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narrative-drift-monitor

Use when the user asks to "check if our surfaces have drifted from the canon", "watch for competitor repositioning", or "define when we should reposition"; produces a drift report — self-drift per flagship surface vs the narrative-registry canon over time (via wayback.py, change history Measured with as-of dates), competitor-repositioning alerts, an explicit repositioning-trigger condition set, and a D1/W1/M1 message-shift retro (intended vs actual pull-through, evidence-labeled) — feeding the TALE L drift-audit sub-items and the narrative-whiplash guardrail fact base. Not for the first-time consistency check before a surface ships — use narrative-cascade-planner; not for computing the TALE profile result or running the vetoes — use narrative-quality-auditor; not for echo-rate / AI-answer resonance measurement — use narrative-resonance-monitor. 自漂移监测/竞品重定位告警/重定位触发/叙事漂移复盘

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.

The body presents a rigorous, well-checkpointed seven-step workflow with explicit stop conditions, evidence labeling, and routing — workflow clarity is excellent. Weaknesses are repetition of the same registry/propose guardrails across four sections and reliance on scripts (wayback.py, registry-events.py) and reference files whose usage and contents are never shown in this bundle.

Suggestions

State the registry sole-writer and claims.ndjson propose rules once (e.g. in Skill Contract) and reference them from other sections instead of repeating them three to four times.

Include a minimal invocation example for scripts/connectors/wayback.py (arguments and expected output for a change-history pull) so the core measurement step is executable without leaving the skill.

Ship or inline the essential content of the externally-resolved references (stimulus-binding retro rules, handoff-summary format) so the skill is self-contained when distributed alone.

DimensionReasoningScore

Conciseness

Mostly efficient — no generic-concept padding — but noticeably repetitious: the 'sole writer of memory/narrative-registry/' rule and the 'authorized operation: propose request to registry-events.py' pattern each appear three times (Scope guard, Skill Contract, Save Results), and the resonance-monitor disambiguation is restated in three sections. Fits the 3 anchor ('could be tightened'); not 2 because it never explains concepts Claude already knows, and not 4 because the duplicated guardrails are a real trimming opportunity, not minor.

3 / 5

Actionability

Concrete guidance throughout: exact paths (memory/narrative-registry/canon.md, versions.md), a verdict taxonomy (matches / drifted / contradicts), a decision enum (retain|retest|reversion-proposal|unknown), and evidence labels (Measured / User-provided / Estimated). It falls short of the 5 anchor because wayback.py and registry-events.py are invoked by name only — no invocation syntax, arguments, or output shape appears in the skill, and the recipes live in an unbundled CONNECTORS.md.

4 / 5

Workflow Clarity

Seven clearly sequenced steps with explicit validation checkpoints and feedback paths: step 1 stops with NEEDS_INPUT when no canon exists ('no canon' is never pass-by-default), step 2 records an explicit 'no history available' note rather than guessing, step 6 refuses to call a message validated without a matching result observation/contract, plus a 'Done when' checklist, decision routing, and termination/ambiguity rules. Matches the 5 anchor (explicit validation steps, error recovery, checklist).

5 / 5

Progressive Disclosure

Good structure: clean sections (Quick Start, Skill Contract, Data Sources, Instructions, Save Results, Reference Materials, Next Best Skill) and a well-labeled, one-level-deep reference list. However, the skill ships no bundle files of its own, and several load-bearing references (../../../references/skill-contract.md, ../message-test-designer/references/stimulus-binding.md, ../../../CONNECTORS.md) resolve outside the skill directory and cannot be verified from the bundle — minor organization gaps that hold it at the 4 anchor rather than the fully self-contained 5.

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.

The description is exceptionally strong: it specifies four concrete deliverables with named tooling, opens with an explicit 'Use when' clause containing three natural quoted trigger phrases, and explicitly fences off three adjacent skills to prevent mis-triggering. The only gap is modest synonym coverage for users who describe drift in other words.

Suggestions

Add one or two plain-language trigger variants (e.g. \"has our messaging changed since launch\" or \"brand consistency over time\") alongside the quoted canon-drift phrases to widen natural-term coverage.

Consider trimming the TALE-L / guardrail-fact-base lineage detail, which is ecosystem jargon that adds length without helping a user decide whether to invoke the skill.

DimensionReasoningScore

Specificity

Lists multiple concrete, comprehensive actions: 'self-drift per flagship surface vs the narrative-registry canon over time (via wayback.py…)', 'competitor-repositioning alerts', 'an explicit repositioning-trigger condition set', and 'a D1/W1/M1 message-shift retro', each with named mechanics (as-of dates, evidence labeling). This matches the 5 anchor (multiple specific concrete actions, comprehensive coverage); it is not 4 because there are no meaningful gaps in the action inventory.

5 / 5

Completeness

Explicitly answers both: what ('produces a drift report — self-drift… competitor-repositioning alerts, an explicit repositioning-trigger condition set, and a D1/W1/M1 message-shift retro') and when ('Use when the user asks to…' with three concrete quoted trigger phrases), plus explicit negative scope routing to three sibling skills. Matches the 5 anchor exactly; the 'when' is explicit and multi-phrased, not merely present as at 4.

5 / 5

Trigger Term Quality

Includes natural quoted trigger phrases a user would actually say — "check if our surfaces have drifted from the canon", "watch for competitor repositioning", "define when we should reposition" — plus drift/repositioning variants. Good coverage, but a few natural synonyms (e.g. "brand message changed over time", "consistency check over time") are missing, so it fits the 4 anchor rather than the comprehensive-synonyms 5 anchor.

4 / 5

Distinctiveness Conflict Risk

Clear niche (post-launch narrative drift monitoring) with explicit disambiguation — 'not for the first-time consistency check… use narrative-cascade-planner; not for computing the TALE profile result… use narrative-quality-auditor; not for echo-rate / AI-answer resonance measurement — use narrative-resonance-monitor'. Minimal conflict risk, matching the 5 anchor; at 4 there would be residual overlap with closely related skills, which the explicit negative scope eliminates.

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: 38 suspicious

Warning

referenced_paths_exist

Referenced path issues: 4 missing, 4 deeper-than-1-level

Warning

Total

12

/

16

Passed

Repository

aaron-he-zhu/aaron-marketing-skills

Path

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

Reviewed

Table of Contents

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