{"slug":"google-ai-edge-gpu-clean-conversion","name":"gpu-clean-conversion","description":"Convert a PyTorch or Hugging Face model into a LiteRT model that runs fully on the GPU via the CompiledModel API with verified-correct output, and lay it out as a model recipe. Use when converting a new model, or when a converted model is rejected by the GPU, falls back to CPU, or returns wrong numbers on device.","long_description":"---\nname: gpu-clean-conversion\ndescription: Convert a PyTorch or Hugging Face model into a LiteRT model that runs fully on the GPU via the CompiledModel API with verified-correct output, and lay it out as a model recipe. Use when converting a new model, or when a converted model is rejected by the GPU, falls back to CPU, or returns wrong numbers on device.\n---\n\n# GPU-clean conversion\n\nA conversion is done when three things hold, in this order:\n\n1. it converts,\n2. every node runs on the GPU,\n3. **the on-device output matches the source model.**\n\nStep 3 is not implied by step 2. The delegate can report full residency and\nstill return wrong numbers — that is the failure mode most of the rewrites\nbelow exist for. Never call a model done without a numerical check against\nCPU or PyTorch on the actual device.\n\n## Loop\n\n**1. Convert plain first.** No patches. This tells you what the model\nactually needs rather than what you assumed.\n\n**2. Verify through the CompiledModel API before touching a device.**\n\n```python\nfrom litert_gpu_toolkit import check_gpu_compatibility, print_report\nprint_report(check_gpu_compatibility(\"model.tflite\"))\n```\n\nIt compiles the model for the GPU accelerator, runs every signature on\nrandom inputs, and compares the outputs against a CPU-compiled reference.\nA failed GPU compile names the offending op — route it through the table\nbelow. A pass with a CPU-fallback warning means some ops fell back to the\nCPU; treat them the same way if you need full residency. This exercises the\nhost GPU, so step 5 on the actual device is still required.\n\n**3. Map each symptom to a rewrite.** The rewrites live in\n`utilities/litert_gpu_toolkit` — plain Python, no build step. Outside this\nrepo, clone litert-samples and put `utilities/` on `PYTHONPATH`, or vendor\nthe directory:\n\n| What you see | Rewrite |\n|---|---|\n| `GATHER_ND` named in the compile error | Find its source. Stride-2 slicing (`x[:, ::2]`, Focus stems, patch merging), `grid_sample`, `MaxPool` padding, bicubic `interpolate`, and reflect-mode `F.pad` all lower to it. `patch_grid_sample`, `patch_maxpool_zeropad`, `patch_interpolate`, `patch_patch_merging` |\n| A rank-5+ tensor named in the compile error | `PixelShuffle` (rank-6 reshape), windowed attention, `einops.rearrange`, packed-QKV attention head splits. `pixelshuffle_to_conv_transpose`, `patch_window_attention`, `patch_einops` |\n| `TRANSPOSE_CONV` rejected | Version skew, not a missing op. `ZeroStuffConvT1d` / `ZeroStuffConvT2d` — zero-stuff plus a plain conv, exact to ~1e-7 |\n| `SELECT` / `SELECT_V2` | `PReLU`, `ELU`, in-place index assignment, and `torch.where` masking. Replace with arithmetic: `x*(1-m) + v*m` |\n| `BROADCAST_TO` | Two cases. On a compile-time constant: an outer product or `.expand` that did not fold — bake the result as a constant at its target shape. On a **runtime** tensor the GPU delegate rejects it outright, even at rank 4 — the canonical case is GQA's `repeat_kv` (`x[:,:,None].expand(...)`, which is also rank-5, so two walls in one line). Exact rewrite: `torch.cat([x[:, i:i+1].expand(b, n_rep, s, d) for i in range(n_kv)], dim=1)` — same head order, bit-exact. Tracked upstream: google-ai-edge/LiteRT#9191 |\n| Masked attention wrong only on device: token 0 bit-exact, every later token wrong | Broadcast `ADD` whose LHS is a `BATCH_MATMUL` result (the `scores + mask[1,1,S,S]` idiom) silently miscomputed on older runtimes (fixed in newer; head-axis size-1 broadcast only). The signature mimics broken RoPE — tap the rope output before blaming it. Rewrites: pre-expand the mask to `[1,H,S,S]`, or materialize the BMM as a second output |\n| An `ADD` result that is both a graph output **and** consumed downstream comes back wrong | Output aliasing: the returned tensor holds an *operand*, not the sum — `ADD` with two runtime operands (`SUB`/`MUL` exact, `x + 1.0` exact). This is the shape of every explicit state update in a streaming/recurrent graph. Workaround: emit `acc * one` where `one` is a **runtime** input holding 1.0 — a constant folds straight back into the pattern. Tracked upstream: google-ai-edge/LiteRT#8599 |\n| `RELU_0_TO_1` rejected by the GPU delegate | Emitted by hard-sigmoid / `nn.Hardtanh(0,1)`. Accepted in litert 2.1.3, rejected from 2.1.5 on — a model at full residency on an older runtime hard-fails `CompiledModel` creation after an upgrade. Rewrite: `relu(x) - relu(x-1)`, exact. Tracked upstream: google-ai-edge/LiteRT#8598 |\n| `DIV: No support of few identical inputs` / `Expected 1 const input tensor(s)`, device only | The delegate declines an op whose two inputs are the same tensor, and ops whose inputs are all constants — together these split a perceiver-style block (softmax over a length-1 axis of a constant latent bank) into several partitions. Fixes: special-case the degenerate axis (a softmax over a length-1 axis is identically 1), or make one input non-constant. Note the sibling LayerNorm-over-a-constant pattern no longer reaches the delegate at all — the converter folds it to a single `MUL`. Tracked upstream: google-ai-edge/LiteRT#9192 |\n| `NHWC node rewriter not found: amax` | `x.amax(...)`/`x.max(dim)` in stable-softmax, adaptive norms, qk-norm. Rewrite channel reduce-max as `max_pool2d(x.reshape(N,1,C,H*W), kernel=(C,1))` — numerically identical — or drop the norm to 3D |\n| `Lowering not found: aten._fft_r2c` / `aten.complex` | `torch.stft`/`istft` and complex views have no lowering (fails before any GPU check). A DFT is a fixed linear map: windowed-DFT as `Conv1d` with the cos/sin basis baked into kernels (stride = hop), iSTFT as inverse-DFT matmul + overlap-add via zero-stuffed conv-transpose — exact. Model-selection corollary: prefer time-domain vocoder branches over iSTFT-based ones. ⚠ Library STFT-as-conv stacks (torchlibrosa-style) have **numerically mis-converted** (corr 0.83) while the op check looks clean — verify the spectrogram numerically or compute log-mel host-side |\n| Compiles, runs, output is wrong or NaN | The fp16 reduction family — and the trigger is the **fp16 accumulator passing 65504**, so a plain single-axis mean/sum over enough elements overflows just like variance does. `patch_safe_layernorm`, `patch_rmsnorm`, `patch_instance_norm`, `hierarchical_mean`. Caveats: at extreme magnitudes (\\|x\\| in the thousands) even the adaptive safe-LN form overflows when it reconstructs the large variance — the robust form stays entirely in the down-scaled domain (`xs = x/S`, normalize `xs`, never multiply the variance back by `S²`); `hierarchical_mean` is exact only for power-of-two spatial dims (for arbitrary dims, cascade `/2` avg-pools with `ceil_mode` so each stage averages ≤~49 elements). Diagnostic split: **all-zero/all-blank output = an overflow in one block; a result that starts near-correct and degrades with depth = precision compounding**, which no overflow patch (and no fp32-precision flag) fixes |\n| Head outputs exactly zero | RMSNorm `Σx²` overflowed fp16 to `inf`. `patch_rmsnorm` |\n\n**4. Re-convert and re-verify.