workflow
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.

Any agent
npx skills add Swih/mistral-mcp --skill research-pipeline-workflow
Clone the repo
git clone --depth 1 https://github.com/Swih/mistral-mcp

Made for: Claude Code.

Or install mistral-mcp, the plugin that ships this one along with the rest of its 11 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for research-pipeline-workflow

README.md
[![agentmods](https://agentmods.dev/badge/skills/swih/mistral-mcp/research-pipeline-workflow/github.svg)](https://agentmods.dev/skills/swih/mistral-mcp/research-pipeline-workflow)
Your own site
<a href="https://agentmods.dev/skills/swih/mistral-mcp/research-pipeline-workflow"><img src="https://agentmods.dev/badge/skills/swih/mistral-mcp/research-pipeline-workflow/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for research-pipeline-workflow

Your own site · 80×15
<a href="https://agentmods.dev/skills/swih/mistral-mcp/research-pipeline-workflow"><img src="https://agentmods.dev/badge/skills/swih/mistral-mcp/research-pipeline-workflow.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 58 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,372 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 5.1 $0.00058 $0.01372
Opus 5 $0.00029 $0.00686
Sonnet 5 $0.00012 $0.00274
Haiku 4.5 $0.00006 $0.00137

Measured 12d ago against content hash d862826d4868, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

research-pipeline-workflow 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 12d 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.

claude-plugin/skills/research-pipeline-workflow/SKILL.md · 186 lines

How it starts

The opening of the file, as written. The whole thing — 186 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Research pipeline workflow

Orchestrates a deployed Mistral Workflow for multi-step research (literature review, competitive analysis, technical deep-dive). Queries hypotheses at each checkpoint, lets the user validate or amend them, and injects additional sources when needed — all via workflow_interact.

Profile note: workflow_execute, workflow_status, and workflow_interact are available in the core profile (default). No additional profile is required for workflow-only orchestration.

Temporal behavior: the workflow status stays RUNNING even when the pipeline is blocked waiting for hypothesis validation (a wait_for_input() pause in the workflow). Detect this state by querying a status handler — do not wait for a PAUSED status, which does not exist in this API.

Important: handler names are workflow-specific

Ask the user (or consult mistral://workflows) for:

  • Query handler for progress: e.g. "get_progress"
  • Query handler for hypotheses: e.g. "get_hypotheses"
  • Signal handler for hypothesis decisions: e.g. "hypothesis_decision"

Steps

Step 1 — Define the research mission

Ask the user for:

  1. workflowIdentifier — the deployed research workflow name or ID
  2. topic — the research question (be specific)
  3. depth"shallow" (quick scan), "medium" (balanced), "deep" (comprehensive)
  4. output_format"bullets", "report", or "json"
  5. Optional: initial source URLs or file_ids to seed the pipeline
  6. Handler names (if non-default)

Step 2 — Launch the pipeline

Call workflow_execute:

{
  "workflowIdentifier": "<workflow name or ID>",
  "input": {
    "topic": "<research question>",
    "depth": "medium",
    "output_format": "report",
    "sources": ["<url_or_file_id>"]
  }
}

Note structuredContent.execution_id. Confirm: "Research pipeline started — execution ID: <execution_id>."

Step 3 — Poll and query progress

Loop:

  1. Call workflow_status with { "executionId": "<execution_id>" }
  2. Check structuredContent.status:
    • COMPLETED → go to Step 5
    • FAILED / TIMED_OUT / CANCELED → surface error and stop
    • RUNNING → continue

Read the full file on GitHub · 186 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. 12d ago First seen · 186 lines · 58 tokens per session scan A d862826d4868

Subscribe to this mod's changes

research-pipeline-workflow is a skill published in the GitHub repository Swih/mistral-mcp (15 stars, last pushed 3d ago), licensed MIT. It adds 58 tokens to every session and 1,372 once invoked, about $0.0003 per session on Opus 5. 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.

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