BB fly-io-deployer
Deploy and operate Node, Python, Go, Rust, Elixir, and Docker apps on Fly.io with production-grade fly.toml authoring, Machines API orchestration, region selection (latency vs sovereignty vs egress), Fly Postgres clustering, LiteFS for SQLite replication, Upstash Redis bindings, Tigris object storage, persistent volumes, WireGuard private networking with 6PN, secrets via flyctl, blue/green deploys via auto-stopping machines, scale-to-zero strategies, scheduled scaling, preview deploys per PR, multi-region replicas, hot-config reload, machine SSH, log shipping to Better Stack/Axiom/Datadog/Logtail, and aggressive cost tuning. Triggers on "fly.io", "flyctl", "fly machines", "fly.toml", "fly postgres", "litefs", "tigris", "upstash on fly", "fly deploy", "migrate from heroku", "migrate from render", "migrate from railway", "scale to zero", "fly regions", "fly volumes", "fly wireguard", "6pn", "fly secrets".
Deploy and operate Node, Python, Go, Rust, Elixir, and Docker apps on Fly.io with production-grade fly.toml authoring, Machines API orchestration, region…
As a process B 69/100 · Nearly there — weak spots: result and completion, running it twice
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- 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
description-no-whendescription does not say WHEN to use the skill (no "use when") - warning
body-longSKILL.md body ≈ 5988 tokens (recommended < 5000); move details to references/ - note
edit-residuethe text marks something as outdated (lines 480): check that old rules are not kept next to new ones — the full check reads the text for contradictions
Process rating: all ten parameters 69/100
- 30Running it twice. 39 mutating operations with no state check
- 40Result and completion. Does not say what the result is
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (python, node) that frontmatter does not declare
- 70Inputs and preconditions. Inputs and preconditions are listed
- 70Execution cost. Instruction body is 5988 tokens
- 85Steps. 99 steps, 1 vague phrases
- 100When it triggers. States when to use and when not to
- 100Consistency. Name and required fields are in place
- 100Progress reporting. Reports progress
- low 18 top-level sections: this looks like several domains in one skill
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
- +4Description does not say when NOT to use the skill (false activations)
- +3Description length 916: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +5Description quotes 17 example trigger phrases
- +4Structure: 23 headings
- +3Step-by-step instructions: 99 items
- +4Has examples (14 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 64.