** Repeat 2–3 until the check passes.\n\n**5. Verify on device, numerically.** Run the same input through the source\nmodel and the on-device graph and compare. Correlation on the output tensor,\nplus a task-level check where one exists (argmax match, IoU, mask foreground\ncount). Record the device and the residency line (`N/N` nodes) alongside the\nnumber — a result without both is not reproducible.\n\nIf residency is full and the output is wrong, bisect by intermediate: dump\ntensors at block boundaries and find the first one that diverges. The cause is\nusually a reduction, not the op you suspect.\n\n## Watch for\n\n- **fp16 is used even for an fp32 graph.** The delegate reduces in fp16\n  regardless of tensor dtype. Anything that sums many large values — variance,\n  `Σx²`, multi-axis mean — is a candidate.\n- **Approximation choices are not free.** Substituting a GELU or Swish\n  approximation changes numerics. A head with a wide output range can lose\n  real accuracy to the coarser form.\n- **`inplace=True` activations mutate a residual.** `out = self.act(x)` then\n  `x + out` adds `relu(x)`, not `x`. Swapping the activation silently changes\n  the math.\n- **A silent CPU fallback looks like success.** If the GPU output is\n  bit-identical to CPU fp32, it probably did not run on the GPU. Genuine fp16\n  execution drifts in the last digits.\n- **Reflect padding routes through `F.pad` even at `padding=0`** — every\n  conv with `padding_mode='reflect'` is affected, not just padded ones.\n  Slice+concat reflect pads are cheap for small pad widths.\n- **Channel-attention through a `Linear` confuses layout handling at\n  convert time** (`tfl.mul operands don't have broadcast-compatible\n  shapes`) — express channel attention as a 1×1 conv.\n- **`.chunk()` lowers to `SPLIT`** (GPU-rejected) — slice directly\n  instead; bit-exact.\n- **Constant folding can explode the file**: a frozen-param × constant\n  product materializes at full size per block (fp16 casting skips\n  non-weight constants, so it cannot rescue it). Feed one factor as a\n  runtime input — param × input never folds.\n\n## Output layout\n\nShip the result as a model recipe, not a one-off script:\n\n```\nmodels/<family>/<model>/\n  README.md                 what the model is, what was produced\n  converted/\n    README.md               environments, pipeline, verification results\n    export_*.py             one script per graph\n    dump_*_ref.py           reference dumps from the source model\n    verify_*.py             parity check against those references\n```\n\nKeep the export, the reference dump, and the verification separate so each\ncan be re-run alone. State the verification numbers in the README with the\ndevice they came from. Weights are not committed.\n\nThe tree above is this repo's convention. In your own project the directory\nnames matter less than the split — keep the same three separately-runnable\npieces wherever your models live.\n\nDo the model first. A demo can follow, and reusable inference code matters\nmore in it than a full UI.\n","tagline":"Convert a PyTorch or Hugging Face model into a LiteRT model that runs fully on the GPU via the CompiledModel API with verified-correct output, and lay it out as a model recipe. Use when converting a new model, or when a converted model is rejected by the GPU, falls back to CPU, o","category":"automation","tags":["agent-skill"],"author":"google-ai-edge","verified":false,"attribution":{"status":"registry_indexed","statusLabel":"Registry indexed","shortLabel":"REGISTRY INDEXED","sourceLabel":"recursive skill source sync","sourceDetail":"google-ai-edge/litert-samples","creatorName":"google-ai-edge","creatorUrl":"https://github.com/google-ai-edge","sourceUrl":"https://github.com/google-ai-edge/litert-samples/tree/main/skills/gpu-clean-conversion","indexedBy":"OpenAgentSkill community index","claimUrl":"https://www.openagentskill.com/skills/google-ai-edge-gpu-clean-conversion#claim-this-skill","claimCta":"Claim this skill","trustNote":"This listing was indexed from public sources and is not marked official until a maintainer claim is approved.","publicNote":"Attribution links to the public repository or creator profile. 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use only in a sandbox with explicit approval.","Quality score needs review","Permission surface needs review: secrets or environment access, filesystem or document access","Permission surface: secrets or environment access, filesystem or document access"],"backward_compatible":{"trust_score_v4":{"version":"trust-score-v4","score":76,"tier":"strong","label":"Strong shortlist","summary":"Good trust signals with a few areas worth checking before rollout."