BD proof-engine
Transforms every result [PRINCIPAL_NAME] achieves into deployable proof across all business domains. Captures P&L, agent performance, funnel revenue, testimonials, milestones, and media mentions. Converts raw data into compelling stories via the Storytelling Engine. Generates proof-based content ready for all platforms. Tracks a multi-channel financial dashboard. Scans high-potential business opportunities for 2026. Deploys proof automatically into funnels, brand, outreach, and VSL scripts. The credibility engine that makes everything else convert.
As a process D 49/100 · Unfinished process — weak spots: steps, when it triggers, inputs and preconditions
What is at stake
Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
The instructions or scripts send data out: environment variables, keys, file contents, chat history. You may never notice, because the agent performs the upload as "part of the task".
If the upload is not needed for the task, remove it: catalogs flag such skills and delist them. If it is needed, name the destination explicitly, say what leaves the machine, and give the user a switch.
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 · 1
✓ No critical or high findings
Medium and low: 1
-
medium Exfiltration
exfil-webhook-urlproof_manager.py:99Webhook / callback URL commonly used for exfiltration (verify the destination) (quoted — discussed, not commanded)f"https://api.telegram.org/bot{token}/sendMessage",quoted
Files scanned: 8. 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 ≈ 5252 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 49/100
- 0Steps. Prose only: no discrete steps
- 0Inputs and preconditions. Does not say what the process needs to start
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 3 mutating operations with no state check
- 50Failures and branches. 0 branches, has a failure section
- 60Result and completion. Output format stated, no completion criterion
- 70Execution cost. Instruction body is 5252 tokens
- 100Tools and files. No external tools needed
- 100Consistency. Name and required fields are in place
- 100Progress reporting. Reports progress
- low 13 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
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- +3No numbered steps or checklist
- +2Single-language instructions
- +3Description length 554: enough signal without eating the budget
- +4Structure: 37 headings
- +3Output format is stated explicitly
- +4Has examples (28 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 63.