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twilio-agent-augmentation-architect

Planning skill for augmenting human agents with real-time AI intelligence. Qualifies the developer's use case across coaching, compliance, QA, and routing to recommend the right Conversation Intelligence + Conversation Memory + TaskRouter architecture. Handles both "I want to add AI coaching to my call center" and "configure Conversation Intelligence operators for script adherence."

66

Quality

80%

Does it follow best practices?

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SecuritybySnyk

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tessl review fix ./plugins/twilio-developer-kit/skills/twilio-agent-augmentation-architect/SKILL.md
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 discovery/planning skill with a clear decision workflow and concrete product guidance. Its two weaknesses are repeated content across sections and a monolithic body that could benefit from splitting dense reference material into separate files.

Suggestions

Consolidate the recurring cost-model and operator-lifecycle content into a single section (and reference it where needed) to remove repetition and lift conciseness.

Move the detailed Decision Rules (transcription engine, operator, recording method selection) and the GA Constraints block into bundled reference files (e.g., references/decision-rules.md, references/ga-constraints.md) with a one-line pointer from the main body to improve progressive disclosure.

DimensionReasoningScore

Conciseness

The body is largely lean and assumes Claude's competence (no padding about what call centers or sentiment are), but material repeats across sections — the cost model recurs in Step 2, Architectural Warnings, and Decision Rules, and the operator PUT-trap appears in both Warnings and Decision Rules — so it could be tightened to the level-3 ideal.

2 / 3

Actionability

Provides concrete, specific guidance: named operators, exact dial verbs ('<Dial record="record-from-answer">', '<Pay>', '<Start><Recording>'), model trade-offs (GPT-4.1-nano vs GPT-5.2), and per-conversation-character pricing — actionable recommendations rather than vague direction.

3 / 3

Workflow Clarity

A clearly sequenced planning workflow — Step 1 mode detection → Step 2 five qualifying questions → Step 3 capability ladder → Step 4 context → Output Format — with explicit branching by request specificity; it is advisory rather than a destructive/batch operation, so the missing-validation cap does not apply.

3 / 3

Progressive Disclosure

No bundle files exist and the ~200-line body is a monolithic wall of text; detailed material like operator selection, recording-method comparison, and GA constraints could be split into reference files with clear navigation, matching the level-2 'content that should be separate is inline' anchor rather than the well-split level 3.

2 / 3

Total

10

/

12

Passed

Description

82%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 specific, well-scoped description that names concrete capabilities and natural trigger phrasings. Its main weakness is the absence of an explicit 'Use when...' clause, which leaves the 'when to use' guidance implied through examples rather than stated outright.

Suggestions

Add an explicit 'Use when...' clause (e.g., 'Use when adding AI coaching, compliance monitoring, QA automation, or intelligent routing to human contact center agents') to lift completeness to a 3.

Surface a few more common lay variations of trigger terms (e.g., 'agent assist', 'agent copilot', 'call center intelligence') so the description matches the natural language in the body's activation list.

DimensionReasoningScore

Specificity

Quotes concrete actions like 'Qualifies the developer's use case across coaching, compliance, QA, and routing to recommend the right Conversation Intelligence + Conversation Memory + TaskRouter architecture' — multiple specific actions and named products, matching the top anchor rather than the partial level 2.

3 / 3

Completeness

It states what the skill does and embeds example triggers, but lacks a dedicated explicit 'Use when...' clause; per the guideline, a missing explicit trigger clause caps completeness at 2 rather than the level-3 'clearly answers both what AND when'.

2 / 3

Trigger Term Quality

Includes natural user phrasings ('I want to add AI coaching to my call center', 'configure Conversation Intelligence operators for script adherence') alongside coaching, compliance, QA, and routing — good coverage of terms users would actually say.

3 / 3

Distinctiveness Conflict Risk

The niche is sharply defined around Twilio Conversation Intelligence + Memory + TaskRouter agent augmentation, with distinct triggers unlikely to fire for unrelated skills.

3 / 3

Total

11

/

12

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.

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository
openai/plugins
Reviewed

Table of Contents

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