}}},"trust_score_v4":{"version":"trust-score-v4","score":76,"tier":"strong","label":"Strong shortlist","summary":"Good trust signals with a few areas worth checking before rollout.","recommendedAction":"Test in a sandbox workflow and compare its install path with close alternatives.","dimensions":[{"id":"github_adoption","label":"GitHub adoption","score":62,"weight":0.13,"status":"info","detail":"416 GitHub stars"},{"id":"repo_activity","label":"Stars/forks activity","score":62,"weight":0.08,"status":"info","detail":"416 stars, 116 forks; issue activity unavailable in current metadata"},{"id":"maintenance","label":"Recent maintenance","score":100,"weight":0.14,"status":"pass","detail":"3d since push"},{"id":"license","label":"License clarity","score":86,"weight":0.09,"status":"pass","detail":"Apache-2.0"},{"id":"documentation","label":"README/SKILL.md completeness","score":86,"weight":0.14,"status":"pass","detail":"Metadata includes enough usage and workflow context"},{"id":"dependency_risk","label":"Dependency/runtime risk","score":64,"weight":0.12,"status":"info","detail":"credential or environment access, network or browser surface"},{"id":"installability","label":"Install availability","score":92,"weight":0.1,"status":"pass","detail":"npx skills add google-ai-edge/litert-samples --skill gpu-clean-conversion"},{"id":"install_safety","label":"Install command safety","score":92,"weight":0.1,"status":"pass","detail":"standard package or runtime install path"},{"id":"permission_surface","label":"Permission surface","score":46,"weight":0.07,"status":"warn","detail":"secrets or environment access, filesystem or document access"},{"id":"repository","label":"Repository evidence","score":86,"weight":0.04,"status":"pass","detail":"https://github.com/google-ai-edge/litert-samples/tree/main/skills/gpu-clean-conversion"},{"id":"review_status","label":"Review status","score":88,"weight":0.05,"status":"pass","detail":"AI review data available"},{"id":"agent_outcomes","label":"Agent Proven outcomes","score":54,"weight":0.13,"status":"info","detail":"No agent outcome data yet"}],"checks":[{"status":"info","label":"GitHub adoption","detail":"416 GitHub stars"},{"status":"info","label":"Stars/forks activity","detail":"416 stars, 116 forks; issue activity unavailable in current metadata"},{"status":"pass","label":"Recent maintenance","detail":"3d since push"},{"status":"pass","label":"License clarity","detail":"Apache-2.0"},{"status":"pass","label":"README/SKILL.md completeness","detail":"Metadata includes enough usage and workflow context"},{"status":"info","label":"Dependency/runtime risk","detail":"credential or environment access, network or browser surface"},{"status":"pass","label":"Install availability","detail":"npx skills add google-ai-edge/litert-samples --skill gpu-clean-conversion"},{"status":"pass","label":"Install command safety","detail":"standard package or runtime install path"},{"status":"warn","label":"Permission surface","detail":"secrets or environment access, filesystem or document access"},{"status":"pass","label":"Repository evidence","detail":"https://github.com/google-ai-edge/litert-samples/tree/main/skills/gpu-clean-conversion"},{"status":"pass","label":"Review status","detail":"AI review data available"},{"status":"info","label":"Agent Proven outcomes","detail":"No agent outcome data yet"},{"status":"warn","label":"Ownership","detail":"No approved owner claim yet"},{"status":"pass","label":"OpenAgentSkill usage","detail":"4 views, 0 install copies"},{"status":"info","label":"Agent outcomes","detail":"No agent outcome data yet"}],"strengths":["AI review approved","Install path is available","Repository evidence is available","Recently maintained repository","Install command has no obvious high-risk pattern"],"warnings":["This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.","Quality score needs review","Permission surface needs review: secrets or environment access, filesystem or document access","Permission surface: secrets or environment access, filesystem or document access"],"evidence":{"stars":"416 GitHub stars","repoActivity":"416 stars, 116 forks","lastPushed":"3d since push","license":"Apache-2.0","repository":"https://github.com/google-ai-edge/litert-samples/tree/main/skills/gpu-clean-conversion","install":"npx skills add google-ai-edge/litert-samples --skill gpu-clean-conversion","installSafety":"standard package or runtime install path","permissionSurface":"secrets or environment access, filesystem or document access","documentation":"Strong README/SKILL.md context","agentOutcomes":"No agent outcome data yet"},"installReadiness":{"ready":true,"command":"npx skills add google-ai-edge/litert-samples --skill gpu-clean-conversion","policy":"sandbox_only","label":"Sandbox only","notes":["Install path is available","Repository evidence is available","License is declared","No Agent Proven outcome evidence yet","3d since push"]},"agentCompatibility":["Codex","Claude Code","Cursor","OpenAgentSkill CLI"],"riskSummary":{"level":"medium","label":"Review before production","notes":["This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.","Quality score needs review","Permission surface needs review: secrets or environment access, filesystem or document access","Permission surface: secrets or environment access, filesystem or document access"]},"outcomeEvidence":{"total":0,"successes":0,"failures":0,"notRelevant":0,"successRate":null,"installAttempts":0,"riskBlocked":0,"setupRequired":0,"installSuccessRate":null,"avgOutputQuality":null,"avgTimeToUsefulMs":null,"productionOutcomes":0,"humanReviewRequired":0,"recentSuccessRate":null,"recentFailureRate":null,"uniqueAgents":0,"agentProvenScore":0,"agentProvenLabel":"Needs first agent run","lastOutcomeAt":null,"label":"No agent outcome data yet"},"autoInstall":{"allowed":false,"sandboxRequired":true,"policy":"sandbox_only","reason":"Human review or sandbox validation is required before automatic installation."},"bestFor":["automation","agent-skill"],"doNotUseFor":["Production credentials, payments, or irreversible account changes without explicit human review","Sensitive private data before reviewing repository code, license, and permission surface","Automatic installation in a production workspace","Live brokerage, exchange, wallet, or payment credentials outside an explicitly approved sandbox"],"knownRisks":["This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.","Quality score needs review","Permission surface needs review: secrets or environment access, filesystem or document access","Permission surface: secrets or environment access, filesystem or document access"]},"agent_proven":{"version":"agent-proven-v1","score":0,"tier":"unproven","label":"Needs first agent run","summary":"No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.","metrics":{"totalOutcomes":0,"successfulOutcomes":0,"failedOutcomes":0,"installAttempts":0,"installSuccessRate":null,"successRate":null,"recentSuccessRate":null,"recentFailureRate":null,"riskBlocked":0,"setupRequired":0,"notRelevant":0,"avgOutputQuality":null,"avgTimeToUsefulMs":null,"productionOutcomes":0,"humanReviewRequired":0,"uniqueAgents":0,"lastOutcomeAt":null},"signals":[],"penalties":["No real agent outcome evidence yet"]},"outcome_stats":null,"safety":{"score":49,"level":"avoid_auto_install","label":"Avoid automatic install","safety_tier":{"tier":"blocked","label":"Blocked for auto-install","badge":"BLOCKED","summary":"This skill should not be selected