/prod-end-of-day
End-of-day consolidation — analyzes agent memory, ADW logs, meetings, tasks, and learnings to generate a complete daily log. Trigger when user says 'end of day', 'wrap up', 'done for today', 'goodnight', 'shutdown', or anything that signals finishing a work session.
$ npx -y skills add evolution-foundation/evo-nexus --skill prod-end-of-day --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/prod-end-of-day
Context preview
The summary Claude sees to decide when to auto-load this skill.
End-of-day consolidation — analyzes agent memory, ADW logs, meetings, tasks, and learnings to generate a complete daily log. Trigger when user says 'end of day', 'wrap up', 'done for today', 'goodnight', 'shutdown', or anything that signals finishing a work session.
SKILL.md
prod-end-of-day.SKILL.mdname: prod-end-of-day
description: "End-of-day consolidation — analyzes agent memory, ADW logs, meetings, tasks, and learnings to generate a complete daily log. Trigger when user says 'end of day', 'wrap up', 'done for today', 'goodnight', 'shutdown', or anything that signals finishing a work session."
End of Day — Daily Consolidation
End-of-day routine that consolidates everything that happened during the day: agent memory, ADW logs, meetings, tasks, and learnings.
**Always respond in English.**
Step 1 — Collect data for the day (silently)
Read all available sources without narrating each step:
1a. Agent memory
Read recent memory files from each agent in `.claude/agent-memory/`:
- `flux-finance/` — financial decisions of the day
- `atlas-project/` — project updates
- `kai-personal-assistant/` — if there is anything relevant
- Any other agent that was used
1b. ADW logs
Read today's JSONL log at `ADWs/logs/YYYY-MM-DD.jsonl` to see which routines ran, duration, and status.
1c. Meetings of the day
Check `workspace/meetings/summaries/` and `workspace/meetings/fathom/` for today's synced meetings.
1d. Tasks
Run `todoist today` to see completed and pending tasks for the day.
1e. Git changes of the day
Run `git diff --stat` and `git log --oneline --since="today 00:00"` to see:
- Files created, modified, or deleted today
- Commits made (messages and authors)
- Uncommitted changes (working tree)
This gives the real overview of what changed in the workspace — more accurate than reading the conversation.
1f. Current session
Review the current session conversation — what was discussed, decided, and done.
Step 2 — Consolidate learnings
Analyze everything that was collected and identify:
- **Decisions made** — what was decided and why
- **Learnings** — patterns, corrections, feedback that should be remembered
- **People** — new context about team members
- **Real pending items** — things that truly remain open (do not fabricate)
Step 3 — Save memory
If there are relevant decisions, learnings, or feedback, save to persistent memory in `memory/` following the workspace memory system (see `prod-memory-management`).
Do not duplicate — check if similar memory already exists before creating.
Step 4 — Generate daily log
Read the template at `.claude/templates/end-of-day-log.md` and fill with the consolidated data.
Save to:
workspace/daily-logs/[C] YYYY-MM-DD.md
The log should include:
- What was done (projects, tasks, meetings)
- Files created or modified
- ADW routines that ran (with status)
- Pending items (only if real)
- Where to resume tomorrow
Step 5 — Organize tasks
Review Todoist tasks directly (do NOT invoke `/prod-review-todoist` as a sub-skill — it sends a duplicate Telegram notification):
- Run `todoist today` to list today's tasks
- For each uncategorized or non-PT-BR task: rename/recategorize via `todoist update`
- Report how many were organized
Step 6 — Confirm
Present a short summary:
## Day closed
**Log:** workspace/daily-logs/[C] YYYY-MM-DD.md
**ADW Routines:** {N} executed ({status})
**Tasks:** {completed}/{total} completed
**Memories:** {N} created/updated
**Learnings:** {N} recorded
**Tomorrow:** {sentence about where to resume}Read more
name: prod-end-of-day description: "End-of-day consolidation — analyzes agent memory, ADW logs, meetings, tasks, and learnings to generate a complete daily log. Trigger when user says 'end of day', 'wrap up', 'done for today', 'goodnight', 'shutdown', or anything that signals finishing a work session."
End of Day — Daily Consolidation
End-of-day routine that consolidates everything that happened during the day: agent memory, ADW logs, meetings, tasks, and learnings.
