CC perstate
A git-native remote knowledge graph that provides persistent state for agents and individuals. /perstate checks status, /perstate save writes insights, /perstate search recalls, /perstate fork copies branches, /perstate switch changes branches, /perstate info shows stats, /perstate view visualizes, /perstate prune cleans up. Trigger: user says "persist" "remember" "recall" "memory" "save insight" or types /perstate. Auto-extracts insights, deduplicates and merges, multi-hop queries, graph visualization.
A git-native remote knowledge graph that provides persistent state for agents and individuals.
As a process C 55/100 · Has gaps — weak spots: result and completion, inputs and preconditions
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 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
- 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 · 1
-
high Exfiltration
intent-browser-credential-storeskill-card.md:14Accesses a browser credential / cookie storeDevelopers, agent operators, and individuals use Perstate to maintain persistent memory as a git-backed markdown knowledge graph. It supports saving conversation-derived insights, searching and traver
Files scanned: 14. 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 55/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 50When it triggers. No condition that starts the skill
- 55Failures and branches. 1 branches
- 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
- 85Steps. 22 steps, 1 vague phrases
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3620 tokens
- 100Running it twice. Mutating operations check current state
- 100Progress reporting. Reports progress
- low The response is described with custom markup (21 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
- +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
- +5Description quotes 5 example trigger phrases
- +3Description length 508: enough signal without eating the budget
- +4Structure: 24 headings
- +3Step-by-step instructions: 22 items
- +4Has examples (12 code blocks)
- +3All 10 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 92.