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pleaseai/please-plugins

Auto-detect project dependencies and recommend matching Claude Code plugins from the pleaseai marketplace

Quality

82%

Does it follow best practices?

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SecuritybySnyk

Passed

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Overview
Quality
Evals
Security
Files

Quality

Content

75%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.

A highly actionable, well-sequenced workflow where every step has an executable command and the risky step (install) is gated on user confirmation. The main cost is the stale-prone Plugin Categories table, which pads the token budget without adding anything beyond the live catalog the skill already reads.

Suggestions

Drop the Plugin Categories table (or move it to a references/ file loaded only when ranking is ambiguous) — Step 1 already reads the authoritative catalog, and hardcoded plugin lists will drift as the marketplace changes.

Add a brief fallback for a missing or unreadable marketplace.json (e.g., marketplace not added) so the workflow has an explicit error-recovery path rather than failing silently.

Tighten the 'How It Works' preamble to a single sentence per layer; the two-layer explanation partially restates what Steps 1–2 already demonstrate.

DimensionReasoningScore

Conciseness

Most of the body is lean and command-driven, but roughly a fifth of it is the "Plugin Categories" table (14 rows of example plugin names like "nuxt, vue, react... nanobanana, gatekeeper") which duplicates data the skill already tells Claude to read from marketplace.json in Step 1 and will go stale as the catalog changes. That is more than the "minor instances of over-explanation" of the 4 anchor, fitting "includes some unnecessary explanation or could be tightened".

3 / 5

Actionability

Every step ships a copy-paste-ready command: `cat ~/.claude/plugins/marketplaces/pleaseai/.claude-plugin/marketplace.json`, `grep -rilFi "<search-terms>" .../*/SKILL.md`, `jq -r '.plugins | keys[]' ~/.claude/plugins/installed_plugins.json`, and `claude plugin install {plugin-name}@pleaseai`, plus a concrete output template for recommendations. Placeholders are clearly delimited and parameterized by the user's query, so the common cases are fully covered — matching the 5 anchor rather than 4's "minor gaps".

5 / 5

Workflow Clarity

Steps 1–5 are clearly sequenced (catalog → skill search → deduplicate and rank → present with installed-status check → offer install), with a user-confirmation checkpoint before the state-changing install step and a fallback section for the no-match case. It falls short of 5 because there is no error-recovery path for a missing or unreadable marketplace catalog (e.g., marketplace not yet added), leaving a minor validation gap per the 4 anchor.

4 / 5

Progressive Disclosure

The skill is a single self-contained file (no references/, scripts/, or assets/ exist) with clean, well-labeled sections for each phase. It scores 4 rather than 5 because the body is ~113 lines — above the under-50-line simple-skill exception — and the inline categories table is reference-style data that could live in a separate file or be dropped, a minor organization gap under "most content is appropriately placed".

4 / 5

Total

16

/

20

Passed

Description

82%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.

A strong description with an explicit, well-quoted trigger set and a clear what/when structure. Its weaknesses are the thin action list in the what-clause and the very broad implicit triggers ("how do I do X", "stuck on a task") that raise conflict risk with other skills.

DimensionReasoningScore

Specificity

The what-clause is "Helps users discover and install Claude Code plugins from the pleaseai marketplace" — it names a precisely-scoped domain and exactly two concrete actions (discover, install). This matches the anchor "Names domain and 1-2 concrete actions, but not comprehensive"; it does not reach 4 because capabilities like searching skill descriptions, checking installed status, or ranking/presenting recommendations are never stated as actions.

3 / 5

Completeness

Both halves are explicit: what = "discover and install Claude Code plugins from the pleaseai marketplace", and when = "Use this skill whenever a user asks..." followed by concrete trigger phrases. This directly matches the anchor "Clearly and explicitly answers both what AND when with concrete trigger phrases"; a 4 would require the when-clause to be less explicit or specific, which it is not.

5 / 5

Trigger Term Quality

The description quotes the exact natural phrases users would say — "how do I do X", "find a plugin for X", "is there a plugin that can...", "recommend a plugin", "what plugins are available" — plus paraphrase triggers like "wants to extend Claude Code capabilities" and implicit triggers ("mentions a technology/framework", "stuck on a task"). Coverage is comprehensive with synonyms and user-voice variations, matching the top anchor; it is not a 4 because no common natural phrasing for this need is obviously missing.

5 / 5

Distinctiveness Conflict Risk

The niche itself is distinct (a specific marketplace's plugin catalog), but the triggers "how do I do X" and "use when the user is stuck on a task... even if they don't explicitly ask for a plugin" are extremely broad and could fire on a large fraction of queries where another skill is the better answer. This lands on "Somewhat specific but could still overlap with similar skills" rather than 4, whose "minor overlap risk" understates how generic the implicit triggers are.

3 / 5

Total

16

/

20

Passed

Validation

93%

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

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

allowed_tools_field

'allowed-tools' contains unusual tool name(s)

Warning

Total

15

/

16

Passed

Reviewed

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