Borrowing it
Nothing to install: this file belongs to Tele-AI/TeleFuser. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Tele-AI/TeleFuser/main/.claude/skills/optimize-pipeline/SKILL.mdgit clone --depth 1 https://github.com/Tele-AI/TeleFuserWrote 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.
[](https://agentmods.dev/skills/tele-ai/telefuser/optimize-pipeline)<a href="https://agentmods.dev/skills/tele-ai/telefuser/optimize-pipeline"><img src="https://agentmods.dev/badge/skills/tele-ai/telefuser/optimize-pipeline.svg" alt="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00064 | $0.00733 |
| Opus 5 | $0.00032 | $0.00367 |
| Sonnet 5 | $0.00013 | $0.00147 |
| Haiku 4.5 | $0.00006 | $0.00073 |
Grade A, and why
optimize-pipeline 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 7d 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.
How it starts
The opening of the file, as written. The whole thing — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Optimize a Pipeline
Require a correct, reproducible baseline before changing performance behavior. Read current implementations and canonical docs rather than relying on hard-coded speedup or memory estimates.
Define the target
Record the workload, model/checkpoint, task, shape, frame count, step count, dtype, attention backend, devices, warmup, measured latency/throughput, peak memory, and quality or parity criterion. Distinguish latency, throughput, capacity, and output cadence; they require different choices.
If no baseline exists, profile first. Use .claude/skills/profile-pipeline/SKILL.md and docs/en/profiler.md.
Reuse supported mechanisms
Inspect the closest model and pipeline before choosing an optimization. Consult the relevant current docs:
docs/en/ops.mdanddocs/en/attention.mddocs/en/parallel.mddocs/en/offload.mddocs/en/feature_cache.mddocs/en/torch_compile_compatibility.mddocs/en/configuration.md
Apply these constraints:
- Route model operations through
telefuser.ops; do not import Triton kernels directly frommodels/. - Preserve exact semantics when replacing an op: layout, normalization, RoPE, masks, causal behavior, scale, dtype, and numerical tolerances must match.
- Use current
AttentionConfig,ModelRuntimeConfig,ParallelConfig,FeatureCacheConfig,CompileConfig,QuantConfig, andOffloadConfigAPIs as implemented in the repository. - Confirm that the target model and stage implement the selected parallel or optimization path. A config field existing does not prove model support.
- Treat sparse attention, feature caching, quantization, distillation, and approximate computation as behavior or quality changes; require explicit user agreement and parity/quality evidence.
- Do not add a new public interface, configuration system, environment variable, loader, or parallel abstraction as part of optimization without first demonstrating a missing extension point and obtaining approval.
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.
- 7d ago First seen · 59 lines · 64 tokens per session scan A b10ab3443831
optimize-pipeline is a skill published in the GitHub repository Tele-AI/TeleFuser (25 stars, last pushed 2d ago), licensed Apache-2.0. It adds 64 tokens to every session and 733 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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