BC auto-memory
Indestructible agent memory — permanently stored, never lost. Save decisions, identity, and context as a memory chain on the Autonomys Network. Rebuild your full history from a single CID, even after total state loss.
Indestructible agent memory — permanently stored, never lost.
As a process C 56/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
The same skill appears in 2 more places: ClawHub, ClawHub
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 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
- For Hermes the description must be one sentence under 60 characters; move the conditions to a "When to Use" section.
- 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 · 3
✓ No critical or high findings
Medium and low: 3
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medium Broad scope
meta-broad-allowed-toolsSKILL.md:1Broad tool permissions pre-approved: Bash(curl:*)allowed-tools: Bash(curl:*) Bash(jq:*) Bash(file:*) Read Write
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medium Broad scope
meta-requests-env-secretSKILL.md:1Skill asks the runtime to inject credential env vars into its sandbox: AUTO_DRIVE_API_KEY — verify each one is needed for the stated purposerequired_environment_variables: AUTO_DRIVE_API_KEY
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low Secrets in code
secret-high-entropy-tokenreferences/memory-chain.md:75High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)The contract is at `0x51…A3c` on Autonomys Mainnet (Chain ID 870). It provides:
quoted
Files scanned: 13. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-long-hermesdescription is 217 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period) - note
frontmatter-keyunknown frontmatter key "required_environment_variables"
Process rating: all ten parameters 56/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 5 mutating operations with no state check
- 60Consistency. The Hermes dialect needs category and tags
- 70Failures and branches. 4 branches
- 100Tools and files. Tools declared in frontmatter
- 100Steps. 32 steps
- 100Execution cost. Instruction body is 2746 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
- -43 reference files, but SKILL.md never points to them: the model will not open them
- -32 of 8 scripts are never mentioned in SKILL.md
- +2Single-language instructions
- +3Description length 217: enough signal without eating the budget
- +4Structure: 15 headings
- +3Step-by-step instructions: 32 items
- +4Has examples (11 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 71.