Two-layer memory system for SEO/GEO projects with hot cache and cold storage.
Works with
Maintains a lean hot cache (CLAUDE.md, ~100 lines) for active context and structured cold storage directories for keywords, competitors, audits, and historical data
Automatically promotes frequently referenced items to hot cache and demotes stale data to archives based on reference frequency and age
Implements efficient context lookup flow across hot cache, glossary, and cold storage layers with intelligent
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionmemory-managementExecute the skills CLI command in your project's root directory to begin installation:
Fetches memory-management from aaron-he-zhu/seo-geo-claude-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate memory-management. Access via /memory-management in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
0
total installs
0
this week
873
GitHub stars
0
upvotes
Run in your terminal
0
installs
0
this week
873
stars
SEO & GEO Skills Library · 20 skills for SEO + GEO · ClawHub · skills.sh System Mode: This cross-cutting skill is part of the protocol layer and follows the shared Skill Contract and State Model.
This skill implements a three-tier memory system (HOT/WARM/COLD) for SEO and GEO projects. HOT memory (80 lines max) loads automatically every session via the SessionStart hook. WARM memory loads on demand per skill. COLD memory is archived data queried only when explicitly requested. The skill manages the full lifecycle: capture, promote, demote, and archive.
System role: Campaign Memory Loop. It defines how project context is captured, promoted, archived, and handed off across sessions. It is the sole executor of WARM-to-COLD archival and the aggregator for cross-skill project status queries.
Use this whenever project state should survive the current session — even if the user doesn't use memory terminology:
memory/hot-cache.md (80 lines max) — loaded automatically every session by SessionStart hookmemory/ subdirectories — loaded on demand by relevant skillsmemory/archive/ with date prefixmemory/ subdirectoriesmemory/open-loops.md, reminds user of stale items via SessionStart hookmemory/wiki/index.md — a structured, auto-refreshed index of all WARM files with precise fields (score, 健康度, status, next_action, mtime) and best-effort summaries. Supports project isolation via memory/wiki/<project>/index.md. Auto-refreshed on PostToolUse; user confirmation not required (index is a fully rebuildable derived artifact). Delete memory/wiki/ at any time to revert to pre-wiki behavior./seo:wiki-lint.wiki-lint --retire-preview lists WARM files fully covered by wiki compiled pages as retirement candidates. Actual archival to COLD requires explicit user confirmation.Start with one of these prompts. Finish with a hot-cache update plan and a handoff summary using the repository format in Skill Contract.
Refresh wiki index
Build wiki index for [project name]
Generates memory/wiki/index.md from existing WARM files. Required once to enable wiki features; subsequent refreshes happen automatically.
Set up SEO memory for [project name]
Initialize memory structure for a new [industry] website optimization project
Update memory after ranking check for [keyword group]
Refresh hot cache with latest competitor analysis findings
What are our hero keywords?
Show me the last ranking update date for [keyword category]
Look up our primary competitors and their domain authority
Promote [keyword] to hot cache
Archive stale data that hasn't been referenced in 30+ days
Add [term] to project glossary: [definition]
What does [internal jargon] mean in this project?
Expected output: a memory update plan, hot-cache changes, and a short handoff summary.
memory/hot-cache.md, memory/open-loops.md, memory/decisions.md, and related memory/ folders. Manages WARM-to-COLD archival in memory/archive/. Compiles memory/wiki/index.md (auto-refreshed) and wiki compiled pages (user-confirmed).Next Best Skill below when the project memory baseline is ready for active work.See references/promotion-demotion-rules.md for the full promotion/demotion table and action procedures.
This skill's behavior is reinforced by the library's prompt-based hooks:
memory/hot-cache.md, reminds of stale open loops; loads memory/wiki/<project>/index.md (or global index.md) if it exists; provides light-user guidance based on Quick Status when next_action items are availablememory/wiki/index.md (Phase 1); prompts to update compiled pages (Phase 2)See CONNECTORS.md for tool category placeholders.
