# Send NumPy to your agent Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually. ## Fast path - Download the package from Yavira. - Extract it into a folder your agent can access. - Paste one of the prompts below and point your agent at the extracted folder. ## Suggested prompts ### New install ```text I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete. ``` ### Upgrade existing ```text I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run. ``` ## Machine-readable fields ```json { "schemaVersion": "1.0", "item": { "slug": "numpy", "name": "NumPy", "source": "tencent", "type": "skill", "category": "AI 智能", "sourceUrl": "https://clawhub.ai/ivangdavila/numpy", "canonicalUrl": "https://clawhub.ai/ivangdavila/numpy", "targetPlatform": "OpenClaw" }, "install": { "downloadUrl": "/downloads/numpy", "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=numpy", "sourcePlatform": "tencent", "targetPlatform": "OpenClaw", "packageFormat": "ZIP package", "primaryDoc": "SKILL.md", "includedAssets": [ "SKILL.md", "memory-template.md", "setup.md" ], "downloadMode": "redirect", "sourceHealth": { "source": "tencent", "status": "healthy", "reason": "direct_download_ok", "recommendedAction": "download", "checkedAt": "2026-04-30T16:55:25.780Z", "expiresAt": "2026-05-07T16:55:25.780Z", "httpStatus": 200, "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=network", "contentType": "application/zip", "probeMethod": "head", "details": { "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=network", "contentDisposition": "attachment; filename=\"network-1.0.0.zip\"", "redirectLocation": null, "bodySnippet": null }, "scope": "source", "summary": "Source download looks usable.", "detail": "Yavira can redirect you to the upstream package for this source.", "primaryActionLabel": "Download for OpenClaw", "primaryActionHref": "/downloads/numpy" }, "validation": { "installChecklist": [ "Use the Yavira download entry.", "Review SKILL.md after the package is downloaded.", "Confirm the extracted package contains the expected setup assets." ], "postInstallChecks": [ "Confirm the extracted package includes the expected docs or setup files.", "Validate the skill or prompts are available in your target agent workspace.", "Capture any manual follow-up steps the agent could not complete." ] } }, "links": { "detailUrl": "https://openagent3.xyz/skills/numpy", "downloadUrl": "https://openagent3.xyz/downloads/numpy", "agentUrl": "https://openagent3.xyz/skills/numpy/agent", "manifestUrl": "https://openagent3.xyz/skills/numpy/agent.json", "briefUrl": "https://openagent3.xyz/skills/numpy/agent.md" } } ``` ## Documentation ### Setup On first use, read setup.md for integration guidelines. Creates ~/numpy/ to store preferences and snippets. ### When to Use User needs numerical computing in Python. Agent handles array operations, mathematical computations, linear algebra, and data manipulation with NumPy. ### Architecture Memory lives in ~/numpy/. See memory-template.md for structure. ~/numpy/ ├── memory.md # Preferences + common patterns used └── snippets/ # User's saved code patterns ### Quick Reference TopicFileSetup processsetup.mdMemory templatememory-template.md ### 1. Vectorize First Never use Python loops for array operations. NumPy's vectorized operations are 10-100x faster. # BAD - Python loop result = [] for x in arr: result.append(x * 2) # GOOD - Vectorized result = arr * 2 ### 2. Understand Broadcasting Broadcasting allows operations on arrays of different shapes. Know the rules: Dimensions align from the right Size-1 dimensions stretch to match Missing dimensions treated as size-1 # Shape (3,1) + (4,) broadcasts to (3,4) a = np.array([[1], [2], [3]]) # (3,1) b = np.array([10, 20, 30, 40]) # (4,) result = a + b # (3,4) ### 3. Prefer Views Over Copies Slicing returns views (same memory). Use .copy() only when needed. # View - modifying b changes a b = a[::2] # Copy - independent b = a[::2].copy() ### 4. Use Appropriate Dtypes Choose the smallest dtype that fits your data. Saves memory and speeds up computation. # For integers 0-255 arr = np.array(data, dtype=np.uint8) # For floats that don't need double precision arr = np.array(data, dtype=np.float32) ### 5. Axis Awareness Most functions accept axis parameter. Know your axes: axis=0: operate along rows (down columns) axis=1: operate along columns (across rows) axis=None or omit: operate on flattened array arr = np.array([[1, 2], [3, 4]]) np.sum(arr, axis=0) # [4, 6] - sum each column np.sum(arr, axis=1) # [3, 7] - sum each row ### 6. Leverage Built-in Functions NumPy has optimized functions for common operations. Don't reinvent them. NeedUseElement-wise mathnp.sin, np.exp, np.logStatisticsnp.mean, np.std, np.medianLinear algebranp.dot, np.linalg.*Sortingnp.sort, np.argsortSearchingnp.where, np.searchsorted ### Shape Mismatches # TRAP: Confusing (n,) with (n,1) or (1,n) a = np.array([1, 2, 3]) # shape (3,) b = np.array([[1, 2, 3]]) # shape (1,3) c = np.array([[1], [2], [3]]) # shape (3,1) # FIX: Use reshape or newaxis a.reshape(-1, 1) # (3,1) a[np.newaxis, :] # (1,3) ### Silent Type Coercion # TRAP: Integer array silently truncates floats arr = np.array([1, 2, 3]) # int64 arr[0] = 1.9 # becomes 1, not 1.9! # FIX: Declare dtype upfront arr = np.array([1, 2, 3], dtype=np.float64) ### View vs Copy Confusion # TRAP: Fancy indexing returns copy, slicing returns view arr = np.array([1, 2, 3, 4, 5]) # This is a VIEW (changes affect original) view = arr[1:4] # This is a COPY (independent) copy = arr[[1, 2, 3]] ### Broadcasting Surprises # TRAP: Unexpected broadcasting a = np.array([1, 2, 3]) b = np.array([1, 2]) a + b # ERROR - shapes don't broadcast # TRAP: Accidental broadcasting a = np.zeros((3, 4)) b = np.array([1, 2, 3]) a + b # ERROR - (3,4) and (3,) don't align a + b.reshape(-1, 1) # Works - (3,4) and (3,1) ### In-Place Operations # TRAP: Some operations modify in-place, others don't np.sort(arr) # Returns sorted copy arr.sort() # Sorts in-place # Safe pattern: be explicit arr = np.sort(arr) # Clear intent ### Create Arrays np.zeros((3, 4)) # All zeros np.ones((3, 4)) # All ones np.full((3, 4), 7) # All sevens np.eye(3) # Identity matrix np.arange(0, 10, 2) # [0, 2, 4, 6, 8] np.linspace(0, 1, 5) # [0, 0.25, 0.5, 0.75, 1] np.random.rand(3, 4) # Uniform [0,1) np.random.randn(3, 4) # Normal distribution ### Reshape and Stack arr.reshape(2, 6) # New shape (must match size) arr.flatten() # 1D copy arr.ravel() # 1D view np.concatenate([a, b]) # Join along existing axis np.stack([a, b]) # Join along new axis np.vstack([a, b]) # Stack vertically np.hstack([a, b]) # Stack horizontally ### Boolean Indexing arr = np.array([1, 5, 3, 8, 2]) mask = arr > 3 arr[mask] # [5, 8] arr[arr > 3] = 0 # Replace values > 3 with 0 np.where(arr > 3, 1, 0) # 1 where >3, else 0 ### Linear Algebra np.dot(a, b) # Matrix multiplication a @ b # Same (Python 3.5+) np.linalg.inv(a) # Inverse np.linalg.det(a) # Determinant np.linalg.eig(a) # Eigenvalues/vectors np.linalg.solve(a, b) # Solve Ax = b ### Security & Privacy Data that stays local: All computations run locally Code patterns saved in ~/numpy/ This skill does NOT: Send data externally Access files outside ~/numpy/ Require network connectivity ### Related Skills Install with clawhub install if user confirms: data — data processing workflows math — mathematical computations statistics — statistical analysis ### Feedback If useful: clawhub star numpy Stay updated: clawhub sync ## Trust - Source: tencent - Verification: Indexed source record - Publisher: ivangdavila - Version: 1.0.0 ## Source health - Status: healthy - Source download looks usable. - Yavira can redirect you to the upstream package for this source. - Health scope: source - Reason: direct_download_ok - Checked at: 2026-04-30T16:55:25.780Z - Expires at: 2026-05-07T16:55:25.780Z - Recommended action: Download for OpenClaw ## Links - [Detail page](https://openagent3.xyz/skills/numpy) - [Send to Agent page](https://openagent3.xyz/skills/numpy/agent) - [JSON manifest](https://openagent3.xyz/skills/numpy/agent.json) - [Markdown brief](https://openagent3.xyz/skills/numpy/agent.md) - [Download page](https://openagent3.xyz/downloads/numpy)