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BF hermes-attestation-guardian

Hermes-only runtime security attestation and drift detection skill for operator-managed Hermes infrastructure.

ClawHub Agent Skills author: davida-ps v0.1.7 MIT-0 25 files · 1 script body ≈ 2 643 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

Hermes-only runtime security attestation and drift detection skill for operator-managed Hermes infrastructure.

As a process F 28/100 · Will not run — References files that are not bundled: scripts/*.mjs

AnalyzerInfrastructureWriting and documentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
B
85/100
safety, quality, tests
Safety 60%
99
Quality 40%
64
Run on models
none yet
Process rating
F
28/100
Will not run
References files that are not bundled: scripts/*.mjs
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
This is a copy of a skill from another catalog; the rating counts the canonical one: hermes-attestation-guardian (ClawHub)

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. 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 · 1

✓ No critical or high findings

Medium and low: 1
  • low Secrets in code secret-high-entropy-token lib/feed.mjs:8
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    MCow…XJX+GYGv…m6A=
    quoted

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")
  • warning missing-ref reference to a missing file: scripts/*.mjs
  • note frontmatter-key unknown frontmatter key "homepage"
  • note frontmatter-key unknown frontmatter key "hermes"

Process rating: all ten parameters 28/100

Will not run. References files that are not bundled: scripts/*.mjs
  • 0Tools and files. 1 referenced file(s) missing: scripts/*.mjs
  • 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
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 3 mutating operations with no state check
  • 40Consistency. Frontmatter name (hermes-attestation-guardian) differs from the folder (clawsec-hermes-attestation-guardian)
  • 100Steps. 49 steps
  • 100Execution cost. Instruction body is 2643 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)
  • +3Description length 110: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +4Structure: 11 headings
  • +3Step-by-step instructions: 49 items
  • +4Has examples (3 code blocks)
  • +3All 7 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 64.

External checks

ClawHub: clean
This is a disclosed Hermes security attestation skill with opt-in scheduling and local advisory feed verification, not evidence of hidden or malicious behavior.
LLM: benign (high) · VirusTotal: · 13 Jul 2026