by an agent without explicit human security review.","recommended_action":"Do not auto-install. Inspect the source, dependencies, and permission surface first.","auto_install_policy":"block","reasons":["Audit risk exceeds the requested agent policy","Audit classified this skill as risky","Audit risk risky exceeds max_risk=medium"]},"auto_install_allowed":false,"human_review_required":true,"blocked":true,"audit_risk":"risky","permission_hints":[{"id":"browser","label":"Browser automation","reason":"Skill may drive a browser or interact with web pages.","severity":"medium"},{"id":"network","label":"Network access","reason":"Skill likely fetches remote pages, APIs, repositories, or external services.","severity":"medium"},{"id":"filesystem","label":"Filesystem access","reason":"Skill may read or write project files, documents, generated artifacts, or local workspace state.","severity":"medium"},{"id":"secrets","label":"Secrets or environment access","reason":"Skill metadata references credentials, tokens, environment variables, or secret-bearing workflows.","severity":"high"}],"policy_warnings":["Audit risk risky exceeds max_risk=medium","High-risk permission hints: Secrets or environment access","Permission surface may require sandboxing"],"constraints_applied":{"max_risk":"medium","needs_install_command":true,"min_stars":0}},"safety_gate":{"tier":"blocked","label":"Blocked for auto-install","badge":"BLOCKED","auto_install_policy":"block","auto_install_allowed":false,"blocked":true,"human_review_required":true,"recommended_action":"Do not auto-install. Inspect the source, dependencies, and permission surface first.","reasons":["Audit risk exceeds the requested agent policy","Audit classified this skill as risky","Audit risk risky exceeds max_risk=medium"]},"eval":{"version":"openagentskill-skill-eval-v1","status":"failed","score":71,"risk_level":"high","decision":{"recommendation":"do_not_auto_install","reason":"Audit score: Risky","auto_install_allowed":false,"policy":"block","human_review_required":true},"blockers":["Audit score: Risky","Agent safety gate: This skill should not be selected by an agent without explicit human security review.","Permission surface: secrets or environment access, filesystem or document access"],"warnings":["Trust score: Good trust signals with a few areas worth checking before rollout.","Audit risk risky exceeds max_risk=medium","High-risk permission hints: Secrets or environment access","Permission surface may require sandboxing","Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required","This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.","Quality score needs review","Permission surface needs review: secrets or environment access, filesystem or document access","Permission surface: secrets or environment access, filesystem or document access"],"validation_plan":["Inspect repository, README/SKILL.md, license, and recent commits before production use.","Install in an isolated workspace or sandbox with no production secrets available.","Run the smallest representative task and record files touched, commands run, network access, and outputs.","Compare the selected skill against at least one alternative when the eval status is review or failed.","Promote only after the agent reports a successful verification result and unresolved warnings are accepted."],"checks":[{"id":"task_fit","label":"Task fit","status":"pass","score":84,"required_for_auto_install":true,"detail":"Task wording matches this skill metadata.","evidence":["Evaluate gpu-clean-conversion before installing it in an agent workflow","automation","Browser automation workflows; Claude Code teams; builders willing to evaluate younger projects"]},{"id":"install_path","label":"Install path","status":"pass","score":92,"required_for_auto_install":true,"detail":"Install handoff is available.","evidence":["npx skills add google-ai-edge/litert-samples --skill gpu-clean-conversion"]},{"id":"install_safety","label":"Install command safety","status":"pass","score":92,"required_for_auto_install":true,"detail":"standard package or runtime install path","evidence":["npx skills add google-ai-edge/litert-samples --skill gpu-clean-conversion"]},{"id":"trust_score","label":"Trust score","status":"warn","score":76,"required_for_auto_install":true,"detail":"Good trust signals with a few areas worth checking before rollout.","evidence":["Strong shortlist","416 GitHub stars","Apache-2.0"]},{"id":"audit_score","label":"Audit score","status":"fail","score":81,"required_for_auto_install":true,"detail":"Risky","evidence":["Permission surface may require sandboxing"]},{"id":"agent_safety_gate","label":"Agent safety gate","status":"fail","score":49,"required_for_auto_install":true,"detail":"This skill should not be selected by an agent without explicit human security review.","evidence":["Do not auto-install. Inspect the source, dependencies, and permission surface first.","Audit risk exceeds the requested agent policy"]},{"id":"readme_skillmd_completeness","label":"README/SKILL.md completeness","status":"pass","score":86,"required_for_auto_install":false,"detail":"Metadata includes enough usage and workflow context","evidence":["Strong README/SKILL.md context"]},{"id":"license_clarity","label":"License clarity","status":"pass","score":86,"required_for_auto_install":true,"detail":"Apache-2.0","evidence":["Apache-2.0"]},{"id":"recent_maintenance","label":"Recent maintenance","status":"pass","score":100,"required_for_auto_install":false,"detail":"3d since push","evidence":["3d since push"]},{"id":"permission_surface","label":"Permission surface","status":"fail","score":46,"required_for_auto_install":true,"detail":"secrets or environment access, filesystem or document access","evidence":["Browser automation: medium","Network access: medium","Filesystem access: medium"]},{"id":"alternatives","label":"Alternatives available","status":"info","score":55,"required_for_auto_install":false,"detail":"No close alternatives were found in the current shortlist.","evidence":[]}],"endpoints":{"web":"https://www.openagentskill.com/skills/google-ai-edge-gpu-clean-conversion/evals","api":"/api/agent/evals?slug=google-ai-edge-gpu-clean-conversion","text":"/api/agent/evals?slug=google-ai-edge-gpu-clean-conversion&format=text"}},"agent_readable_metadata":{"version":"openagentskill-agent-metadata-v2","skill":{"slug":"google-ai-edge-gpu-clean-conversion","name":"gpu-clean-conversion","description":"Convert a PyTorch or Hugging Face model into a LiteRT model that runs fully on the GPU via the CompiledModel API