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AB atlassian-admin

Plans and audits Atlassian Cloud or Data Center administration: users, SSO, permissions, licences and instance health. Use when onboarding or offboarding users, auditing access, cutting licence cost or planning a DC exit.

borghei/Claude-Skills Agent Skills author: borghei 10 files · 3 scripts body ≈ 2 380 tokens Open the sourcegithub.com↗ analyzed 8 h ago

Plans and audits Atlassian Cloud or Data Center administration: users, SSO, permissions, licences and instance health.

As a process B 72/100 · Nearly there — weak spots: result and completion, inputs and preconditions, running it twice

AnalyzerConfluenceJiraCommerceOperations and projectstype and topics are labelled automatically from the skill text
Runs in: 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) OpenClaw Goose OpenCode
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
97
Quality 40%
92
Run on models
none yet
Process rating
B
72/100
Nearly there
Inputs and preconditions w 11
0
Running it twice w 4
30
Result and completion w 14
40
the three weakest of ten parameters · all ten

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
    • low Secrets in code secret-high-entropy-token examples/scaling-jira-50-to-500-users.md:104
      High-entropy token-like string (may be an id, hash or a credential) (test fixture / example file)
      Paym…mod     -> Team Project        1
      fixture
    • low Secrets in code secret-high-entropy-token examples/scaling-jira-50-to-500-users.md:105
      High-entropy token-like string (may be an id, hash or a credential) (test fixture / example file)
      Paym…nal      -> Team Project        2
      fixture
    • low Secrets in code secret-high-entropy-token references/red-flags.md:18
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      > "We have 47 permission schemes. New projects copy 'Eng-…024' which inherits half-deprecated groups."
      quoted

    Files scanned: 10. 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 72/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 2 mutating operations with no state check
    • 40Result and completion. Does not say what the result is
    • 50When it triggers. No condition that starts the skill
    • 100Tools and files. No external tools needed
    • 100Steps. 20 steps
    • 100Failures and branches. 1 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2380 tokens
    • 100Progress reporting. Reports progress
    • 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
    • +2Single-language instructions
    • +3Description length 221: enough signal without eating the budget
    • +4Structure: 14 headings
    • +3Step-by-step instructions: 20 items
    • +4Has examples (1 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)
    • +3All 3 scripts are documented
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 92.