narrative-resonance-monitor

Use when the user asks to "measure how our narrative is landing", "track echo rate against our canon lexicon", or "check how AI answer engines describe our brand"; produces a resonance report — echo rate (overlap of market language with the narrative-registry canon lexicon, method declared), AI-answer perception via tavily.py --answer (proxy-labeled), share-of-voice on a locked competitor panel (reusing share-of-voice-tracker), and resonance signals from bluesky.py / gdelt.py / pageviews.py — every number labeled Measured / proxy / User-provided, feeding the TALE E dimension and the upstream of the E1 evidence-integrity veto. Not for rebuilding share-of-voice machinery — use share-of-voice-tracker; not for own-site GA4/GSC analytics — use performance-monitor; not for scoring TALE profile result — use narrative-quality-auditor; not for adjudicating claims — use offer-claims-registry. 回声率/AI回答感知/份额之声/共鸣信号

Quality

92%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

Securityby

Low

Low-risk findings worth noting

Quality

Content

85%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.

The body is a well-structured, highly actionable orchestration skill with a clear sequenced workflow and well-signaled one-level-deep references. Its single weakness is token efficiency: the same evidence-integrity invariants are restated verbatim in multiple sections and could be consolidated without losing clarity.

Suggestions

Consolidate the repeated 'authorized operation: propose request to registry-events.py' / claims.ndjson routing and the 'Measured / proxy / User-provided' labeling rule into a single stated-once invariant in the Skill Contract, then reference it from Instructions and Save Results instead of restating it.

Move the four 'Not for… — use X' boundary statements (currently split between the Scope guard paragraph and the description) into one canonical scope table to avoid duplication.

Trim the repeated 'this skill never adjudicates' caveat to a single declaration in the Skill Contract.

Dimension	Reasoning	Score
Conciseness	It assumes Claude's intelligence (no beginner-concept padding), but the proxy-labeling mantra ('Measured / proxy / User-provided'), the 'authorized operation: propose request to registry-events.py' phrasing, and 'never adjudicates' are repeated verbatim across the Contract, Data Sources, Instructions, and Save Results sections and could be tightened.	2 / 3
Actionability	Concrete, executable guidance throughout: named connector invocations (scripts/connectors/tavily.py --answer, gdelt.py, bluesky.py, pageviews.py), exact memory paths, explicit matching-method options (exact phrase / stem / semantic), and copy-paste Quick Start prompts — actionable for an instruction/orchestration skill.	3 / 3
Workflow Clarity	A clear 7-step numbered sequence with explicit checkpoints: a NEEDS_INPUT stop-and-route guard when no canon exists (step 1), a panel-change 'trend restart' flag (step 5), and a 'Done when' checklist defining completion with labeling and as-of-date requirements.	3 / 3
Progressive Disclosure	Well-organized into Quick Start, Skill Contract, Data Sources, Instructions, Save Results, Reference Materials, and Next Best Skill; external references (tale-benchmark.md, skill-contract.md, CONNECTORS.md, SECURITY.md, sibling SKILL.md files) are one level deep and clearly signaled in a dedicated Reference Materials list, with no nested reference chains. No bundle files exist under references/, scripts/, or assets/ to verify.	3 / 3
	Total	11 / 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.

The description is third-person, tightly scoped, and answers what/when with explicit natural-language triggers plus concrete outputs. Its only weakness is density — the TALE/E1 taxonomy jargon and the repeated proxy-labeling caveat make it longer than necessary, but no dimension is weakened enough to drop a level.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — 'echo rate (overlap of market language with the narrative-registry canon lexicon, method declared)', 'AI-answer perception via tavily.py --answer (proxy-labeled)', 'share-of-voice on a locked competitor panel', and 'resonance signals from bluesky.py / gdelt.py / pageviews.py' — rather than vague language.	3 / 3
Completeness	Explicitly answers both halves: 'Use when the user asks to…' (when) and 'produces a resonance report — …' (what), with explicit trigger guidance rather than implied timing.	3 / 3
Trigger Term Quality	Quotes natural phrasings a user would say — 'measure how our narrative is landing', 'track echo rate against our canon lexicon', 'check how AI answer engines describe our brand' — giving good coverage of likely trigger terms.	3 / 3
Distinctiveness Conflict Risk	Four explicit 'Not for… — use X' carve-outs (share-of-voice-tracker, performance-monitor, narrative-quality-auditor, offer-claims-registry) give it a clear niche and low conflict risk with sibling skills.	3 / 3
	Total	12 / 12 Passed

Validation

75%

Warnings & errors only

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

Validation — 12 / 16 Passed

Validation for skill structure

Criteria	Description	Result
metadata_field	'metadata' should map string keys to string values	Warning
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning
relative_links	Relative link issues: 31 suspicious	Warning
referenced_paths_exist	Referenced path issues: 13 missing, 13 deeper-than-1-level	Warning

	Total	12 / 16 Passed

Repository: aaron-he-zhu/aaron-marketing-skills
Path: narrative/evaluate/narrative-resonance-monitor/SKILL.md
Commit: 4a77c48

Reviewed: 9 days ago

Table of Contents

Discovery Implementation Validation

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.