SKILLEMALL.ai

DF hermesclawzero-auto-memory

Unified memory platform for Hermes, OpenClaw and AI agents — persistent long-term memory, cross-skill sharing, automated capture, governance rules, optimization, multi-tenant dashboard with pgvector search.

Not recommendedcritical or high security findings · low grade D
ClawHub Agent Skills author: Hansjoerg Hofer v3.0.2 MIT-0 14 files · 4 scripts body ≈ 2 350 tokens Open the sourceclawhub.ai analyzed 22 h ago

Unified memory platform for Hermes, OpenClaw and AI agents — persistent long-term memory, cross-skill sharing, automated capture, governance rules…

As a process F 38/100 · Will not run — References files that are not bundled: install_via_agent.md, CONTRIBUTING.md

ProcedureGitHubPostgreSQLAI and agentsData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
D
58/100
safety, quality, tests
Safety 60%
64
Quality 40%
49
Run on models
none yet
Process rating
F
38/100
Will not run
References files that are not bundled: install_via_agent.md, CONTRIBUTING.md
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
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.

Secrets in code
If you install

The files contain someone else's key or token. If it is live, your agent will call third-party services under a stranger's identity; if it was revoked, the skill's scripts simply fail. Such a key often arrives with the author's whole workspace, personal data included.

For the author

The key is visible to everyone who downloaded the skill and has likely been copied by catalog-scanning bots already. Revoke it now, check bills and access logs, then reissue.

Concealment
If you install

The skill tells the agent to hide things from you: not to show errors, not to mention actions, to report differently from what was done. You lose the ability to see what the agent really did.

For the author

Transparency beats a smooth answer. If the goal is to hide technical noise, ask the agent to "summarise briefly", not to "not mention".

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.
  3. The text references files that are not there: add them or drop the references.
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 · 2

  • high Secrets in code meta-credential-files secrets.yaml
    Credential / dotenv files bundled with the skill (1)
    secrets.yaml
  • high Concealment en-hide-from-user SKILL.md:178
    Instruction to hide actions from the user
    1. Silently run: `python C:\dev\HermesClawZero-ConfigSidecar\memory.py search "user profile preferences current project state" 5`

Files scanned: 14. 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")
  • warning missing-ref reference to a missing file: install_via_agent.md
  • warning missing-ref reference to a missing file: CONTRIBUTING.md
  • note frontmatter-key unknown frontmatter key "enforce"
  • note frontmatter-key unknown frontmatter key "categories"
  • note frontmatter-key unknown frontmatter key "topics"

Process rating: all ten parameters 38/100

Will not run. References files that are not bundled: install_via_agent.md, CONTRIBUTING.md
  • 0Tools and files. 2 referenced file(s) missing: install_via_agent.md, CONTRIBUTING.md
  • 0Result and completion. Does not say what the result is
  • 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. 5 mutating operations with no state check
  • 55Failures and branches. 1 branches
  • 100Steps. 22 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2350 tokens
  • low 10 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)
  • +3Output format is not stated: the model decides each time
  • -2localhost URLs: will not work for another user
  • -212 emoji in the instructions: noise for the model
  • -41 reference files, but SKILL.md never points to them: the model will not open them
  • -33 of 6 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 206: enough signal without eating the budget
  • +4Structure: 16 headings
  • +3Step-by-step instructions: 22 items
  • +4Has examples (8 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 49.

External checks

ClawHub: suspicious
This is a local memory skill, but it asks agents to silently retain and reuse conversation data and creates some unclear background and policy-control behavior that users should review carefully.
LLM: suspicious (high) · VirusTotal: · 14 Jul 2026