BF speculative-decoding
Accelerate LLM inference using speculative decoding, Medusa multiple heads, and lookahead decoding techniques. Use when optimizing inference speed (1.5-3.6× speedup), reducing latency for real-time applications, or deploying models with limited compute. Covers draft models, tree-based attention, Jacobi iteration, parallel token generation, and production deployment strategies.
Accelerate LLM inference using speculative decoding, Medusa multiple heads, and lookahead decoding techniques.
As a process F 28/100 · No process to follow — References files that are not bundled: references/draft_model.md
The same skill appears in 2 more places: RA-Skills, AI-Research-SKILLs
How to improve
- The text references files that are not there: add them or drop the references.
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 0
✓ No critical or high findings
Files scanned: 3. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
missing-refreference to a missing file: references/draft_model.md - note
frontmatter-keyunknown frontmatter key "dependencies"
Process rating: all ten parameters 28/100
- 0Tools and files. 1 referenced file(s) missing: references/draft_model.md
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 2 mutating operations with no state check
- 40Consistency. Frontmatter name (speculative-decoding) differs from the folder (emerging-techniques-speculative-decoding)
- 100Steps. 37 steps
- 100Execution cost. Instruction body is 3329 tokens
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- +2Single-language instructions
- +3Description length 379: enough signal without eating the budget
- +4Structure: 22 headings
- +3Step-by-step instructions: 37 items
- +4Has examples (17 code blocks)
- +4Reference files are cited in the instructions (2 of 2)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.