send-to-linear

casper-studios/casper-marketplace · updated Apr 8, 2026

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$npx skills add https://github.com/casper-studios/casper-marketplace --skill send-to-linear
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summary

Turn unstructured input into well-structured Linear tickets.

skill.md

Send to Linear

Turn unstructured input into well-structured Linear tickets.

Setup

Config resolution

Look for team configuration in this order (first match wins):

  1. ~/.agents/configs/send-to-linear/config.json (user overrides)
  2. references/config.json (bundled defaults, relative to this skill file)

Use the user config if found. Otherwise fall back to the bundled config.json.

If no user config exists AND the bundled config has empty required fields (team, default_assignee), stop and prompt the user:

No local config found. The bundled references/config.json has empty defaults and will be overwritten whenever this skill updates.

Create a user config at: ~/.agents/configs/send-to-linear/config.json

Copy the bundled references/config.json as a starting point and fill in your team, project, assignee, labels, and conventions.

Template resolution

Same pattern for the ticket template:

  1. ~/.agents/configs/send-to-linear/ticket-template.md (user overrides)
  2. references/ticket-template.md (bundled default)

The template rarely needs customization, so no prompt if only the bundled version exists.

Phase 1: Ingest Input

Accept any combination of:

  • Slack dump — pasted messages or fetched via Slack MCP tools
  • Call transcript — from Fireflies (mcp__fireflies__* tools), pasted text, or a file
  • Screenshots — read image files with the Read tool
  • Meeting notes / docs — any markdown, text, or document content
  • User description — freeform "we need X, Y, Z"

If the user's input is ambiguous or incomplete, ask one clarifying question before proceeding. Do not over-interrogate.

For Fireflies transcripts specifically: fetch both the summary (fireflies_get_summary) and full transcript (fireflies_get_transcript). Use the summary for topic identification, the full transcript for detail extraction.

Phase 2: Extract Topics and Actionable Items

For short input (<5K chars): extract directly in the main context.

For long input (>5K chars, e.g. full call transcripts): launch parallel subagents, one per major topic or time segment, to extract:

  1. Concrete examples and use cases — the most critical output. Capture specific scenarios, companies/tickers, KPIs, exact quotes, timestamps, and reasoning chains. A ticket saying "fix interpretation errors" is useless. A ticket saying "fix interpretation errors — e.g., when commentary says 'flat to down' for Intel CapEx but consensus expects 'down', recognize this as upward revision" is actionable.
  2. Feature requirements — what needs to be built or changed
  3. Limitations/boundaries — things explicitly ruled out
  4. Acceptance criteria — testable conditions

Categorize items as:

  • Actionable tickets — clear scope, can be worked on immediately
  • Ideas / needs more thought — visionary or exploratory, tracked but not immediately actionable

Phase 3: Draft Tickets to Scratchpad

Write to .claude/scratchpad/linear-tickets-YYYY-MM-DD.md using the format from references/ticket-template.md.

Every ticket with a real-world example from the source MUST include that example verbatim — do not summarize away specifics.

Phase 4: Verification (for long-form input only)

Skip for short/simple input. For call transcripts or long Slack threads:

Launch a verification subagent that reads the full source material and the drafted tickets, then produces a gap list:

  • Use cases mentioned but missing from tickets
  • Action items assigned to people but not captured
  • Design decisions agreed upon but not in acceptance criteria
  • Any "we need to do X" statements not in any ticket

Update the draft with any gaps found.

Phase 5: User Review

STOP. Tell the user the file is ready and wait for explicit instruction before creating anything in Linear.

The user may restructure, merge, split, rename, add notes, or tell you to skip items. Apply all feedback to the scratchpad file before proceeding.

Phase 6: Create in Linear (only on explicit approval)

Read config using the resolution order from Setup, then:

  1. mcp__linear__list_teams — resolve team ID
  2. mcp__linear__list_issue_labels — resolve label IDs
  3. mcp__linear__list_projects — resolve project ID (if configured)
  4. mcp__linear__list_cycles with type: "current" — resolve current cycle (if assign_to_current_cycle is true)

Create each approved ticket with mcp__linear__create_issue:

  • team: from config
  • project: from config
  • assignee: from config
  • cycle: current cycle number
  • state: from config default_status
  • labels: matched from ticket's Labels field
  • title: from ticket
  • description: full ticket body in markdown
  • links: source link if available (e.g. Fireflies transcript URL, Slack permalink)

Report created ticket identifiers back to the user.

Key Rules

  1. Concrete examples are non-negotiable. Engineers understand what to build from specific scenarios, not abstract descriptions. Preserve full reasoning chains, exact quotes, and real-world context.
  2. Never create tickets without user approval. Always draft to scratchpad first.
  3. Config is defaults, not constraints. User can override team, assignee, labels, or any other field per invocation.
  4. Don't over-consolidate. If two items are independent work, they're separate tickets. Group only when items are truly part of the same deliverable.
  5. Standalone requirements buried in conversation are tickets too. Things said in passing like "we also need to compare dates" represent distinct work items.
how to use send-to-linear

How to use send-to-linear on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add send-to-linear
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/casper-studios/casper-marketplace --skill send-to-linear

The skills CLI fetches send-to-linear from GitHub repository casper-studios/casper-marketplace and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/send-to-linear

Reload or restart Cursor to activate send-to-linear. Access the skill through slash commands (e.g., /send-to-linear) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.660 reviews
  • Pratham Ware· Dec 28, 2024

    Solid pick for teams standardizing on skills: send-to-linear is focused, and the summary matches what you get after install.

  • Yusuf Shah· Dec 16, 2024

    send-to-linear reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Sophia Srinivasan· Dec 8, 2024

    send-to-linear fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Chaitanya Patil· Dec 4, 2024

    Useful defaults in send-to-linear — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Ren Mensah· Nov 27, 2024

    Registry listing for send-to-linear matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Piyush G· Nov 23, 2024

    send-to-linear is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Sophia Harris· Nov 7, 2024

    We added send-to-linear from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Luis Dixit· Oct 26, 2024

    send-to-linear fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Kofi Flores· Oct 18, 2024

    send-to-linear reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Shikha Mishra· Oct 14, 2024

    Keeps context tight: send-to-linear is the kind of skill you can hand to a new teammate without a long onboarding doc.

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