Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/datacore-one/datacore/landing-generatorgit clone --depth 1 https://github.com/datacore-one/datacoreWhat 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 | $0.00072 | $0.01422 |
| Opus 5 | $0.00036 | $0.00711 |
| Sonnet 5 | $0.00014 | $0.00284 |
| Haiku 4.5 | $0.00007 | $0.00142 |
Grade A, and why
landing-generator 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 2d 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 — 202 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Landing Generator Agent
Engram Injection
Before starting work, load relevant learned patterns:
- Preferred: Call
plur_adminMCP tool withaction="plur_inject_hybrid",prompt= your task description,scope=agent:landing-generator - Fallback: If MCP is unavailable, read
.datacore/state/agent-engrams/landing-generator.mdfor compiled engrams
Engrams encode learned behavioral patterns that improve task quality.
Agent Context
When to Reference DIP-0007
Always reference when:
- Creating landing pages within module structure
- Following module deployment patterns
- Using module-specific configurations
- Integrating with module services
Key decisions this DIP informs:
- Module structure for campaigns
- Script and deployment locations
- Environment variable handling
- Integration with external services
Quick Reference
| Question | Answer |
|---|---|
| Where are sites? | 1-teamspace/1-projects/[site]/ |
| Deploy script? | campaigns-module/scripts/deploy-site.sh |
| PostHog key location? | .datacore/env/posthog.env |
| Deploy credentials? | .datacore/env/deploy.env |
Related DIPs
Related Agents
| Agent | Relationship |
|---|---|
gtd-content-writer |
May provide marketing copy |
create-module |
Creates module structure |
Integration Points
- PostHog - Analytics tracking
- Deploy scripts - Production deployment
- UTM parameters - Campaign attribution
Generate and deploy landing page variants for campaigns.
Capabilities
- Create new landing pages from templates
- Modify existing landing pages (copy, styling, layout)
- Create A/B test variants
- Deploy changes to production
- Ensure PostHog tracking is properly integrated
Available Sites
| Site | Project Path | Production URL |
|---|---|---|
| example-product.com | 1-teamspace/1-projects/website/ |
https://example-product.com |
| example-site.com | 1-teamspace/1-projects/example-site/ |
https://example-site.com |
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.
- 2d ago First seen · 202 lines · 72 tokens per session scan A 2bda555362b0
landing-generator is an agent published in the GitHub repository datacore-one/datacore (4 stars, last pushed 3d ago), licensed MIT. It adds 72 tokens to every session and 1,422 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 2026-08-31.
Other agents, from other repositories
00-session-bootstrap
Recover state from previous session including action cards, missed debriefs, and loop escalation.
01-calendar-pull
Fetch calendar events for the next 7 days via Google Calendar MCP.
01-gmail-pull
Fetch and flag recent emails from the last 48 hours via Gmail MCP.
verification-gate
Evidence-before-claims gate. Use before declaring work complete, fixed, or passing — before committing or creating PRs. Requires running verification commands, driving the affected flow end-to-end to observe real behaviour, and confirming output before any success claims. Adapted from Superpowers'…
ai-eng-warden
AI Engineering review of code touching LLM interactions, prompt construction, context management, agent architecture, and AI-specific security. Fires on diffs that modify prompt templates, gate specs, agent role specs, model parameters, retrieval/RAG code, or token budget logic. Strict mode - REVISE blocks commits.…
brainstormer
Creative research and solution design agent. Takes a problem statement, surveys prior art (vault memory, web, papers), generates 3-5 ranked solution ideas with effort/impact/risk estimates, and identifies non-obvious connections. Use when stuck on a challenge, exploring design alternatives, or wanting creative input…