SKILLEMALL.ai

CC multi-agent-memory

多 agent 共享记忆与项目协作架构。支持项目状态隔离、知识库共享、跨项目搜索、版本控制、里程碑跟踪、周报和交接文档。适用于多个 agent 协作开发多个项目的场景。

Not recommendedcritical or high security findings
ClawHub Agent Skills author: brucey0017-cloud v0.1.0 MIT-0 7 files · 4 scripts body ≈ 1 420 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 55/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedurePostgreSQLAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
C
74/100
safety, quality, tests
Safety 60%
82
Quality 40%
61
Run on models
none yet
Process rating
C
55/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

What is at stake

The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.

Exfiltration
If you install

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".

For the author

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

  1. Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
  2. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
For the model run — optional
  • 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

  • high Exfiltration intent-browser-credential-store skill-card.md:14
    Accesses a browser credential / cookie store
    Developers and agent operators use this skill to coordinate multiple agents across projects by maintaining project-local state, shared knowledge, recurring status checks, weekly summaries, milestones,

Files scanned: 7. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 55/100

  • 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
  • 20When it triggers. No condition that starts the skill
  • 100Tools and files. No external tools needed
  • 100Steps. 36 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1420 tokens
  • 100Running it twice. No mutating operations
  • 100Progress reporting. Reports progress
  • low 11 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)
  • +3Description length 84: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • -5TODO / placeholder text left in the skill
  • -32 of 4 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +4Structure: 30 headings
  • +3Step-by-step instructions: 36 items
  • +4Has examples (12 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 61.

External checks

ClawHub: suspicious
This is a coherent multi-agent project-memory tool, but it needs Review because it persists and searches cross-project data while leaving important scoping and path-safety controls undefined.
LLM: suspicious (high) · VirusTotal: · 29 May 2026