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managed-model-endpoints

Register a model service in the managed family — a local model server container the daemon starts/stops on demand, or a remote upstream model API (https). Read the runbook, allocate a port (local only), compose idempotent start/stop scripts (local only), register once. Load when the user wants a model service available for inference, or when list_compute shows managed endpoints.

75

Quality

92%

Does it follow best practices?

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SKILL.md
Quality
Evals
Security

Quality

Content

100%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is a tightly written operational contract: executable code with justified placeholders, explicit validation and recovery loops, and well-signaled deferral to sibling skills for runbook and call-side detail. It avoids basic-concept padding entirely, so it scores at the top of every dimension.

DimensionReasoningScore

Conciseness

The body is dense with proprietary daemon-contract detail Claude would not know (managed-family semantics, free_port mutex, port-mismatch and runtime-binding guards, DOCKER_CONFIG scoping, container-uid cache derivation) and contains no padding about basic concepts like containers or Docker, matching the 'lean and efficient; every token earns its place' anchor. Not 2 because the length is justified by genuinely non-obvious operational density rather than unnecessary explanation.

3 / 3

Actionability

Provides a fully executable register() call with all parameters and a complete bash start script (create_service, port-mismatch guard, warm/cold branching, runtime-binding guard) that is copy-paste ready, matching the 'fully executable code/commands' anchor. The only placeholders (<image from the runbook>, <cache target from the runbook>) are explicitly justified because the runbook skill supplies them, so it is not penalized as pseudocode.

3 / 3

Workflow Clarity

The registration and lifecycle flow is clearly sequenced (free_port -> register -> daemon start -> readiness route -> cell) with explicit validation checkpoints (live='/v1/health/ready', port-mismatch guard, docker port check) and error-recovery feedback loops (FAILED is sticky -> Stop or byte-identical re-register; stuck stop -> out-of-band recovery), matching the 'clear sequence with explicit validation steps; feedback loops for error recovery' anchor. Not 2 because checkpoints are explicit, not implicit.

3 / 3

Progressive Disclosure

No bundle files exist; the single SKILL.md is organized into clear sections and defers detail to clearly-named sibling skills one level deep ('the model's own runbook skill', 'use the using-model-endpoint skill (that one documents the call side in full)'), matching the 'clear overview with well-signaled one-level-deep references; easy navigation' anchor. Not 2 because references are clearly signaled and the inline start script is the core contract, not content that should be split out.

3 / 3

Total

12

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12

Passed

Description

85%

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is specific, complete, and distinctive, clearly stating both what the skill does and when to load it. Its only weakness is trigger-term quality: the 'Load when' clause relies on internal jargon and misses common user-facing phrasings.

Suggestions

Add natural user-facing trigger terms such as 'serve a model', 'run a model server', or 'host a model endpoint' alongside the existing 'managed endpoints'/'list_compute' jargon.

Keep the explicit 'Load when...' clause but broaden its first branch to mirror how a user would actually phrase the need (e.g. 'when the user wants to serve or host a model for inference').

Consider stating the remote case as a user-facing trigger too (e.g. 'when the user wants to call a remote/upstream model API'), not only via the list_compute tool state.

DimensionReasoningScore

Specificity

Names multiple concrete actions — 'Read the runbook, allocate a port (local only), compose idempotent start/stop scripts (local only), register once' — matching the 'lists multiple specific concrete actions' anchor; it is above 2 because the action list is comprehensive, not merely 'some actions'. Voice is third-person imperative, so no person-penalty applies.

3 / 3

Completeness

Explicitly answers 'what' (the registration contract: 'Register a model service... compose idempotent start/stop scripts... register once') and 'when' ('Load when the user wants a model service available for inference, or when list_compute shows managed endpoints'), matching the 'clearly answers both what AND when with explicit triggers' anchor. Not capped at 2 because an explicit 'Load when' trigger clause is present.

3 / 3

Trigger Term Quality

An explicit 'Load when...' trigger exists, but the keywords lean on internal jargon ('managed family', 'list_compute', 'managed endpoints') and omit common user phrasings like 'serve/run a model' or 'model server', matching the 'some relevant keywords but missing common variations' anchor. Not 3 because coverage is jargon-heavy; not 1 because a natural-ish trigger phrase ('wants a model service available for inference') is present.

2 / 3

Distinctiveness Conflict Risk

Occupies a narrow named niche — daemon-owned 'managed family' model endpoints with distinctive triggers ('when list_compute shows managed endpoints') — making conflict with unrelated skills unlikely, matching the 'clear niche with distinct triggers' anchor. Not 2 because the managed/daemon framing is specific rather than merely 'somewhat specific'.

3 / 3

Total

11

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12

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
UnicomAI/wanwu
Reviewed

Table of Contents

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