**Always respond in English.**
Step 1 — Collect data for the day (silently)
Read all available sources without narrating each step:
1a. Agent memory
Read recent memory files from each agent in `.claude/agent-memory/`:
- `flux-finance/` — financial decisions of the day
- `atlas-project/` — project updates
- `kai-personal-assistant/` — if there is anything relevant
- Any other agent that was used
1b. ADW logs
Read today's JSONL log at `ADWs/logs/YYYY-MM-DD.jsonl` to see which routines ran, duration, and status.
1c. Meetings of the day
Check `workspace/meetings/summaries/` and `workspace/meetings/fathom/` for today's synced meetings.
1d. Tasks
Run `todoist today` to see completed and pending tasks for the day.
1e. Git changes of the day
Run `git diff --stat` and `git log --oneline --since="today 00:00"` to see:
- Files created, modified, or deleted today
- Commits made (messages and authors)
- Uncommitted changes (working tree)
This gives the real overview of what changed in the workspace — more accurate than reading the conversation.
1f. Current session
Review the current session conversation — what was discussed, decided, and done.
Step 2 — Consolidate learnings
Analyze everything that was collected and identify:
- **Decisions made** — what was decided and why
- **Learnings** — patterns, corrections, feedback that should be remembered
- **People** — new context about team members
- **Real pending items** — things that truly remain open (do not fabricate)
Step 3 — Save memory
If there are relevant decisions, learnings, or feedback, save to persistent memory in `memory/` following the workspace memory system (see `prod-memory-management`).
Do not duplicate — check if similar memory already exists before creating.
Step 4 — Generate daily log
Read the template at `.claude/templates/end-of-day-log.md` and fill with the consolidated data.
Save to:
workspace/daily-logs/[C] YYYY-MM-DD.md
The log should include:
- What was done (projects, tasks, meetings)
- Files created or modified
- ADW routines that ran (with status)
- Pending items (only if real)
- Where to resume tomorrow
Step 5 — Organize tasks
Review Todoist tasks directly (do NOT invoke `/prod-review-todoist` as a sub-skill — it sends a duplicate Telegram notification):
- Run `todoist today` to list today's tasks
- For each uncategorized or non-PT-BR task: rename/recategorize via `todoist update`
- Report how many were organized
Step 6 — Confirm
Present a short summary:
## Day closed
**Log:** workspace/daily-logs/[C] YYYY-MM-DD.md
**ADW Routines:** {N} executed ({status})
**Tasks:** {completed}/{total} completed
**Memories:** {N} created/updated
**Learnings:** {N} recorded
**Tomorrow:** {sentence about where to resume}Other skills on evo-nexus.
- /ai-image-creator
Generate PNG images using AI (multiple models via OpenRouter including Gemini, FLUX.2, Riverflow, SeedDream, GPT-5 Image, proxied through Cloudflare AI Gateway BYOK). Also analyze/describe existing images using multimodal AI vision. Use when user asks to "generate an image",
Open skill - /create-agent
Create a new custom agent for the workspace. Guides the user through defining agent name, domain, personality, skills, model, and memory folder. Use when the user says 'create an agent', 'new agent', 'add an agent', 'I need a custom agent', or wants to create a specialized agent
Open skill - /create-command
Create a new slash command for Claude Code. Guides the user through defining the command name, what it does, and generates the markdown file in .claude/commands/. Use when the user says 'create a command', 'new command', 'add a slash command', 'I want a shortcut for', or wants
Open skill - /create-goal
Create a Mission, Project, or Goal (Mission → Project → Goal → Task hierarchy) in EvoNexus. Guides the user through picking a mission, choosing or creating a project, defining a measurable goal with metric_type and target_value. Writes to the SQLite goals tables via POST
Open skill - /create-heartbeat
Create a new heartbeat (proactive agent scheduled with a decision prompt) for EvoNexus. Guides the user through picking an agent, setting interval, wake triggers, and the decision prompt that governs when the agent acts. Writes to config/heartbeats.yaml with pydantic validation.
Open skill - /create-integration
Create a new custom integration (API/service wrapper) for the workspace. Guides the user through defining the integration's slug, display name, description, category, and required env keys. Writes .claude/skills/custom-int-{slug}/SKILL.md via POST /api/integrations/custom. Use
Open skill

