Reviews RAI agent skills for structure, content quality, prompt engineering, boundaries, examples, and agent usability. Use when creating, reviewing, or auditing skills in rai-agent-skills or rai-agent-skills-private.
Installation
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Reviews RAI agent skills for structure, content quality, prompt engineering, boundaries, examples, and agent usability. Use when creating, reviewing, or auditing skills in rai-agent-skills or rai-agent-skills-private.
RAI Skills Review
Litmus Test: Agent Usability
The ultimate quality gate. Everything below serves this — if an agent can't discover, navigate, adapt, and execute from the skill alone, the skill isn't done.
Discovery: Given a realistic user task, does the skill's description cause it to trigger (and not trigger for unrelated tasks)?
Navigation: Can an agent find the right section/example within the skill in 1-2 lookups (not wandering)?
Pattern adaptation: Given a novel problem, can an agent locate a relevant example pattern and adapt it to a new domain without hallucinating API calls?
Self-sufficiency: Can an agent go from skill content to working code without needing external docs, clarification, or guessing?
Negative test: Does the skill clearly redirect the agent when the task is out of scope (via "When NOT to use" pointers)?
Structure
YAML frontmatter with name and description (one line, imperative mood, trigger-ready, under 1024 characters)
Description answers two questions: (1) WHAT the skill covers, framed from user-intent angle not implementation mechanics; (2) WHEN Claude should invoke it, including cases where the user doesn't name the domain directly ("even if they don't mention X"). One sentence, both halves present.
SKILL.md at root, references/ for deep-dive, examples/ if applicable
## Summary with What, When to use, When NOT to use, Overview
## Quick Reference near top — tables/code blocks, not prose
## Common Pitfalls table (Mistake / Cause / Fix) captures counterintuitive, environment-specific facts the agent would get wrong without being told — not generic advice
## Examples table linking to example files
## Reference files with "when to use" framing
Stability classification (v1-STABLE or v1-SENSITIVE) below title
Content Quality
SKILL.md body under 500 lines
One term per concept throughout (no synonym alternation)
Examples don't contradict any documented rule/pattern
References one level deep from SKILL.md (no deep nesting)
Degrees of freedom match task fragility (narrow bridge = specific; open field = general)
No explaining the obvious: omit what the agent already knows (general concepts, standard libraries, common protocols) — every token should earn its place
Concise over exhaustive: stepwise guidance with a working example beats encyclopedic coverage — if content covers every edge case, check whether most are better left to agent judgment
Defaults over menus: when multiple tools/approaches apply, one is the default with brief escape hatch — not equal-weight lists of options
Short, generic parentheticals: keep inline "e.g." examples short and generic. Drop overly-specific example phrases unless they disambiguate a rule — when in doubt, cut them.
Extracted content keeps an entry point: when content moves to a reference file (line-count, depth, or scope reasons), SKILL.md retains (a) an inline summary or canonical table for the extracted topic, (b) a specific load-trigger pointer naming what's in the reference file, AND (c) a Reference Files table row. The agent must still discover the topic from SKILL.md alone.
Grounded, not generic: guidance reflects specific APIs, conventions, and failure modes — if a paragraph could apply to any project ("follow best practices", "handle errors appropriately"), cut or replace it with the project-specific rule it's standing in for
Prompt Engineering
Explicit instructions with specific decision criteria — no "use your judgment"
Every major rule explains WHY (runtime error, trivial solution, etc.)
Positive framing: state what to do, not just what to avoid
Description triggers on the problem class, affirmatively: the WHEN-clause names the intent/problem-type in positive terms (e.g. "node classification, regression, link prediction"; "optimization and constraint-satisfaction tasks") and routes onward with positive pointers ("then query with rai-querying"). Prefer this over negative-only framing ("even if they don't say X", "Not for Y"); keep a negative boundary only where needed to prevent overlap with an adjacent skill.
CRITICAL/MUST reserved for genuine compile/runtime failures only (not section headers)
Each piece of guidance stated once in one authoritative location
No hardcoded domain enumerations: no domain-specific laundry lists in prose or pattern definitions ("price ticks, sensor readings, KPIs…"). State the heuristic or shape distinction principled so the agent adapts to the user's data. One concise illustrative phrase is fine; a list is not.
Hallucination guard: instruct to verify references against provided context
Examples demonstrate correct pattern, not just anti-patterns
Procedures over declarations: skill teaches how to approach a class of problems, not what to produce for a specific instance — the method should generalize even when individual details are specific
Boundaries
MECE within skill (no content duplication across files)
MECE across skills (no overlap; boundary drawn in "When NOT to use")
Every ## section falls within scope defined by Summary
Cross-references use skill name only (e.g., rai-pyrel-coding), point to existing skills
Coherent scope: skill covers one coherent unit of work — not so narrow that related tasks force multiple skills to load, not so broad that activation becomes imprecise
Negative boundary in description when the positive scope could overlap with an adjacent skill — explicit "not for X (see other-skill)" clause. Routing failures from over-broad WHEN clauses are silent and expensive.
Instruction Patterns
Validation loops: for tasks with verifiable output, skill instructs the agent to validate its own work before moving on (run validator → fix → re-validate)
Reference load triggers are specific: references say when to load (e.g., "if the API returns non-200") — not generic "see references/ for details"
In-context reference links at point of need: each workflow step / decision point links the reference file it depends on right there — not only in a trailing catch-all or the Reference Files table. Cross-references are live markdown links: a bare section-name parenthetical (e.g. "(Advanced Variable Patterns)") with no link is a dead end — make it […](file.md) or cut it.
Pattern matches task shape: output-format templates when the agent must produce a specific structure; explicit checklists for multi-step workflows with dependencies; plan-validate-execute for batch/destructive operations; bundled scripts for logic the agent would otherwise reinvent each run
PyRel Examples
One canonical example per pattern (variants only when justified)
Every example file linked from Examples table or inline text
Short patterns (< 10 lines) inline; larger examples in separate files
Example files are self-contained (copy-paste-execute)
Adaptable: example concept and property names should be generic enough that an agent can map them to any domain. If a property name encodes a specific business vertical or workflow, rename it to a structural equivalent unless the pattern strictly requires the specific term.
Pattern-titled, pattern-framed: example file names, section titles, opening comments, and index-row descriptions lead with the PyRel construct or pattern being demonstrated. Keep descriptions pattern-only; don't append (illustrated with <domain>) tails unless the tail names a canonical OR / textbook pattern that itself functions as a pattern tag (the name is as widely understood as the construct it demonstrates).
No domain creep in property/concept names: catch vertical-specific terms in both the example-row descriptions and inside the example file contents. If the term is recognizable only within a specific industry or business function, it's too specific.
All code blocks parse with ast.parse — except blocks explicitly marked as anti-patterns (# WRONG: comment or surrounding prose flags them as intentionally invalid). Intentional anti-patterns illustrate failure modes and should not be coerced into compiling.
