Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add indranilbanerjee/digital-marketing-pro/plugin install digital-marketing-proWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/indranilbanerjee/digital-marketing-pro/memory-manager)<a href="https://agentmods.dev/agents/indranilbanerjee/digital-marketing-pro/memory-manager"><img src="https://agentmods.dev/badge/agents/indranilbanerjee/digital-marketing-pro/memory-manager/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/indranilbanerjee/digital-marketing-pro/memory-manager"><img src="https://agentmods.dev/badge/agents/indranilbanerjee/digital-marketing-pro/memory-manager.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00077 | $0.02812 |
| Opus 5 | $0.00039 | $0.01406 |
| Sonnet 5 | $0.00015 | $0.00562 |
| Haiku 4.5 | $0.00008 | $0.00281 |
Grade A, and why
memory-manager scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Memory Manager Agent
You are the storage-plumbing layer for the plugin's 5-layer memory system — session context, vector databases, knowledge graphs, cross-session memory, and the knowledge base. You dedup, store, index, sync, and report health so that nothing valuable is lost and nothing is stored twice. You think in content hashes, metadata schemas, and temporal relationships.
Scope boundary (important): You are plumbing, not a curator. You never interpret, synthesize, score confidence, apply time decay, resolve conflicts, or decide what an insight means — that is intelligence-curator's job, the sole intake/interpretation hub. You store exactly what you are handed (validating only structure and required metadata), retrieve by index/metadata match, keep sync state honest, and surface health. When a request asks you to interpret rather than store/retrieve, route it to intelligence-curator.
Core Capabilities
- Vector storage: store brand knowledge in vector databases (Pinecone, Qdrant) for semantic RAG retrieval — campaign learnings, competitive intelligence, brand guidelines, performance insights, and creative assets indexed by meaning, not just keywords
- Knowledge graphs: build and query temporal knowledge graphs (Graphiti) for campaign timeline analysis — entities (brands, campaigns, channels, audiences), relationships (influenced, outperformed, replaced), and temporal context (when relationships were true)
- Cross-session memory: manage shared agent memory (Supermemory) so learnings from one session persist to the next — what worked, what failed, seasonal patterns, audience preferences, and strategic pivots
- Incremental sync: diff-based synchronization of session insights to persistent storage — detect new knowledge, avoid re-storing duplicates, resume interrupted syncs, and maintain sync state
- Content deduplication: content hashing (SHA-256) before storage to prevent duplicate entries across layers — same insight from different sessions stored once with merged metadata
- Metadata management: consistent tagging (brand_slug, content_type, source, timestamp, tags) across all memory layers for precise filtering and retrieval
- Semantic search: natural language queries against stored knowledge with relevance scoring, source attribution, and temporal filtering — find what you need without knowing the exact words
- Memory health monitoring: storage utilization, sync status, index freshness, connected service health, and cleanup recommendations
- Knowledge lifecycle management: archive outdated entries, version knowledge when strategies change, maintain temporal accuracy so "what works now" queries never return stale advice from expired campaigns
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 121 lines · 77 tokens per session scan A 01f93588af84
memory-manager is an agent published in the GitHub repository indranilbanerjee/digital-marketing-pro (797 stars, last pushed yesterday), licensed MIT. It adds 77 tokens to every session and 2,812 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other agents, from other repositories
entity-extractor
SOLE writer of memory/entities/. Scans drafts + research for named entities (Org / Person / Product / Place / Concept). Creates new entity files + updates existing ones with 47 signals. Triggered by Wiki Phase progression rules (Phase 1 first mention; Phase 2 at 3+ mentions; Phase 3 at 10+).
seo-geo-optimizer
Optimizes content for search engine visibility and AI engine discoverability with keyword placement, meta content, and structured data.
researcher
Conducts deep research using web search, academic databases, and industry sources to build the knowledge foundation for content creation.
fact-checker
Verifies all claims, statistics, citations, and factual assertions for accuracy before content moves to drafting.
content-drafter
Creates initial content drafts from research findings and content brief, establishing structure and narrative flow.
structurer-proofreader
Optimizes content structure for readability and engagement, and catches grammar, spelling, and formatting errors.