CD x0x
Secure computer-to-computer networking for AI agents — gossip broadcast, direct messaging, CRDTs, group encryption. Post-quantum encrypted, NAT-traversing. Everything you need to build any decentralized application.
Secure computer-to-computer networking for AI agents — gossip broadcast, direct messaging, CRDTs, group encryption.
As a process D 41/100 · Unfinished process — References files that are not bundled: scripts/install.py
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.
- 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 · 5
✓ No critical or high findings
Medium and low: 5
-
low Exfiltration
net-credential-useSKILL.md:175Credential used in a network call (verify the destination is the intended service) (destination host is a configured variable; documentation of a security skill)curl -s -H "Authorization: Bearer $TOKEN" "http://$API/status"
variable hostsecurity skill -
low Exfiltration
net-credential-useSKILL.md:187Credential used in a network call (verify the destination is the intended service) (destination host is a configured variable; documentation of a security skill)curl -X POST "http://$API/subscribe" -H "Authorization: Bearer $TOKEN" -H "Content-Type: application/json" -d '{"topic":"hello-world"}'variable hostsecurity skill -
low Exfiltration
net-credential-useSKILL.md:188Credential used in a network call (verify the destination is the intended service) (destination host is a configured variable; documentation of a security skill)curl -X POST "http://$API/publish" -H "Authorization: Bearer $TOKEN" -H "Content-Type: application/json" \
variable hostsecurity skill -
low Exfiltration
net-credential-useSKILL.md:190Credential used in a network call (verify the destination is the intended service) (destination host is a configured variable; documentation of a security skill)curl -N -H "Authorization: Bearer $TOKEN" "http://$API/events" # SSE; fields nested under "data"
variable hostsecurity skill -
low Exfiltration
net-credential-useSKILL.md:214Credential used in a network call (verify the destination is the intended service) (destination host is a configured variable; documentation of a security skill)curl -X PUT "http://$API/profile" -H "Authorization: Bearer $TOKEN" -H "Content-Type: application/json" \
variable hostsecurity skill
Files scanned: 2. 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 ≈ 6866 tokens (recommended < 5000); move details to references/ - warning
missing-refreference to a missing file: scripts/install.py - note
frontmatter-keyunknown frontmatter key "repository" - note
frontmatter-keyunknown frontmatter key "homepage" - note
frontmatter-keyunknown frontmatter key "keywords"
Process rating: all ten parameters 41/100
- 0Tools and files. 1 referenced file(s) missing: scripts/install.py
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 27 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
- 70Execution cost. Instruction body is 6866 tokens
- 100Steps. 18 steps
- 100Consistency. Name and required fields are in place
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 13 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (5 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
- +2Single-language instructions
- +3Description length 215: enough signal without eating the budget
- +4Structure: 25 headings
- +3Step-by-step instructions: 18 items
- +4Has examples (17 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 54.