with verified-correct output, and lay it out as a model recipe. Use when converting a new model, or when a converted model is rejected by the GPU, falls back to CPU, or returns wrong numbers on device.","category":"automation","url":"https://www.openagentskill.com/skills/google-ai-edge-gpu-clean-conversion","repository":"https://github.com/google-ai-edge/litert-samples/tree/main/skills/gpu-clean-conversion","github_repo":"google-ai-edge/litert-samples"},"suited_tasks":["Browser automation workflows","Claude Code teams","builders willing to evaluate younger projects","Navigate pages","Click and type safely","Check visual and DOM state","Search sources","Extract claims"],"suited_agents":["Codex","Claude Code","Cursor","OpenAgentSkill CLI","CLI"],"install":{"command":"npx skills add google-ai-edge/litert-samples --skill gpu-clean-conversion","ready":true,"targets":[{"id":"openagentskill-cli","label":"CLI","kind":"command","value":"npx --yes https://github.com/Leon-Drq/openagentskill/releases/download/cli-v0.3.0/openagentskill-0.3.0.tgz add google-ai-edge-gpu-clean-conversion"},{"id":"codex","label":"Codex","kind":"agent-prompt","value":"Install the \"gpu-clean-conversion\" agent skill from https://github.com/google-ai-edge/litert-samples/tree/main/skills/gpu-clean-conversion. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Convert a PyTorch or Hugging Face model into a LiteRT model that runs fully on the GPU via the CompiledModel API with verified-correct output, and lay it out as a model recipe. Use when converting a new model, or when a converted model is rejected by the GPU, falls back to CPU, or returns wrong numbers on device. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"google-ai-edge-gpu-clean-conversion\",\"task\":\"Install gpu-clean-conversion\",\"agent\":\"codex\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes."},{"id":"claude-code","label":"Claude Code","kind":"agent-prompt","value":"Add \"gpu-clean-conversion\" as a Claude Code skill from https://github.com/google-ai-edge/litert-samples/tree/main/skills/gpu-clean-conversion. Inspect the skill instructions, place the reusable skill files in the appropriate local skills location for this project, and report the activation steps. Skill purpose: Convert a PyTorch or Hugging Face model into a LiteRT model that runs fully on the GPU via the CompiledModel API with verified-correct output, and lay it out as a model recipe. Use when converting a new model, or when a converted model is rejected by the GPU, falls back to CPU, or returns wrong numbers on device. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"google-ai-edge-gpu-clean-conversion\",\"task\":\"Install gpu-clean-conversion\",\"agent\":\"claude-code\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes."},{"id":"cursor","label":"Cursor","kind":"agent-prompt","value":"Turn \"gpu-clean-conversion\" from https://github.com/google-ai-edge/litert-samples/tree/main/skills/gpu-clean-conversion into a reusable Cursor project rule or agent instruction. Preserve the core workflow, adapt paths to this repo, and keep the rule scoped to tasks where it is relevant. Skill purpose: Convert a PyTorch or Hugging Face model into a LiteRT model that runs fully on the GPU via the CompiledModel API with verified-correct output, and lay it out as a model recipe. Use when converting a new model, or when a converted model is rejected by the GPU, falls back to CPU, or returns wrong numbers on device. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"google-ai-edge-gpu-clean-conversion\",\"task\":\"Install gpu-clean-conversion\",\"agent\":\"cursor\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes."}],"handoff_url":"https://www.openagentskill.com/api/skills/google-ai-edge-gpu-clean-conversion/install","manifest_url":"https://www.openagentskill.com/api/registry/manifest/google-ai-edge-gpu-clean-conversion"},"trust":{"score":76,"label":"Strong shortlist","version":"trust-score-v4","install_policy":"sandbox_only","evidence":{"stars":"416 GitHub stars","repoActivity":"416 stars, 116 forks","lastPushed":"3d since push","license":"Apache-2.0","repository":"https://github.com/google-ai-edge/litert-samples/tree/main/skills/gpu-clean-conversion","install":"npx skills add google-ai-edge/litert-samples --skill gpu-clean-conversion","installSafety":"standard package or runtime install path","permissionSurface":"secrets or environment access, filesystem or document access","documentation":"Strong README/SKILL.md context","agentOutcomes":"No agent outcome data yet"},"outcome_evidence":{"total":0,"successes":0,"failures":0,"not_relevant":0,"success_rate":null,"recent_success_rate":null,"recent_failure_rate":null,"install_attempts":0,"install_success_rate":null,"risk_blocked":0,"setup_required":0,"avg_output_quality":null,"production_outcomes":0,"last_outcome_at":null,"label":"No agent outcome data yet"},"auto_install":{"allowed":false,"sandbox_required":true,"reason":"Human review or sandbox validation is required before automatic installation."},"best_for":["automation","agent-skill"],"known_risks":["This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.","Quality score needs review","Permission surface needs review: secrets or environment access, filesystem or document access","Permission surface: secrets or environment access, filesystem or document access"]},"agent_proven":{"version":"agent-proven-v1","score":0,"tier":"unproven","label":"Needs first agent run","summary":"No agent outcome reports yet. 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Inspect the source, dependencies, and permission surface first."},"quality":{"score":73,"label":"Strong"},"supply":{"track":"Research and knowledge work","scenario":"Research agents","maintenance":"3d since push","risk":"Risky"},"alternative_skills":[],"do_not_use_when":["teams that need a vendor-supported SLA","high-compliance environments without internal security review","No major risk signals from current metadata","Audit risk risky exceeds max_risk=medium","High-risk permission hints: Secrets or environment access","Permission surface may require sandboxing","Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required","This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval."],"agent_contract":{"task_input":"Use gpu-clean-conversion in an agent workflow","recommended_action":"Do not auto-install. Inspect the source, dependencies, and permission surface first.","install_policy":"block","minimum_review_before_use":["Trust: 76/100 Strong shortlist","Audit: 81/100 Risky","Safety: 49/100 Avoid automatic install","Review repository, license, install command, and permission surface before production use."],"expected_agent_output":{"selected_skill":"google-ai-edge-gpu-clean-conversion (gpu-clean-conversion)","install_command":"npx skills add google-ai-edge/litert-samples --skill gpu-clean-conversion","risk_summary":"Risky; Blocked for auto-install; Review before production","verification_result":"Report the smallest successful task, files touched, warnings, and any missing setup."