CC moltcare-open
Install and configure the MoltCare Agent Framework - a four-layer configuration system (SOUL/AGENTS/USER/MEMORY) with three-layer trigger architecture (Exact + Semantic + Agent Evaluation) and PUA problem-solving framework. Use when the user wants to set up or configure OpenClaw Agent with structured personality, intelligent memory triggers, proactive problem-solving, and multi-expert decision modes. Triggers on phrases like 'install moltcare', 'setup agent framework', 'configure openclaw personality', 'set up memory triggers', or when the user needs a complete Agent configuration system.
As a process C 64/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice
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.
The skill contains commands that delete files, rewrite disks or execute code fetched from the network. The agent may run them without asking if it believes the instructions require it.
Replace destructive commands with safe equivalents that ask for confirmation, scope them to one folder, and stop piping curl into a shell: pin a version and a checksum.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.
Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.
How to improve
- 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.
- 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 · 10
-
high Dangerous commands
cmd-persistenceassets/CONFIG_CHECKLIST.md:114Persistence mechanism (cron / launchd / scheduled task / autorun registry)echo "0 3 * * 1 cd ~/.openclaw/workspace && echo '检查token优化' >> ~/.openclaw/workspace/.audit-trigger 2>&1" | crontab -
-
high Dangerous commands
cmd-persistencescripts/install.sh:57Persistence mechanism (cron / launchd / scheduled task / autorun registry)(crontab -l 2>/dev/null; echo "0 3 * * 1 cd ${WORKSPACE} && echo '检查token优化' >> ${WORKSPACE}/.audit-trigger 2>&1") | crontab -
Medium and low: 8
-
medium Broad scope
meta-agent-memory-dumpassets/HEARTBEAT.mdAgent memory / workspace files bundled with the skill (5) — likely a workspace dump with personal data or tokensassets/HEARTBEAT.md, assets/IDENTITY.md, assets/MEMORY.md, assets/SOUL.md, assets/USER.md
-
medium Dangerous commands
cmd-pipe-to-shell-known-hostREADME.md:14Pipe-to-shell installer from a well-known host (still executes remote code)curl -fsSL https://raw.githubusercontent.com/useens/moltcare-open/master/skill/scripts/install.sh | bash
-
low Dangerous commands
cmd-cron-mentionassets/CONFIG_CHECKLIST.md:107Mentions editing / listing crontabcrontab -l | grep "检查token优化"
-
low Dangerous commands
cmd-cron-mentionassets/TOOLS.md:106Mentions editing / listing crontabcrontab -e
-
low Dangerous commands
cmd-cron-mentionassets/TOOLS.md:107Mentions editing / listing crontabcrontab -l
-
low Dangerous commands
cmd-cron-mentionscripts/install.sh:56Mentions editing / listing crontabif ! crontab -l 2>/dev/null | grep -q "检查token优化"; then
-
low Dangerous commands
cmd-cron-mentionscripts/install.sh:57Mentions editing / listing crontab(crontab -l 2>/dev/null; echo "0 3 * * 1 cd ${WORKSPACE} && echo '检查token优化' >> ${WORKSPACE}/.audit-trigger 2>&1") | crontab - -
low Dangerous commands
cmd-cron-mentionSKILL.md:228Mentions editing / listing crontabcrontab -e
Files scanned: 18. 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 64/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 30Running it twice. 3 mutating operations with no state check
- 55Failures and branches. 1 branches
- 70When it triggers. States when to use, but not when not to
- 100Tools and files. No external tools needed
- 100Steps. 37 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2447 tokens
- 100Progress reporting. Reports progress
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
- -223 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +3Description length 595: enough signal without eating the budget
- +4Structure: 22 headings
- +3Step-by-step instructions: 37 items
- +4Has examples (14 code blocks)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.