eve-agent-memory▌
incept5/eve-skillpacks · updated Apr 8, 2026
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Agents on Eve Horizon have no built-in "memory" primitive, but the platform provides storage systems at every timescale. This skill teaches how to compose them into coherent memory for agents that learn, remember, and share.
Eve Agent Memory
Agents on Eve Horizon have no built-in "memory" primitive, but the platform provides storage systems at every timescale. This skill teaches how to compose them into coherent memory for agents that learn, remember, and share.
The Memory Problem
Every agent starts cold. Without deliberate memory design, agents:
- Re-discover the same facts on every job.
- Lose context when jobs end.
- Cannot share learned knowledge with sibling agents.
- Accumulate stale information with no expiry.
Solve this by mapping what to remember to where to store it, using the right primitive for each timescale.
Storage Primitives by Timescale
Short-Term (within a job)
Workspace files — the git repo checkout available during job execution.
# Workspace is at $EVE_REPO_PATH
# Write working state to .eve/ (gitignored by convention)
echo '{"findings": [...]}' > .eve/agent-scratch.json
# Workspace modes control sharing:
# job — fresh checkout per job (default)
# session — shared across jobs in a session
# isolated — no git state, pure scratch
eve job create --workspace-mode session --workspace-key "auth-sprint"
Use for: scratch notes, intermediate results, coordination inbox files. Ephemeral by design — workspace state does not survive the job unless committed to git or saved elsewhere.
Coordination inbox — .eve/coordination-inbox.md is auto-generated from coordination thread messages at job start. Read it for sibling status without API calls.
Agent KV Store — lightweight operational state with optional TTL. Use for: feature flags, rate counters, agent state machines, deduplication keys. Namespace-partitioned.
# Set a KV value with TTL
eve kv set --org $ORG_ID --agent $AGENT_SLUG --key "pr-123-status" --value '{"phase":"review"}' --namespace workflow --ttl 86400
# Get a KV value
eve kv get --org $ORG_ID --agent $AGENT_SLUG --key "pr-123-status" --namespace workflow
# List keys in a namespace
eve kv list --org $ORG_ID --agent $AGENT_SLUG --namespace workflow
# Batch get multiple keys
eve kv mget --org $ORG_ID --agent $AGENT_SLUG --keys "pr-123-status,pr-456-status" --namespace workflow
# Delete a key
eve kv delete --org $ORG_ID --agent $AGENT_SLUG --key "pr-123-status" --namespace workflow
Medium-Term (across jobs within a project)
Job attachments — named key-value pairs attached to any job. Survive after job completion.
# Store findings
eve job attach $EVE_JOB_ID --name findings.json --content '{"patterns": [...]}'
eve job attach $EVE_JOB_ID --name summary.md --file ./analysis-summary.md
# Retrieve from any job (including parent/child)
eve job attachment $PARENT_JOB_ID findings.json --out ./prior-findings.json
eve job attachments $JOB_ID # list all
Use for: job outputs, decision records, analysis results. Attached to a specific job, so retrievable by job ID. Good for passing structured data between parent and child jobs.
Threads — message sequences with continuity across sessions.
# Project threads maintain chat context
eve thread messages $THREAD_ID --since 1h
# Coordination threads connect parent/child agents
eve thread post $COORD_THREAD_ID --body '{"kind":"update","body":"Found 3 auth issues"}'
eve thread follow $COORD_THREAD_ID # poll for sibling updates
Use for: inter-agent communication, rolling context, coordination. Thread summaries provide compressed history. Coordination threads (coord:job:{parent_job_id}) are auto-created for team dispatches.
Thread Distillation — convert thread conversations into memory docs or org docs. Use for: preserving valuable discussion outcomes as searchable knowledge.
eve thread distill $THREAD_ID --org $ORG_ID --agent reviewer --category learnings --key "auth-discussion-findings"
Resource refs — versioned pointers to org documents, mounted into job workspaces.
eve job create \
--description "Review the approved plan" \
--resource-refs='[{"uri":"org_docs:/pm/features/FEAT-123.md@v3","label":"Plan","mount_path":"pm/plan.md"}]'
Use for: pinning specific document versions as job inputs. The referenced document is hydrated into the workspace at the specified mount path. Events track hydration success/failure.
Long-Term (across projects, persistent)
Org Document Store — versioned documents scoped to the organization.
# Store knowledge
eve docs write --org $ORG_ID --path /agents/learnings/auth-patterns.md --file ./auth-patterns.md
# Retrieve
eve docs read --org $ORG_ID --path /agents/learnings/auth-patterns.md
eve docs list --org $ORG_ID --prefix /agents/learnings/
# Search
eve docs search --org $ORG_ID --query "authentication retry"
Use for: curated knowledge, decision logs, learned patterns. Versioned (every update creates a new version). Emits system.doc.created/updated/deleted events on the event spine. Best for knowledge that is reviewed, refined, and shared.
Agent Memory Namespaces — curated knowledge stored as org docs with agent-scoped path conventions. Categories: learnings, decisions, runbooks, context, conventions. Supports confidence scores, tags, review dates, and expiration. Use for: accumulated expertise, decision logs, operational runbooks.