}},"outcome_feedback":{"endpoint":"https://www.openagentskill.com/api/agent/outcome","method":"POST","requires_resolve_event_id":true,"event_id_source":"Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.","expected_outcomes":["success","failed","not_relevant","blocked_by_risk","setup_required"],"payload_template":{"event_id":"<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>","skill_slug":"google-ai-edge-gpu-clean-conversion","task":"Use gpu-clean-conversion in an agent workflow","agent":"codex","outcome":"success","install_used":true,"risk_blocked":false,"setup_required":false,"task_success":true,"output_quality":4,"error_type":null,"human_review_required":false,"workspace":"sandbox","time_to_useful_ms":120000,"notes":"Report the smallest successful task, setup friction, files touched, and risk notes."}},"endpoints":{"web":"https://www.openagentskill.com/skills/google-ai-edge-gpu-clean-conversion","api":"https://www.openagentskill.com/api/agent/skills/google-ai-edge-gpu-clean-conversion","audit":"https://www.openagentskill.com/skills/google-ai-edge-gpu-clean-conversion/audit","eval":"https://www.openagentskill.com/api/agent/evals?slug=google-ai-edge-gpu-clean-conversion&task=Use%20gpu-clean-conversion%20in%20an%20agent%20workflow&max_risk=medium","resolve":"https://www.openagentskill.com/api/agent/resolve?task=Use%20gpu-clean-conversion%20in%20an%20agent%20workflow&agent=codex&max_risk=medium","receipt":"https://www.openagentskill.com/api/agent/receipt?task=Use%20gpu-clean-conversion%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text","install":"https://www.openagentskill.com/api/skills/google-ai-edge-gpu-clean-conversion/install","manifest":"https://www.openagentskill.com/api/registry/manifest/google-ai-edge-gpu-clean-conversion"}},"machine_metadata":{"version":"openagentskill-agent-metadata-v2","skill":{"slug":"google-ai-edge-gpu-clean-conversion","name":"gpu-clean-conversion","description":"Convert a PyTorch or Hugging Face model into a LiteRT model that runs fully on the GPU via the CompiledModel API with verified-correct output, and lay it out as a model recipe. Use when converting a new model, or when a converted model is rejected by the GPU, falls back to CPU, or returns wrong numbers on device.","category":"automation","url":"https://www.openagentskill.com/skills/google-ai-edge-gpu-clean-conversion","repository":"https://github.com/google-ai-edge/litert-samples/tree/main/skills/gpu-clean-conversion","github_repo":"google-ai-edge/litert-samples"},"suited_tasks":["Browser automation workflows","Claude Code teams","builders willing to evaluate younger projects","Navigate pages","Click and type safely","Check visual and DOM state","Search sources","Extract claims"],"suited_agents":["Codex","Claude Code","Cursor","OpenAgentSkill CLI","CLI"],"install":{"command":"npx skills add google-ai-edge/litert-samples --skill gpu-clean-conversion","ready":true,"targets":[{"id":"openagentskill-cli","label":"CLI","kind":"command","value":"npx --yes https://github.com/Leon-Drq/openagentskill/releases/download/cli-v0.3.0/openagentskill-0.3.0.tgz add google-ai-edge-gpu-clean-conversion"},{"id":"codex","label":"Codex","kind":"agent-prompt","value":"Install the \"gpu-clean-conversion\" agent skill from https://github.com/google-ai-edge/litert-samples/tree/main/skills/gpu-clean-conversion. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Convert a PyTorch or Hugging Face model into a LiteRT model that runs fully on the GPU via the CompiledModel API with verified-correct output, and lay it out as a model recipe. Use when converting a new model, or when a converted model is rejected by the GPU, falls back to CPU, or returns wrong numbers on device. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"google-ai-edge-gpu-clean-conversion\",\"task\":\"Install gpu-clean-conversion\",\"agent\":\"codex\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes."},{"id":"claude-code","label":"Claude Code","kind":"agent-prompt","value":"Add \"gpu-clean-conversion\" as a Claude Code skill from https://github.com/google-ai-edge/litert-samples/tree/main/skills/gpu-clean-conversion. Inspect the skill instructions, place the reusable skill files in the appropriate local skills location for this project, and report the activation steps. Skill purpose: Convert a PyTorch or Hugging Face model into a LiteRT model that runs fully on the GPU via the CompiledModel API with verified-correct output, and lay it out as a model recipe. Use when converting a new model, or when a converted model is rejected by the GPU, falls back to CPU, or returns wrong numbers on device. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"google-ai-edge-gpu-clean-conversion\",\"task\":\"Install gpu-clean-conversion\",\"agent\":\"claude-code\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes."},{"id":"cursor","label":"Cursor","kind":"agent-prompt","value":"Turn \"gpu-clean-conversion\" from https://github.com/google-ai-edge/litert-samples/tree/main/skills/gpu-clean-conversion into a reusable Cursor project rule or agent instruction. Preserve the core workflow, adapt paths to this repo, and keep the rule scoped to tasks where it is relevant. Skill purpose: Convert a PyTorch or Hugging Face model into a LiteRT model that runs fully on the GPU via the CompiledModel API with verified-correct output, and lay it out as a model recipe. Use when converting a new model, or when a converted model is rejected by the GPU, falls back to CPU, or returns wrong numbers on device. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"google-ai-edge-gpu-clean-conversion\",\"task\":\"Install gpu-clean-conversion\",\"agent\":\"cursor\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes."