SKILLEMALL.ai

AC olares-shared

Olares profile and authentication foundation for olares-cli — required prerequisite for every other olares-cli skill on Olares (files, market, settings, dashboard, cluster). Covers the Olares profile model (one profile = one Olares instance + one Olares ID, e.g. alice@olares.com), first-time Olares login with password and optional TOTP, importing an existing refresh_token, switching / listing / removing Olares profiles, OS-keychain token storage keyed by Olares ID, automatic access_token refresh on 401/403, and the full Olares auth-error recovery table. Use when the user mentions Olares, Olares ID, olares-cli, OpenClaw on Olares, profile, login, logout, two-factor / 2FA / TOTP, refresh token, keychain, or sees errors like 'server rejected the access token', 'refresh token for X became invalid', 'no access token for X', 'already authenticated', or 'two-factor authentication required'.

ClawHub Agent Skills author: olares v4.0.1 MIT-0 2 files body ≈ 2 005 tokens Open the sourceclawhub.ai analyzed 2 d ago

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

IntegrationInfrastructureData and analyticsDesigntype and topics are labelled automatically from the skill text
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
81
Run on models
none yet
Process rating
C
62/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
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: 2. 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
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 9 steps
    • 100Failures and branches. 2 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2005 tokens
    • 100Running it twice. Mutating operations check current state
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low The response is described with custom markup (4 tags): a typed call is more reliable

    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 896: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +4Structure: 12 headings
    • +3Step-by-step instructions: 9 items
    • +4Has examples (3 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.

    External checks

    ClawHub: suspicious
    The skills are mostly coherent, but the bundled autoreview helper defaults to running a nested reviewer with unrestricted local access and can pass code diffs to fallback reviewer tools.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026