AB flashrev-mailer
Use this skill when an AI agent needs to plan, build, commit, monitor, or follow up on FlashRev-powered email outreach via the flashrev-mailer npm CLI (v2.0+). Triggers on requests involving cold email, outreach campaigns, multi-step follow-up sequences, AI auto-reply, prospect reply triage, or mailbox-pool drip sending. The CLI delegates send timing and reply tracking to the FlashRev backend sequence engine; live sends require explicit per-batch user approval; AI auto-reply prompts must be shown verbatim and approved before enabling. Agents should invoke with FLASHREV_MAILER_AI_MODE=1 (or --ai-mode) so all list/view outputs and errors are JSON-structured.
As a process B 69/100 · Nearly there — weak spots: result and completion, running it twice
The same skill appears in 1 more place: ClawHub
How to improve
- 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: 5. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 69/100
- 0Result and completion. Does not say what the result is
- 30Running it twice. 41 mutating operations with no state check
- 60Tools and files. Uses tools (bash, node) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Steps. 71 steps
- 100Failures and branches. 5 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3859 tokens
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 12 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (3 tags): a typed call is more reliable
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
- +1No license
- +2Single-language instructions
- +3Description length 664: enough signal without eating the budget
- +4Structure: 14 headings
- +3Step-by-step instructions: 71 items
- +4Has examples (6 code blocks)
- +4Reference files are cited in the instructions (1 of 1)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.