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project-development

This skill should be used for project-level decisions about LLM-powered systems: whether an LLM is the right primitive for the task at hand, the shape of a multi-stage batch or agent pipeline, token and cost estimation, choosing between single-agent and multi-agent at the project level, structured output design for downstream parsing, and structuring agent-assisted iteration. Use this when the unit of work is a whole project or a multi-stage pipeline. Route individual tool design to tool-design and individual skill-loading or context-budget tactics to context-optimization.

72

Quality

88%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

77%Weight 40%Scale 1-3

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

A well-structured, actionable methodology body with strong workflow sequencing and validation, held back by tightening opportunities in its rationale prose and an orphaned bundle script that breaks progressive-disclosure navigation.

Suggestions

Trim the repeated "because …" rationale clauses and the table rationale columns where the justification is self-evident, keeping the actionable rules but reducing token load (addresses conciseness).

Add a signaled link to scripts/pipeline_template.py from the body — e.g. in Practical Guidance or References — with a "Use when" trigger, so the existing 24KB bundle script is discoverable rather than orphaned (addresses progressive_disclosure).

Consider moving the two real-world Examples (Karpathy HN Capsule, Vercel d0) into case-studies.md and leaving a one-line pointer, since detailed case analysis already lives in that reference file (addresses both conciseness and progressive_disclosure).

DimensionReasoningScore

Conciseness

The body is information-dense and does not explain basic concepts Claude already knows, but the pervasive "because" rationale clauses and the table rationale columns add justification that could be tightened, so it is mostly efficient rather than maximally lean. It is not a 1 (no basic-concept padding) and not a 3 (not every token earns its place).

2 / 3

Actionability

Provides concrete, specific guidance — the canonical acquire→prepare→process→parse→render pipeline, the data/{id}/ file layout, the cost formula, a sequenced 5-step planning template, and real-numbered examples ($58, 930 items, 15 workers) — meeting the high bar for an instruction/methodology skill where absence of code is not penalized.

3 / 3

Workflow Clarity

Clear sequences with explicit validation checkpoints — the manual prototype as a go/no-go gate, idempotent delete-the-output-file-and-rerun as an error-recovery feedback loop, and a planning template that validates each step's assumptions before the next — satisfying the batch-operation feedback-loop requirement.

3 / 3

Progressive Disclosure

The two referenced files (case-studies.md, pipeline-patterns.md) are well-signaled and one level deep, but the bundle's 24KB scripts/pipeline_template.py is never referenced from the body, so navigation is incomplete. It is above a 2's "references not clearly signaled" on the linked files but below a 3's "easy navigation" because a major bundle file is undiscoverable.

2 / 3

Total

10

/

12

Passed

Description

100%Weight 40%Scale 1-3

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, specific description that answers both what and when, uses natural trigger terms, and explicitly disambiguates itself from sibling skills via routing. No weaknesses across the four dimensions.

DimensionReasoningScore

Specificity

Lists multiple concrete project-level actions — "whether an LLM is the right primitive", "shape of a multi-stage batch or agent pipeline", "token and cost estimation", "choosing between single-agent and multi-agent", "structured output design for downstream parsing", "structuring agent-assisted iteration" — matching the anchor for several specific concrete actions.

3 / 3

Completeness

Explicitly answers both what (the enumerated project-level decisions) and when ("Use this when the unit of work is a whole project or a multi-stage pipeline"), so it is not capped at 2 by the missing-trigger guideline.

3 / 3

Trigger Term Quality

Covers natural terms a practitioner would say — "project-level decisions", "multi-stage pipeline", "token and cost estimation", "single-agent and multi-agent", "LLM-powered systems" — giving good coverage of likely user phrasing rather than only jargon.

3 / 3

Distinctiveness Conflict Risk

Carves a clear niche (project/pipeline shape) and explicitly routes adjacent work away ("Route individual tool design to tool-design and individual skill-loading or context-budget tactics to context-optimization"), making wrong-skill activation unlikely.

3 / 3

Total

12

/

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
muratcankoylan/Agent-Skills-for-Context-Engineering
Reviewed

Table of Contents

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