pymc-bayesian-modeling

Compatibility alias for the descriptive PyMC skill name. Delegate to the canonical local `pymc` payload while preserving route and README compatibility.

Quality

65%

Does it follow best practices?

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Securityby

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./bundled/skills/pymc-bayesian-modeling/SKILL.md

Quality

Content

86%Weight 40%Scale 1-5

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

The content is an exemplary thin alias: concise, clearly structured, and pointing one level deep to the canonical sibling payload. The only gap is the absence of a fallback path when the sibling is missing.

Dimension	Reasoning	Score
Conciseness	The ~30-line body is lean and purposeful with no padding or explanation of concepts Claude already knows; every section (Purpose, Resolution Order, Minimal Workflow) earns its place.	5 / 5
Actionability	It gives concrete, executable guidance with specific paths ("Read `../pymc/SKILL.md`", "Use the canonical templates and diagnostic scripts from `../pymc/`"), but provides no fallback if the sibling payload is absent, leaving a minor gap.	4 / 5
Workflow Clarity	The Resolution Order and Minimal Workflow are clearly numbered and sequenced, and the single delegation action is unambiguous; it loses a point only because there are no checkpoints (e.g., confirming the sibling payload resolved), though none are strictly required for a non-destructive alias.	4 / 5
Progressive Disclosure	The body is a thin overview with well-signaled, one-level-deep references to the sibling bundle (`../pymc/SKILL.md`, `../pymc/assets/**`, references, scripts) and content appropriately split out, which is exactly the right structure for an alias skill.	5 / 5
	Total	18 / 20 Passed

Description

45%Weight 40%Scale 1-5

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 clearly conveys the skill's alias/delegation nature but lacks trigger guidance and describes administrative rather than capability actions. It also carries intentional overlap with the canonical pymc skill it mirrors.

Suggestions

Add a 'Use when ...' clause naming natural triggers such as Bayesian modeling, MCMC, or probabilistic model fitting so Claude knows when to invoke this skill.

Reframe the action language around Bayesian-modeling capabilities (e.g., 'Build and diagnose PyMC Bayesian models') instead of alias-administration verbs like 'Delegate' and 'preserving route compatibility'.

Clarify distinctiveness by stating how this alias differs from the canonical `pymc` skill at trigger time, or note it should only fire for callers explicitly requesting the descriptive name.

Dimension	Reasoning	Score
Specificity	The description names the domain ("PyMC") and gives two meta-actions ("Delegate to the canonical local `pymc` payload", "preserving route and README compatibility"), but these are alias-administration actions rather than concrete Bayesian-modeling capabilities, so it sits at 'names the domain but actions are minimal'.	2 / 5
Completeness	It has a clear 'what' (a compatibility alias that delegates to the canonical pymc payload) but no 'Use when...' clause or equivalent trigger guidance, so per the cap it cannot exceed 3.	3 / 5
Trigger Term Quality	"PyMC" is a natural keyword a user would say, but the description omits common variations and synonyms such as Bayesian, MCMC, or probabilistic modeling, fitting 'some relevant keywords but missing common variations'.	3 / 5
Distinctiveness Conflict Risk	It is specific to PyMC/Bayesian modeling, but as an explicit alias of the sibling `pymc` skill it triggers for the same requests, creating real overlap with a closely related skill rather than minimal conflict.	3 / 5
	Total	11 / 20 Passed

Validation

100%

Warnings & errors only

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

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository: foryourhealth111-pixel/Vibe-Skills
Path: bundled/skills/pymc-bayesian-modeling/SKILL.md
Commit: ddcaa2a

Reviewed: 7 days ago

Table of Contents

Discovery Implementation Validation

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