With ~~SEO tool + ~~analytics + ~~search console connected: Automatically populate memory from historical data: keyword rankings over time, competitor domain authority changes, traffic metrics, conversion data, backlink profile evolution. The skill will fetch current rankings, alert on significant changes, and update both hot cache and cold storage.
With manual data only: Ask the user to provide:
Proceed with memory structure creation using provided data. Note in CLAUDE.md which data requires manual updates vs. automated refresh.
When a user requests SEO memory management:
For new projects, create the directory structure defined in the State Model. Key directories: memory/ (decisions, open-loops, glossary, entities, research, content, audits, monitoring) plus memory/wiki/ (auto-managed compiled index with optional per-project subdirectories).
Templates: hot-cache-template.md · glossary-template.md · Wiki spec
When a user references something unclear, follow this lookup sequence:
Step 1: Check CLAUDE.md (Hot Cache)
Step 2: Check Wiki Index (memory/wiki/index.md or project-level) — locate relevant WARM files
Step 3: Check memory/glossary.md
Step 4: Check Cold Storage
Step 5: Ask User
Example lookup:
User: "Update rankings for our hero KWs"
Step 1: Check CLAUDE.md → Found "Hero Keywords (Priority 1)" section
Step 2: Extract keyword list from hot cache
Step 3: Execute ranking check
Step 4: Update both CLAUDE.md and memory/monitoring/rank-history/YYYY-MM-DD-ranks.csv
Reference: See references/promotion-demotion-rules.md for detailed promotion/demotion triggers (keywords, competitors, metrics, campaigns) and the action procedures for each.
Reference: See references/update-triggers-integration.md for the complete update procedures after ranking checks, competitor analyses, audits, and reports; monthly/quarterly archive routines; and integration points with all 8 connected skills (keyword-research, rank-tracker, competitor-analysis, content-gap-analysis, seo-content-writer, content-quality-auditor, domain-authority-auditor).
When invoked for review or cleanup:
memory/hot-cache.md. If >80, list oldest entries for archival.name, description, and type in their frontmatter. Report any missing fields.After delivering any memory update or aggregation to the user, ask:
"Save these results for future sessions?"
If yes, write a dated summary to the appropriate memory/ path using filename YYYY-MM-DD-<topic>.md containing:
If any veto-level issue was found (CORE-EEAT T04, C01, R10 or CITE T03, T05, T09), also append a one-liner to memory/hot-cache.md without asking.
memory/monitoring/rank-history/ has a dated snapshot or exportmemory/research/competitors/ has a dated fileReference: See references/examples.md for three complete examples: (1) updating hero keyword rankings with memory refresh, (2) glossary lookup flow, and (3) initializing memory for a new e-commerce project.
Load full context for [campaign name] — retrieves hot cache + all cold storage files for a campaignRun memory health check — finds orphaned files, missing timestamps, stale items, broken referencesPromote all keywords ranking in top 10 to hot cache / Demote all completed campaigns from Q3Create memory snapshot for [date/milestone] — point-in-time copy for major milestonesCompare memory with [other project] — keyword overlaps, competitor intersections across projects/seo:wiki-lint [--fix] [--project name] [--retire-preview] — contradictions, orphans, stale claims, hash mismatches. See commands/wiki-lint.mdmtime field helps detect staleness.memory/wiki/ anytime to revert.Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
Useful defaults in memory-management — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
I recommend memory-management for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Solid pick for teams standardizing on skills: memory-management is focused, and the summary matches what you get after install.
memory-management has been reliable in day-to-day use. Documentation quality is above average for community skills.
memory-management has been reliable in day-to-day use. Documentation quality is above average for community skills.
Solid pick for teams standardizing on skills: memory-management is focused, and the summary matches what you get after install.
Solid pick for teams standardizing on skills: memory-management is focused, and the summary matches what you get after install.
Registry listing for memory-management matched our evaluation — installs cleanly and behaves as described in the markdown.
We added memory-management from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
memory-management reduced setup friction for our internal harness; good balance of opinion and flexibility.
showing 1-10 of 33