Create geometric objects from coordinates.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionshapely-computeExecute the skills CLI command in your project's root directory to begin installation:
Fetches shapely-compute from parcadei/continuous-claude-v3 and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate shapely-compute. Access via /shapely-compute in your agent's command palette.
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 environment. Always review source, verify the publisher, and test in isolation before production.
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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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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| I want to... | Command | Example |
|---|---|---|
| Create geometry | create |
create polygon --coords "0,0 1,0 1,1 0,1" |
| Intersection | op intersection |
op intersection --g1 "POLYGON(...)" --g2 "POLYGON(...)" |
| Check contains | pred contains |
pred contains --g1 "POLYGON(...)" --g2 "POINT(0.5 0.5)" |
| Calculate area | measure area |
measure area --geom "POLYGON(...)" |
| Distance | distance |
distance --g1 "POINT(0 0)" --g2 "POINT(3 4)" |
| Transform | transform translate |
transform translate --geom "..." --params "1,2" |
| Validate | validate |
validate --geom "POLYGON(...)" |
Create geometric objects from coordinates.
# Point
uv run python scripts/shapely_compute.py create point --coords "1,2"
# Line (2+ points)
uv run python scripts/shapely_compute.py create line --coords "0,0 1,1 2,0"
# Polygon (3+ points, auto-closes)
uv run python scripts/shapely_compute.py create polygon --coords "0,0 1,0 1,1 0,1"
# Polygon with hole
uv run python scripts/shapely_compute.py create polygon --coords "0,0 10,0 10,10 0,10" --holes "2,2 8,2 8,8 2,8"
# MultiPoint
uv run python scripts/shapely_compute.py create multipoint --coords "0,0 1,1 2,2"
# MultiLineString (pipe-separated lines)
uv run python scripts/shapely_compute.py create multilinestring --coords "0,0 1,1|2,2 3,3"
# MultiPolygon (pipe-separated polygons)
uv run python scripts/shapely_compute.py create multipolygon --coords "0,0 1,0 1,1 0,1|2,2 3,2 3,3 2,3"
Boolean geometry operations.
# Intersection of two polygons
uv run python scripts/shapely_compute.py op intersection \
--g1 "POLYGON((0 0,2 0,2 2,0 2,0 0))" \
--g2 "POLYGON((1 1,3 1,3 3,1 3,1 1))"
# Union
uv run python scripts/shapely_compute.py op union --g1 "POLYGON(...)" --g2 "POLYGON(...)"
# Difference (g1 - g2)
uv run python scripts/shapely_compute.py op difference --g1 "POLYGON(...)" --g2 "POLYGON(...)"
# Symmetric difference (XOR)
uv run python scripts/shapely_compute.py op symmetric_difference --g1 "..." --g2 "..."
# Buffer (expand/erode)
uv run python scripts/shapely_compute.py op buffer --g1 "POINT(0 0)" --g2 "1.5"
# Convex hull
uv run python scripts/shapely_compute.py op convex_hull --g1 "MULTIPOINT((0 0),(1 1),(0 2),(2 0))"
# Envelope (bounding box)
uv run python scripts/shapely_compute.py op envelope --g1 "POLYGON(...)"
# Simplify (reduce points)
uv run python scripts/shapely_compute.py op simplify --g1 "LINESTRING(...)" --g2 "0.5"
Spatial relationship tests (returns boolean).
# Does polygon contain point?
uv run python scripts/shapely_compute.py pred contains \
--g1 "POLYGON((0 0,2 0,2 2,0 2,0 0))" \
--g2 "POINT(1 1)"
# Do geometries intersect?
uv run python scripts/shapely_compute.py pred intersects --g1 "..." --g2 "..."
# Is g1 within g2?
uv run python scripts/shapely_compute.py pred within --g1 "POINT(1 1)" --g2 "POLYGON(...)"
# Do geometries touch (share boundary)?
uv run python scripts/shapely_compute.py pred touches --g1 "..." --g2 "..."
# Do geometries cross?
uv run python scripts/shapely_compute.py pred crosses --g1 "LINESTRING(...)" --g2 "LINESTRING(...)"
# Are geometries disjoint (no intersection)?
uv run python scripts/shapely_compute.py pred disjoint --g1 "..." --g2 "..."