}],"handoff_url":"https://www.openagentskill.com/api/skills/google-ai-edge-gpu-clean-conversion/install","manifest_url":"https://www.openagentskill.com/api/registry/manifest/google-ai-edge-gpu-clean-conversion"},"trust":{"score":76,"label":"Strong shortlist","version":"trust-score-v4","install_policy":"sandbox_only","evidence":{"stars":"416 GitHub stars","repoActivity":"416 stars, 116 forks","lastPushed":"3d since push","license":"Apache-2.0","repository":"https://github.com/google-ai-edge/litert-samples/tree/main/skills/gpu-clean-conversion","install":"npx skills add google-ai-edge/litert-samples --skill gpu-clean-conversion","installSafety":"standard package or runtime install path","permissionSurface":"secrets or environment access, filesystem or document access","documentation":"Strong README/SKILL.md context","agentOutcomes":"No agent outcome data yet"},"outcome_evidence":{"total":0,"successes":0,"failures":0,"not_relevant":0,"success_rate":null,"recent_success_rate":null,"recent_failure_rate":null,"install_attempts":0,"install_success_rate":null,"risk_blocked":0,"setup_required":0,"avg_output_quality":null,"production_outcomes":0,"last_outcome_at":null,"label":"No agent outcome data yet"},"auto_install":{"allowed":false,"sandbox_required":true,"reason":"Human review or sandbox validation is required before automatic installation."},"best_for":["automation","agent-skill"],"known_risks":["This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.","Quality score needs review","Permission surface needs review: secrets or environment access, filesystem or document access","Permission surface: secrets or environment access, filesystem or document access"]},"agent_proven":{"version":"agent-proven-v1","score":0,"tier":"unproven","label":"Needs first agent run","summary":"No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.","metrics":{"totalOutcomes":0,"successfulOutcomes":0,"failedOutcomes":0,"installAttempts":0,"installSuccessRate":null,"successRate":null,"recentSuccessRate":null,"recentFailureRate":null,"riskBlocked":0,"setupRequired":0,"notRelevant":0,"avgOutputQuality":null,"avgTimeToUsefulMs":null,"productionOutcomes":0,"humanReviewRequired":0,"uniqueAgents":0,"lastOutcomeAt":null},"signals":[],"penalties":["No real agent outcome evidence yet"]},"audit":{"score":81,"risk_level":"risky","risk_label":"Risky","warnings":["Permission surface may require sandboxing","Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required","This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.","Quality score needs review","Permission surface needs review: secrets or environment access, filesystem or document access","Permission surface: secrets or environment access, filesystem or document access"]},"safety_gate":{"tier":"blocked","label":"Blocked for auto-install","auto_install_policy":"block","auto_install_allowed":false,"human_review_required":true,"blocked":true,"recommended_action":"Do not auto-install. Inspect the source, dependencies, and permission surface first."},"quality":{"score":73,"label":"Strong"},"supply":{"track":"Research and knowledge work","scenario":"Research agents","maintenance":"3d since push","risk":"Risky"},"alternative_skills":[],"do_not_use_when":["teams that need a vendor-supported SLA","high-compliance environments without internal security review","No major risk signals from current metadata","Audit risk risky exceeds max_risk=medium","High-risk permission hints: Secrets or environment access","Permission surface may require sandboxing","Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required","This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval."],"agent_contract":{"task_input":"Use gpu-clean-conversion in an agent workflow","recommended_action":"Do not auto-install. Inspect the source, dependencies, and permission surface first.","install_policy":"block","minimum_review_before_use":["Trust: 76/100 Strong shortlist","Audit: 81/100 Risky","Safety: 49/100 Avoid automatic install","Review repository, license, install command, and permission surface before production use."],"expected_agent_output":{"selected_skill":"google-ai-edge-gpu-clean-conversion (gpu-clean-conversion)","install_command":"npx skills add google-ai-edge/litert-samples --skill gpu-clean-conversion","risk_summary":"Risky; Blocked for auto-install; Review before production","verification_result":"Report the smallest successful task, files touched, warnings, and any missing setup."}},"outcome_feedback":{"endpoint":"https://www.openagentskill.com/api/agent/outcome","method":"POST","requires_resolve_event_id":true,"event_id_source":"Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.","expected_outcomes":["success","failed","not_relevant","blocked_by_risk","setup_required"],"payload_template":{"event_id":"<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>","skill_slug":"google-ai-edge-gpu-clean-conversion","task":"Use gpu-clean-conversion in an agent workflow","agent":"codex","outcome":"success","install_used":true,"risk_blocked":false,"setup_required":false,"task_success":true,"output_quality":4,"error_type":null,"human_review_required":false,"workspace":"sandbox","time_to_useful_ms":120000,"notes":"Report the smallest successful task, setup friction, files touched, and risk notes."}},"endpoints":{"web":"https://www.openagentskill.com/skills/google-ai-edge-gpu-clean-conversion","api":"https://www.openagentskill.com/api/agent/skills/google-ai-edge-gpu-clean-conversion","audit":"https://www.openagentskill.com/skills/google-ai-edge-gpu-clean-conversion/audit","eval":"https://www.openagentskill.com/api/agent/evals?slug=google-ai-edge-gpu-clean-conversion&task=Use%20gpu-clean-conversion%20in%20an%20agent%20workflow&max_risk=medium","resolve":"https://www.openagentskill.com/api/agent/resolve?task=Use%20gpu-clean-conversion%20in%20an%20agent%20workflow&agent=codex&max_risk=medium","receipt":"https://www.openagentskill.com/api/agent/receipt?task=Use%20gpu-clean-conversion%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text","install":"https://www.openagentskill.com/api/skills/google-ai-edge-gpu-clean-conversion/install","manifest":"https://www.openagentskill.com/api/registry/manifest/google-ai-edge-gpu-clean-conversion"}},"supply_profile":{"track":{"slug":"research","label":"Research and knowledge work","shortLabel":"Research","description":"Deep research, source comparison, literature review, RAG, knowledge search, and reports."