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zerlinpizerlinpi/amazon-ads-skills/skills/keyword-optimization/SKILL.md

keyword-optimization

Skill8 starsChanged 53 days agoMITView on GitHub

Govern configured keyword/target structure, match type, state, lifecycle and traffic ownership in Amazon Ads. Use for keyword/target review and structural decisions; route Search Term row interpretation or harvest evidence to search-term-analysis and guarded negative actions to negative-targeting.

The file

We split this skill into the rules it gives an agent

2 rules. About 0 are common, 0 are distinctive, and 2 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈0

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 2

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    When a conclusion depends on live MCP/API/connector data, load ../../references/connector-capability.md before interpreting missing or …

    line 18
  2. Very specific:

    Search Term 相关否定动作继续路由到 ../negative-targeting/SKILL.md;具体 bid 数值继续路由到 ../bid-optimization/SKILL.md。

    line 36

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Keyword Optimization

Connector capability gate

When a conclusion depends on live MCP/API/connector data, load ../../references/connector-capability.md before interpreting missing or empty fields. If a machine-readable capability snapshot is available, identify the exact required capability IDs and run ../../scripts/evaluate_connector_capability_gate.py before metric interpretation.

  • Pass — continue with the Skill's normal evidence, sufficiency, lineage and safety checks.
  • Degraded — do not make a high-confidence dependent recommendation; keep the result to Directional, Hold, Alternate Source, Missing Data, or Manual Review.
  • Blocked — do not treat the dependent observation as action-safe until the capability is resolved or an allowed alternate source is verified.
  • missing_evidence_policy = never_zero — Partial, Unsupported, Unknown, or absent connector capability is never a numeric zero, unchanged state, or proof that the platform lacks the feature.

This gate is read-only and does not authorize live Amazon Ads mutation.

默认模式

Suggest。本 Skill 只生成结构与状态建议;具体 bid 数值优先交给 bid-optimization。

Routing boundary

  • 本 Skill 负责 configured keyword/target 的结构、匹配类型、状态、生命周期和流量分工。
  • Configured keyword/target analysis does not require treating the displayed Search Term string as a literal shopper query.
  • 一旦结论依赖 Search Term row 的显示字符串、query intent、Exact 收割或“这个词是否是真实 shopper query”,先路由到 ../search-term-analysis/SKILL.md,由它判断 term_origin 和 report/context coverage。
  • Search Term 相关否定动作继续路由到 ../negative-targeting/SKILL.md;具体 bid 数值继续路由到 ../bid-optimization/SKILL.md。
  • 不在本 Skill 复制 Search Term origin、report coverage 或 negative safety 的完整规则;复用对应 specialist 的结论。

输入

推荐提供:

  • keyword/target 级性能;
  • campaign/ad group;
  • match type;
  • state、bid;
  • 对应 search term;
  • 目标 ACOS/ROAS 或业务阶段;
  • 品牌词、核心类目词、竞品词等语义标签;
  • 历史窗口。

如果动作依赖 Search Term row,额外需要或明确标记缺失:

  • term_origin: literal_query / inferred_non_search / unknown;
  • report / traffic context / placement / matched-target evidence;
  • search-term-analysis 对该 row 的 origin 与 actionability 结论。

缺少这些 Search Term 语义证据不会阻止纯 configured keyword/target 的结构审查,但会阻止仅凭 displayed string 生成 action-safe harvest_to_exact。

生命周期分层

将关键词/目标分为:

Explore

相关性合理但数据不足。目标是获取样本,不急于做强动作。

Prove

已有初步点击/转化信号,继续验证稳定性。

Scale

稳定达到目标,可进入扩量/独立控制审查。Scale 是生命周期/结构标签,不自动证明存在可盈利 marginal headroom;具体扩量应再核对预算、竞价、零售和增长约束。

Protect

品牌词、核心防御词、战略词,即便短期效率波动也需要谨慎处理。

Reduce

样本充分且持续偏离目标,适合进一步评估降 bid、缩流量或拆分;具体金额/幅度交给相应控制 Skill。

Stop candidate

长期低价值且无战略意义,才考虑暂停/否定;必须保留证据。若结论来自 Search Term displayed string,仍需经过对应 origin/control gate。