# Do geometries overlap?
uv run python scripts/shapely_compute.py pred overlaps --g1 "..." --g2 "..."
# Are geometries equal?
uv run python scripts/shapely_compute.py pred equals --g1 "..." --g2 "..."
# Does g1 cover g2?
uv run python scripts/shapely_compute.py pred covers --g1 "..." --g2 "..."
# Is g1 covered by g2?
uv run python scripts/shapely_compute.py pred covered_by --g1 "..." --g2 "..."
Geometric measurements.
# Area (polygons)
uv run python scripts/shapely_compute.py measure area --geom "POLYGON((0 0,1 0,1 1,0 1,0 0))"
# Length (lines, polygon perimeter)
uv run python scripts/shapely_compute.py measure length --geom "LINESTRING(0 0,3 4)"
# Centroid
uv run python scripts/shapely_compute.py measure centroid --geom "POLYGON((0 0,2 0,2 2,0 2,0 0))"
# Bounds (minx, miny, maxx, maxy)
uv run python scripts/shapely_compute.py measure bounds --geom "POLYGON(...)"
# Exterior ring (polygon only)
uv run python scripts/shapely_compute.py measure exterior_ring --geom "POLYGON(...)"
# All measurements at once
uv run python scripts/shapely_compute.py measure all --geom "POLYGON((0 0,2 0,2 2,0 2,0 0))"
Distance between geometries.
uv run python scripts/shapely_compute.py distance --g1 "POINT(0 0)" --g2 "POINT(3 4)"
# Returns: {"distance": 5.0, "g1_type": "Point", "g2_type": "Point"}
Affine transformations.
# Translate (move)
uv run python scripts/shapely_compute.py transform translate \
--geom "POLYGON((0 0,1 0,1 1,0 1,0 0))" --params "5,10"
# params: dx,dy or dx,dy,dz
# Rotate (degrees, around centroid by default)
uv run python scripts/shapely_compute.py transform rotate \
--geom "POLYGON((0 0,1 0,1 1,0 1,0 0))" --params "45"
# params: angle or angle,origin_x,origin_y
# Scale (from centroid by default)
uv run python scripts/shapely_compute.py transform scale \
--geom "POLYGON((0 0,1 0,1 1,0 1,0 0))" --params "2,2"
# params: sx,sy or sx,sy,origin_x,origin_y
# Skew
uv run python scripts/shapely_compute.py transform skew \
--geom "POLYGON(...)" --params "15,0"
# params: xs,ys (degrees)
Check and fix geometry validity.
# Check if valid
uv run python scripts/shapely_compute.py validate --geom "POLYGON((0 0,1 0,1 1✓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
Steps
- 1Install product management skill
- 2Start with user story generation for known feature
- 3Progress to competitive analysis: research 2-3 competitors
- 4Use for roadmap prioritization: apply RICE/ICE scoring
- 5Draft stakeholder communications and refine based on feedback
- 6Build template library for recurring PM tasks
- 7Share 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
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
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4.5★★★★★72 reviews- BBenjamin Abebe★★★★★Dec 28, 2024
Registry listing for shapely-compute matched our evaluation — installs cleanly and behaves as described in the markdown.
- SShikha Mishra★★★★★Dec 24, 2024
shapely-compute has been reliable in day-to-day use. Documentation quality is above average for community skills.
- KKaira Park★★★★★Dec 24, 2024
shapely-compute has been reliable in day-to-day use. Documentation quality is above average for community skills.
- LLayla Sethi★★★★★Dec 20, 2024
Keeps context tight: shapely-compute is the kind of skill you can hand to a new teammate without a long onboarding doc.
- HHiroshi Chawla★★★★★Dec 20, 2024
shapely-compute fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- IIsabella Park★★★★★Dec 16, 2024
I recommend shapely-compute for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- IIsabella Kapoor★★★★★Nov 19, 2024
Solid pick for teams standardizing on skills: shapely-compute is focused, and the summary matches what you get after install.
- AAditi Park★★★★★Nov 11, 2024
We added shapely-compute from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- DDiya Patel★★★★★Nov 7, 2024
Useful defaults in shapely-compute — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- KKaira Choi★★★★★Nov 3, 2024
Keeps context tight: shapely-compute is the kind of skill you can hand to a new teammate without a long onboarding doc.
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