},"scenario":{"label":"Research agents","description":"I need my agent to research a topic, compare sources, and produce a concise report.","useCases":[{"slug":"browser-automation","title":"Browser automation"},{"slug":"research-agents","title":"Research agents"},{"slug":"workflow-automation","title":"Workflow automation"}]},"applicableAgents":["Claude Code","CLI","Codex","Cursor"],"install":{"ready":true,"command":"npx skills add google-ai-edge/litert-samples --skill gpu-clean-conversion","primaryTarget":"CLI","targetCount":4},"githubQuality":{"stars":416,"starsLabel":"416","forks":116,"license":"Apache-2.0","qualityScore":73,"trustScore":76,"auditScore":81},"maintenance":{"status":"fresh","label":"3d since push","daysSincePush":3,"lastPushedAt":"2026-09-03T21:51:36+00:00"},"risk":{"level":"risky","label":"Risky","requiresReview":true,"notes":["Permission surface may require sandboxing","Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required","This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.","Quality score needs review","Permission surface needs review: secrets or environment access, filesystem or document access"]},"coverageTags":["Research","Research agents","automation","agent-skill"]},"audit":{"audit_score":81,"risk_level":"risky","risk_label":"Risky","quality_score":73,"trust_score":76,"maintenance_score":100,"security_score":80,"install_score":92,"warnings":["Permission surface may require sandboxing","Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required","This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.","Quality score needs review","Permission surface needs review: secrets or environment access, filesystem or document access","Permission surface: secrets or environment access, filesystem or document access"]},"quality_signals":{"model":"v2","star_score":18.34,"usage_score":0,"review_score":5.55,"metadata_score":3,"freshness_score":15},"platforms":["Claude Code"],"use_cases":[{"slug":"browser-automation","title":"Browser automation","url":"https://www.openagentskill.com/use-cases/browser-automation"},{"slug":"research-agents","title":"Research agents","url":"https://www.openagentskill.com/use-cases/research-agents"},{"slug":"workflow-automation","title":"Workflow automation","url":"https://www.openagentskill.com/use-cases/workflow-automation"},{"slug":"local-desktop","title":"Local desktop","url":"https://www.openagentskill.com/use-cases/local-desktop"}],"stacks":[{"slug":"browser-qa-agent","title":"Browser QA agent","url":"https://www.openagentskill.com/collections/browser-qa-agent"},{"slug":"research-report-agent","title":"Research report agent","url":"https://www.openagentskill.com/collections/research-report-agent"},{"slug":"content-growth-agent","title":"Content growth agent","url":"https://www.openagentskill.com/collections/content-growth-agent"}],"install":"npx skills add google-ai-edge/litert-samples --skill gpu-clean-conversion","install_targets":[{"id":"openagentskill-cli","label":"CLI","title":"OpenAgentSkill CLI","kind":"command","value":"npx --yes https://github.com/Leon-Drq/openagentskill/releases/download/cli-v0.3.0/openagentskill-0.3.0.tgz add google-ai-edge-gpu-clean-conversion","description":"Resolve policy, run the source installer safely, and report a verified install receipt.","copyLabel":"Copy command"},{"id":"codex","label":"Codex","title":"Codex install prompt","kind":"agent-prompt","value":"Install the \"gpu-clean-conversion\" agent skill from https://github.com/google-ai-edge/litert-samples/tree/main/skills/gpu-clean-conversion. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Convert a PyTorch or Hugging Face model into a LiteRT model that runs fully on the GPU via the CompiledModel API with verified-correct output, and lay it out as a model recipe. Use when converting a new model, or when a converted model is rejected by the GPU, falls back to CPU, or returns wrong numbers on device. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"google-ai-edge-gpu-clean-conversion\",\"task\":\"Install gpu-clean-conversion\",\"agent\":\"codex\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.","description":"Give Codex a repo-aware install prompt when the skill is not available through a local CLI.","copyLabel":"Copy prompt"},{"id":"claude-code","label":"Claude Code","title":"Claude Code skill prompt","kind":"agent-prompt","value":"Add \"gpu-clean-conversion\" as a Claude Code skill from https://github.com/google-ai-edge/litert-samples/tree/main/skills/gpu-clean-conversion. Inspect the skill instructions, place the reusable skill files in the appropriate local skills location for this project, and report the activation steps. Skill purpose: Convert a PyTorch or Hugging Face model into a LiteRT model that runs fully on the GPU via the CompiledModel API with verified-correct output, and lay it out as a model recipe. Use when converting a new model, or when a converted model is rejected by the GPU, falls back to CPU, or returns wrong numbers on device. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"google-ai-edge-gpu-clean-conversion\",\"task\":\"Install gpu-clean-conversion\",\"agent\":\"claude-code\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.","description":"Use this prompt to ask Claude Code to add the skill and explain the local activation steps.","copyLabel":"Copy prompt"},{"id":"cursor","label":"Cursor","title":"Cursor rule prompt","kind":"agent-prompt","value":"Turn \"gpu-clean-conversion\" from https://github.com/google-ai-edge/litert-samples/tree/main/skills/gpu-clean-conversion into a reusable Cursor project rule or agent instruction. Preserve the core workflow, adapt paths to this repo, and keep the rule scoped to tasks where it is relevant. Skill purpose: Convert a PyTorch or Hugging Face model into a LiteRT model that runs fully on the GPU via the CompiledModel API with verified-correct output, and lay it out as a model recipe. Use when converting a new model, or when a converted model is rejected by the GPU, falls back to CPU, or returns wrong numbers on device. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"google-ai-edge-gpu-clean-conversion\",\"task\":\"Install gpu-clean-conversion\",\"agent\":\"cursor\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.","description":"Use this when installing as Cursor project rules or reusable agent instructions.","copyLabel":"Copy prompt"}],"repository":"https://github.com/google-ai-edge/litert-samples/tree/main/skills/gpu-clean-conversion","github_repo":"google-ai-edge/litert-samples","version":"1.0.0","license":"Apache-2.0","urls":{"web":"https://www.openagentskill.com/skills/google-ai-edge-gpu-clean-conversion","repository":"https://github.com/google-ai-edge/litert-samples/tree/main/skills/gpu-clean-conversion","api":"/api/agent/skills/google-ai-edge-gpu-clean-conversion","install_api":"/api/skills/google-ai-edge-gpu-clean-conversion/install"},"meta":{"created_at":"2026-08-26T20:35:44.359055+00:00","updated_at":"2026-09-04T03:47:54.096287+00:00","agent_friendly":true}}