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BC openclaw-essesseff

Interact with the essesseff DevOps platform — call the essesseff Public API (templates, organizations, apps, deployments, images, image lifecycle, environments, retention policies, packages) and automate app creation and Argo CD setup using the essesseff onboarding utility. Use when the user wants to create essesseff apps, manage deployments, promote images through the DEV→QA→STAGING→PROD lifecycle, configure Argo CD environments, manage retention policies, or run the essesseff-onboard.sh script.

ClawHub Agent Skills author: adamdurst v1.0.2 MIT-0 17 files body ≈ 1 607 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process C 62/100 · Has gaps — weak spots: result and completion, running it twice, progress reporting

IntegrationInfrastructuretype 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
86/100
safety, quality, tests
Safety 60%
85
Quality 40%
88
Run on models
none yet
Process rating
C
62/100
Has gaps
Result and completion w 14
0
Progress reporting w 2
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Secrets in code medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

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 If it is live, your agent will call third-party services under a stranger's identity; if it was revoked, the skill's scripts simply fail.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.

Exfiltration medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

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".

For the author

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

    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 · 3

    ✓ No critical or high findings

    Medium and low: 3
    • medium Secrets in code secret-github-token essesseff-example.txt:10
      GitHub token (detector / deny-list definition; test fixture / example file)
      # GITHUB_ORG_ADMIN_PAT=ghp_yourOrgAdminPersonalAccessToken
      detectorfixture
    • medium Exfiltration net-credential-use README.md:70
      Credential used in a network call (verify the destination is the intended service)
      curl -H "X-API-Key: $ESSESSEFF_API_KEY" "$BASE/global/templates"
    • medium Exfiltration net-credential-use README.md:88
      Credential used in a network call (verify the destination is the intended service)
      curl -H "X-API-Key: $ESSESSEFF_API_KEY" \

    Files scanned: 17. 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 62/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 25 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 17 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1607 tokens

    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
    • +1No license
    • +2Single-language instructions
    • +3Description length 501: enough signal without eating the budget
    • +4Structure: 9 headings
    • +3Step-by-step instructions: 17 items
    • +4Has examples (1 code blocks)
    • +4Reference files are cited in the instructions (11 of 11)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.

    External checks

    ClawHub: suspicious
    This appears to be a legitimate DevOps helper, but it can deploy to production, create repositories, delete packages, and handle secrets without enough built-in guardrails.
    LLM: suspicious (high) · VirusTotal: · 28 May 2026