匹配类型分析

Broad

用于探索。检查是否持续产生大量低价值 Search Term,以及赢家是否值得进入收割审查;不要把未知 origin 的显示字符串直接当成 shopper query。

Phrase

用于意图约束和中等探索。检查流量是否被少数长尾 rows 主导;涉及 query intent 时先走 search-term-analysis。

Exact

用于高控制度的已验证 query/target。检查是否因为 bid/budget 不足而失去已验证流量,但不要因为一个 displayed Search Term string 表现好就反推它一定应该成为 Exact keyword。

不要仅因为 Exact ACOS 高于 Broad 就机械调整;不同 match type 承担的流量阶段不同。

Search Term → Exact 收割门

harvest_to_exact 不是 keyword-optimization 根据 displayed string 自行完成的推断。只有当 Search Term 专项分析能支持以下条件时,才把候选升级为 action-safe:

  1. term_origin = literal_query,或有等价且可审计的 shopper-query 证据;
  2. report/traffic context 足以把显示字符串解释为可由 keyword/target 结构控制的 query;
  3. 已检查现有 Exact/target,避免重复或错误迁移;
  4. 性能/业务证据支持独立控制,而不是一次偶然订单;
  5. 收割后原流量入口的处理顺序明确,避免先切断再创建承接结构。

如果 term_origin = inferred_non_search 或 unknown,该 row 仍可作为 traffic/match/performance signal,但不能仅依据 displayed string 输出 action-safe harvest_to_exact。保持 Manual Review,或请求 search-term-analysis 所需的 origin/context 证据。

重复与争抢

检查:

  • 同一已验证搜索意图在多个 campaign/ad group 重复投放;
  • Exact 已验证收割但源 Broad/Phrase 仍存在需要治理的流量;
  • 多个关键词争抢导致数据碎片化;
  • 同一 ASIN 的品牌/泛词/竞品意图混在一个结构中。

“同一搜索意图”的判断若依赖 Search Term row,需要先满足 origin/context gate;不能把 inferred/unknown row 的 displayed string 当成确定 query identity。

输出“是否真的需要重构”的证据,不以结构整齐为目的过度拆分。

优化工作流

  1. 先识别用户目标和业务阶段;
  2. 按 configured keyword/target 的性能和已知语义给实体分层;
  3. 若需要用 Search Term 解释真实流量质量、query intent 或赢家收割,先调用 search-term-analysis 获取 term_origin / coverage / actionability;
  4. 只有 literal-query(或等价可审计)证据成立时,才把 Search Term winner 升级为 harvest_to_exact 候选;inferred_non_search / unknown 保持 traffic signal / Manual Review;
  5. 找到长期低效 configured keyword/target,并判断更可能是 bid、query mix、placement 还是 Listing/CVR 问题;
  6. 将 bid 数值问题路由给 bid-optimization;
  7. 将搜索词否定问题路由给 negative-targeting;
  8. 输出最小必要结构变更,避免一次大范围重构。

输出

Keyword tiers

按 Scale / Protect / Explore / Reduce / Stop candidate 分类。若某个 tier 结论依赖 Search Term 语义,附上 origin/actionability 状态。

Structural issues

列出重复争抢、匹配类型错位、过度碎片化、流量意图混杂。需要 query identity 的判断必须注明是否经过 Search Term origin gate。

Recommended actions

动作类型可包括:

  • harvest_to_exact — 仅在 literal-query/等价 origin evidence 与控制映射充分时;
  • move_to_dedicated_campaign
  • change_match_strategy
  • pause_candidate
  • route_to_search_term_analysis
  • route_to_bid_optimization
  • route_to_negative_targeting

每项必须包含 reason、evidence、confidence、mode、validation_window。Search Term 派生动作还应包含 term_origin / origin confidence 或 Manual Review 状态。

禁止

  • 不因短期高 ACOS 自动 pause;
  • 不将品牌词与普通泛词用同一阈值判断;
  • 不同时建议同一 keyword “扩量”和“暂停”;
  • 不为了所谓最佳实践强制重构本来表现稳定的账户;
  • 不把 inferred_non_search / unknown Search Term row 的 displayed string 直接收割成 Exact keyword;
  • 不在 keyword-optimization 内绕过 search-term-analysis 已有的 Search Term origin / coverage 安全门。

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