# Store a memory entry
eve memory set --org $ORG_ID --agent reviewer --category learnings --key "auth-retry-patterns" \
--content "Always use exponential backoff..." --confidence 0.9 --tags "auth,reliability" --review-in 30d
# Get a memory entry
eve memory get --org $ORG_ID --agent reviewer --key "auth-retry-patterns" --category learnings
# List entries
eve memory list --org $ORG_ID --agent reviewer --category learnings --limit 20
# Delete an entry
eve memory delete --org $ORG_ID --agent reviewer --category learnings --key "auth-retry-patterns"
# Search across memory (all agents or specific)
eve memory search --org $ORG_ID --query "auth retry patterns" --agent reviewer --limit 10
Namespace convention: /agents/{slug}/memory/{category}/{key}.md or /agents/shared/memory/...
Org Filesystem — shared per-org file storage mounted at .org/ in agent workspaces and synced to local machines.
# Agents access .org/ directly in their workspace (all execution paths)
ls .org/ # browse org files
cat .org/shared/runbook.md # read shared knowledge
echo "new finding" >> .org/agents/reviewer/notes.md # write agent-scoped files
# Developers sync to local machines
eve fs sync init --org $ORG_ID --local ~/Eve/acme --mode two-way
eve fs sync status --org $ORG_ID
eve fs sync logs --org $ORG_ID --follow
Use for: large knowledge bases, design assets, documentation trees. Agents see .org/ in their workspace regardless of execution path (agent-runtime or worker). Markdown-first defaults. Event-driven notifications (file.created/updated/deleted). Best for knowledge that lives as a file tree and benefits from both agent and human editing.
Skills and Skillpacks — distilled patterns packaged for reuse.
Use for: encoding recurring workflows and hard-won knowledge as reusable instructions. When an agent discovers a pattern worth preserving, distill it into a skill (see eve-skill-distillation). Skills are the highest-fidelity form of long-term memory — they don't just store information, they teach how to use it.
Managed databases — environment-scoped Postgres instances with agent-accessible SQL.
eve db sql --env $ENV --sql "SELECT key, value FROM agent_memory WHERE agent_id = 'reviewer' AND expires_at > NOW()"
eve db sql --env $ENV --sql "INSERT INTO agent_memory (agent_id, key, value) VALUES ('reviewer', 'last_review', '...')" --write
Use for: structured queries, relationship data, anything that benefits from SQL. Requires schema setup via migrations. Use eve db rls init --with-groups for access-controlled agent memory tables.
Shared (coordination across agents)
Org threads — org-scoped message sequences for cross-project coordination.
eve thread list --org $ORG_ID
eve thread post $ORG_THREAD_ID --body '{"kind":"directive","body":"All agents: use new auth pattern"}'
Event spine — pub/sub event bus for reactive workflows.
eve event emit --type=agent.memory.updated --source=app --payload '{"agent":"reviewer","key":"patterns"}'
eve event list --type agent.memory.*
Use for: broadcasting knowledge updates, triggering reactive workflows when memory changes.
Unified Search — single query across memory, docs, threads, attachments, events. Use for: finding relevant prior knowledge before starting work.
eve search --org $ORG_ID --query "auth retry patterns" --sources memory,docs,threads --limit 10 --agent reviewer
Memory Patterns
Pattern 1: Job-Scoped Scratch
The simplest pattern. Write working state to workspace files during execution. Nothing survives the job.
Job starts → read inputs → write .eve/scratch.json → process → complete
When to use: single-job tasks with no memory requirement.
Pattern 2: Parent-Child Knowledge Passing
Pass knowledge between orchestrator and workers using attachments and threads.
Parent creates children with resource-refs →
Children execute, attach findings →
Parent resumes, reads child attachments →
Parent synthesizes into final output
# Child stores its findings
eve job attach $EVE_JOB_ID --name findings.json --content "$FINDINGS"
# Parent reads child findings on resume
for child_id in $CHILD_IDS; do
eve job attachment $child_id findings.json --out ./child-${child_id}.json
done
When to use: orchestrated work where children discover information the parent needs.
Pattern 3: Org Knowledge Base
Build persistent, searchable knowledge that survives across projects and time.
Agent discovers pattern →
Check if existing doc covers it (eve docs search) →
If yes: update with new information (eve docs write)
If no: create new document (eve docs write) →
Emit event for other agents (eve event emit)
Namespace convention for agent-maintained docs:
/agents/{agent-slug}/learnings/ — patterns and discoveries
How to use eve-agent-memory on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add eve-agent-memory
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches eve-agent-memory from GitHub repository incept5/eve-skillpacks and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate eve-agent-memory. Access the skill through slash commands (e.g., /eve-agent-memory) or your agent's skill management interface.
Security & Verification Notice
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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
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Use Cases▌
User Story & Requirements Generation
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
Competitive Analysis
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
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
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
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ 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.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.4★★★★★30 reviews- ★★★★★Pratham Ware· Dec 28, 2024
eve-agent-memory is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Fatima Robinson· Dec 28, 2024
Solid pick for teams standardizing on skills: eve-agent-memory is focused, and the summary matches what you get after install.
- ★★★★★Dhruvi Jain· Dec 24, 2024
eve-agent-memory has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Olivia Thomas· Dec 24, 2024
We added eve-agent-memory from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Ava Nasser· Nov 27, 2024
eve-agent-memory is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★William Wang· Nov 19, 2024
eve-agent-memory has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Oshnikdeep· Nov 15, 2024
Solid pick for teams standardizing on skills: eve-agent-memory is focused, and the summary matches what you get after install.
- ★★★★★Mei Abbas· Nov 15, 2024
Useful defaults in eve-agent-memory — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Ava Okafor· Oct 18, 2024
eve-agent-memory fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Yusuf Taylor· Oct 10, 2024
Useful defaults in eve-agent-memory — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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