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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."

61

Quality

73%

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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

67%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 well-structured, actionable discovery skill that maps developer intent to a concrete Twilio architecture via a clear capability ladder and qualification tree, correctly deferring implementation to Product skills. Its main weaknesses are verbatim repetition of constraints across sections and the absence of reference files to offload the long warnings/decision/GA sections.

Suggestions

De-duplicate the GA constraints: state each constraint once (e.g., Conversation Memory summary-only, operator lifecycle trap, cost model) and cross-reference rather than repeating verbatim across Step 3, Architectural Warnings, and GA Constraints.

Move the long Architectural Warnings, Decision Rules, and GA Constraints blocks into a reference file (e.g., references/gotchas.md) referenced one level deep, leaving SKILL.md as a lean overview.

Trim definitional padding that assumes Claude's knowledge (e.g., glossing 'real-time coaching' as 'live suggestions on the agent's screen') since the terms are self-explanatory in context.

DimensionReasoningScore

Conciseness

Mostly information-dense with genuinely unknown product specifics, but it repeats several constraints verbatim across sections (the Conversation Memory summary-only GA constraint appears in Step 3, Architectural Warnings, and GA Constraints; the cost model and operator-lifecycle trap each recur) and includes minor definitional padding like 'Live suggestions/prompts appearing on the agent's screen'; not a 2 because the bulk is non-obvious domain knowledge rather than concepts Claude already knows.

3 / 5

Actionability

Highly actionable for a planning skill: concrete 5-question qualification tree mapping answers to architecture, a capability ladder with explicit 'Skills to install' lists, specific decision rules (Google STT vs Deepgram, recording-method selection), and a copy-paste output template; not a 5 because some guidance remains high-level (e.g., 'which operators to activate' is left contextual) and webhook/API specifics are deferred.

4 / 5

Workflow Clarity

Clear sequenced workflow (Step 1 mode detection → Step 2 questions → Step 3 ladder → Step 4 context → output) with mode-based routing checkpoints (DISCOVERY/VALIDATION/BUILD) and a defined structured deliverable; not a 5 because there are no explicit validation/feedback loops, though the destructive/batch cap does not apply to this advisory skill.

4 / 5

Progressive Disclosure

Well-organized with clear section headers and a correct disclosure pattern that defers implementation detail to named Product skills ('Operator lifecycle gotchas ... are documented in the twilio-conversation-intelligence skill', 'Detailed method comparison ... in the twilio-call-recordings skill'); not a 5 because no bundle reference files exist, so lengthy sections (Architectural Warnings, Decision Rules, GA Constraints) are inlined in a ~200-line body rather than split into one-level-deep reference files.

4 / 5

Total

15

/

20

Passed

Description

78%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, specific description that names its niche, concrete advisory actions, and natural example triggers, with low conflict risk. Its main weakness is the absence of a canonical, comprehensive 'Use when ...' clause and some reliance on product jargon over everyday user phrasing.

Suggestions

Add an explicit 'Use when ...' clause listing natural trigger phrases (e.g., agent assist, agent coaching/copilot, script adherence, compliance monitoring, real-time sentiment detection) so the 'when' is canonical and comprehensive.

Reduce reliance on product-name jargon in favor of the everyday terms users actually say, keeping Conversation Intelligence/Memory/TaskRouter as secondary mentions.

Tighten the two quoted example requests into a single comma-separated trigger list to make the activation conditions scannable.

DimensionReasoningScore

Specificity

Names the domain and several concrete advisory actions — 'Qualifies the developer's use case across coaching, compliance, QA, and routing to recommend the right ... architecture' — with comprehensive coverage of the four augmentation categories; not a 5 because the verbs are abstract planning actions rather than concrete technical operations.

4 / 5

Completeness

Clearly states what it does ('Planning skill ... Qualifies ... to recommend the right architecture') and provides equivalent explicit trigger guidance via the two quoted example requests it 'Handles'; not a 5 because the 'when' is embedded in a 'Handles both' framing rather than a clean, comprehensive 'Use when ...' clause.

4 / 5

Trigger Term Quality

Includes natural user phrases via two quoted example requests ('I want to add AI coaching to my call center', 'configure Conversation Intelligence operators for script adherence') plus capability keywords; not a 5 because it leans on product-name jargon (Conversation Intelligence, Conversation Memory, TaskRouter) and omits common variants like 'agent assist', 'copilot', or 'sentiment detection'.

4 / 5

Distinctiveness Conflict Risk

Occupies a clear narrow niche (real-time human-agent augmentation via Twilio Conversation Intelligence/Memory/TaskRouter) with distinctive triggers, so conflict risk with unrelated skills is minimal.

5 / 5

Total

17

/

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.

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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