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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

84%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

73%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, strongly sequenced measurement workflow with concrete connectors, paths, and validation gates — the 7-step flow and done-when checklist are exemplary. The main cost is token efficiency: the labeling discipline and sibling-skill routing are each restated several times, inflating the body without adding information.

Suggestions

State the Measured / proxy / User-provided labeling rule once (e.g., in the Skill Contract) and reference it from the steps, termination, and done-when clauses instead of repeating the full sentence five times.

Collapse the sibling-skill routing that appears in both the Scope guard paragraph and the Reference Materials list into one table (skill → what it owns → when to hand off).

Add one example connector invocation with arguments (e.g., tavily.py --answer '<brand>' --label proxy) and a short sample resonance-report fragment so the output shape is shown, not only described.

DimensionReasoningScore

Conciseness

The body never explains concepts Claude already knows, but the same discipline sentences are repeated many times: "every number labeled Measured / proxy / User-provided" appears in the intro, the contract's expected output, the done-when clause, step 7, and the termination rule; "never adjudicates" appears three times (contract writes, step 7, Save Results); the scope-guard and Reference Materials sections restate the same sibling-skill routing twice. This matches the 3 anchor ("mostly efficient but... could be tightened") — it is not 4 because the redundancy is systematic, not a few trimmable lines.

3 / 5

Actionability

Concrete, executable guidance throughout: named connector scripts with flags ("scripts/connectors/tavily.py --answer", gdelt.py, bluesky.py, pageviews.py), exact memory paths (memory/narrative-registry/canon.md, memory/events/claims.ndjson), a ready-to-use Quick Start prompt set, and a precise echo-rate definition with the declare-method-first rule. Not 5 because no actual example invocations or a sample report fragment are shown — the reader must infer argument shapes for the connectors and the report layout is only described, not exemplified.

4 / 5

Workflow Clarity

A clear 7-step sequence with explicit validation checkpoints: "If no current non-forked canon exists, stop with NEEDS_INPUT", the method-must-be-declared-before-computing defect rule, the trend-restart guard ("do not merge a new panel into an old trend line"), the [needs source] evidence gate routing to claims.ndjson, and a done-when checklist closing the loop. This is a read/measure skill so destructive-operation caps do not apply; it matches the 5 anchor's explicit validation and checklist criteria.

5 / 5

Progressive Disclosure

Well-organized sections with a Reference Materials list that signals each one-level-deep link and what it is for (tale-benchmark.md, stimulus-binding.md, CONNECTORS.md, SECURITY.md, sibling SKILL.md files). No bundle files ship with this skill (references/, scripts/, assets/ are absent), so the score rests on the body's own structure, which is good but not the 5 anchor: some detail (handoff-summary format, save-results template) is delegated to an external skill-contract.md outside this bundle, and several inlined routing/handoff statements duplicate material that belongs in those referenced files.

4 / 5

Total

16

/

20

Passed

Description

92%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 highly specific, trigger-rich description with explicit use-when phrases and exemplary negative-scope routing to sibling skills. The only weakness is density — one long sentence packing in jargon-heavy detail — and slightly narrow natural-term coverage beyond the quoted triggers.

DimensionReasoningScore

Specificity

Lists multiple concrete actions with the exact machinery for each: "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)", "resonance signals from bluesky.py / gdelt.py / pageviews.py" — comprehensive and concrete throughout. It is dense to the point of being a single run-on sentence, but every clause names a specific capability, not padding, so the verbosity guideline does not pull it below the 5 anchor.

5 / 5

Completeness

Explicitly answers both questions: what ("produces a resonance report — echo rate..., AI-answer perception..., share-of-voice..., and resonance signals..., every number labeled Measured / proxy / User-provided") and when ("Use when the user asks to..." with three concrete trigger phrases). The when-clause is fully explicit with quoted triggers, matching the 5 anchor exactly; it is not the 4 anchor because the trigger guidance is not merely present but concrete and varied.

5 / 5

Trigger Term Quality

Opens with natural quoted phrases users would plausibly say — "measure how our narrative is landing", "track echo rate against our canon lexicon", "check how AI answer engines describe our brand" — plus "describe our brand", "echo rate", "share-of-voice". Good coverage, but common synonyms and looser variants (e.g., "brand perception", "message tracking", "are people repeating our language") are absent, and the heavy TALE/canon jargon narrows the natural-term surface — between the 4 and 5 anchors, closer to 4.

4 / 5

Distinctiveness Conflict Risk

Clear niche (resonance measurement only) with explicit negative boundaries naming four sibling skills: "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". Minimal conflict risk; the routing is unambiguous.

5 / 5

Total

19

/

20

Passed

Validation

75%

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

CriteriaDescriptionResult

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: 29 suspicious

Warning

referenced_paths_exist

Referenced path issues: 9 missing, 9 deeper-than-1-level

Warning

Total

12

/

16

Passed

Repository

aaron-he-zhu/aaron-marketing-skills

Path

narrative/evaluate/narrative-resonance-monitor/SKILL.md

Reviewed

Table of Contents

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