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narrative-registry

Use when the user asks to record/query the brand narrative canon, tagline, message hierarchy, voice/naming rules, or a canon re-version; curates complete versioned canon events through the append-only narrative stream and derived views. Not for TALE scoring — use narrative-quality-auditor; not for authoring the system — use message-system-architect. 品牌叙事台账/canon 记录/语气与命名规范

60

Quality

71%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./protocol/narrative-registry/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

57%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 content is a well-organized protocol overview with good progressive disclosure and a clear step sequence, but it leans on external references for mechanics and lacks an explicit error-recovery feedback loop for its destructive canon-write operations. Some reinforcement repetition could be trimmed for token efficiency.

Suggestions

Add an explicit feedback loop after step 9: if `verify narrative` fails, fix the offending event/reference and re-run before declaring the canon accepted.

Consolidate the repeated 'not for TALE scoring / does not adjudicate claim truth' distinctions into one statement to reduce token redundancy.

Inline a minimal concrete invocation example (e.g., the registry-events.py command shape for an accepted `upsert`) so the core write path is actionable without opening the external reference.

DimensionReasoningScore

Conciseness

The body is mostly lean and assumes Claude's competence (no pedagogical explanation of canon/NDJSON/projection), but several constraints are restated — the 'not for TALE scoring' distinction, 'complete canon object', and 'generated views' recur — and could be tightened.

2 / 3

Actionability

There is some concrete executable guidance (resolving AARON_SKILLS_ROOT, `registry-events.py`, `verify narrative`) but core mechanics are deferred to external references and the bulk is protocol direction rather than copy-paste-ready instruction.

2 / 3

Workflow Clarity

Instructions 1-9 form a clear numbered sequence with validation checkpoints (step 6 validate references/claim IDs, step 9 run `verify narrative`), but for destructive canon writes there is no explicit validate→fix→retry feedback loop, capping clarity at 2 per the batch/destructive guideline.

2 / 3

Progressive Disclosure

The body is a concise overview with well-signaled, one-level-deep references in dedicated sections (Reference Materials, Next Best Skill) pointing to registry-event-protocol.md and runtime-invocation.md, keeping detail external and navigation easy.

3 / 3

Total

9

/

12

Passed

Description

85%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 specific, complete, and strongly disambiguated from sibling skills, with a clear 'Use when' trigger and concrete actions. Its main weakness is trigger-term coverage, which mixes natural domain terms with internal-mechanism jargon rather than offering multiple natural phrasings per concept.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — 'record/query the brand narrative canon, tagline, message hierarchy, voice/naming rules, or a canon re-version' and 'curates complete versioned canon events through the append-only narrative stream and derived views' — matching the multiple-specific-actions anchor.

3 / 3

Completeness

Explicitly answers both what ('curates complete versioned canon events...') and when ('Use when the user asks to record/query the brand narrative canon, tagline...'), satisfying the explicit-trigger anchor.

3 / 3

Trigger Term Quality

Domain-natural terms like 'tagline', 'message hierarchy', and 'voice/naming rules' are present, but mechanism jargon ('append-only narrative stream', 'derived views', 'canon re-version') is mixed in and only one phrasing per concept is offered, so common-variation coverage is incomplete.

2 / 3

Distinctiveness Conflict Risk

Explicit disambiguation — 'Not for TALE scoring — use narrative-quality-auditor; not for authoring the system — use message-system-architect' — carves a clear niche and steers away from adjacent skills.

3 / 3

Total

11

/

12

Passed

Validation

81%

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

Validation13 / 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: 10 suspicious

Warning

Total

13

/

16

Passed

Repository
aaron-he-zhu/aaron-marketing-skills
Reviewed

Table of Contents

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