SKILLEMALL.ai
GitHub ↗ RU Sign in

BC competition-oauth-oidc-chain

Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for OAuth, OIDC, redirect flows, state or nonce handling, PKCE, token exchange, refresh logic, claim mapping, and accepted login paths. Use when the user asks to trace redirects, callback parameters, scopes, state, nonce, PKCE, refresh tokens, consent, or explain how an OAuth or OIDC chain turns into accepted identity or privilege. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

zhaoxuya520/reverse-skill Agent Skills author: zhaoxuya520 MIT 3 files body ≈ 678 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Internal downstream skill for ctf-sandbox-orchestrator.

As a process C 58/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches

IntegrationSoftware developmentMarketingtype 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
B
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
84
Run on models
none yet
Process rating
C
58/100
Has gaps
Inputs and preconditions w 11
0
Failures and branches w 10
0
Progress reporting w 2
0
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 · 0

    ✓ No critical or high findings

    Files scanned: 3. 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 58/100

    • 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
    • 40Result and completion. Does not say what the result is
    • 60Tools and files. Uses tools (node) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 19 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 678 tokens
    • 100Running it twice. No mutating operations

    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
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +3Description length 514: enough signal without eating the budget
    • +4Structure: 8 headings
    • +3Step-by-step instructions: 19 items
    • +4Reference files are cited in the instructions (1 of 1)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.