AGENT SKILLBY RETIEEDRA-PROFOUND
Session end
AT A GLANCE
Session end is an agent skill for general marketing. End-of-session capture: knowledge-base daily log, memory mirroring, candidate surfacing. It is published by retieedra-profound in the retieedra-profound/skills-marketing-library GitHub repository under the MIT license. To add it, run npx skills add retieedra-profound/skills-marketing-library --skill session-end
- REPO STARS
- NOT AVAILABLE
- LICENSE
- MIT
- CATEGORIES
- SKILL.MD SIZE
- 8.2 KB216 LINES
QUICK ANSWER
- WHAT IT IS01
- End-of-session capture: knowledge-base daily log, memory mirroring, candidate surfacing. Delegates session-summary synthesis to a memory plugin. Use when wrapping up a work session. It brings Wispr Flow's specific operating context into session end, so the agent is guided by a sharper source than a generic prompt.
- INPUTS02
- Context the agent needs: your goals, audience, constraints, and any source material the skill asks for.
- OUTPUT03
- A ready-to-use result: the analysis, copy, or recommendations the agent produces.
INSTALL
One command adds this skill from its source repository.
npx skills add retieedra-profound/skills-marketing-library --skill session-endREAD THE SKILL.MD BELOW BEFORE YOU RUN IT. SKILLS INSTRUCT YOUR AGENT.
SKILL.MD
The full file, as published in the source repository. Copy it, download it or open it on GitHub.
ON THIS PAGE · 12
Session End: Knowledge Base Sync + Memory Capture#
Captures curated learnings and mirrors them into a linked knowledge base (for example, an Obsidian vault organized Zettelkasten-style).
Adapt the paths and tool names below to your own setup. This skill assumes a daily-log knowledge base, a cross-session memory layer, and an optional memory plugin. The
<vault>/placeholder is wherever your knowledge base lives.
How this skill relates to a memory plugin#
A memory plugin (for example, Claude Mem) automatically generates a semantic session summary at session end and stores it in its own database. This skill does not duplicate that synthesis. Instead it captures curated signal: decisions that matter, memory updates, knowledge-base mirrors, and promotion candidates. The rich observation log stays in the memory plugin (query it with its search tool).
Step 1: Gather curated session context#
Don't narrate the whole session, the memory plugin has that. Extract:
- Key levers moved: 1-3 decisions or strategic shifts that matter
- Open loops: blockers, pending items carrying forward
- Next focus: priority for next session
- Projects touched: for knowledge-base links
- People mentioned: for knowledge-base links
Step 2: Daily log (primary record)#
Write or append to <vault>/daily/YYYY-MM-DD.md. Create the file if missing with frontmatter:
---
type: session-log
date: YYYY-MM-DD
---Append a block for this session:
## [HH:MM], [workspace], [Brief title]
**Focus:** [one-line]
**Key levers:**
- [Decision or shift that matters]
**Open loops:**
- [Items carrying forward]
**Projects touched:** [[projects/slug-1]], [[projects/slug-2]]
**People:** [[name-1]], [[name-2]]
**Next focus:** [One line]
---Keep it tight. If nothing moved, write one line: No significant levers, routine execution session.
Step 3: Thin local pointer#
Append a short pointer to your local session log at logs/sessions/YYYY-MM-DD.md so a weekly review tool can still find it without re-synthesizing what the memory plugin already stored:
## [HH:MM], [Brief title]
**Focus:** [one-line]
**Key levers:**
- [Top 1-3 items, verbatim copy from the daily log above]
**Next:** [Next focus]
_(Full session history in the memory plugin. Curated atoms live in the knowledge base.)_
---Step 4: Update agent state#
Resolve the current user, then update the per-user state file (NOT the shared one):
{
"last_updated": "YYYY-MM-DD",
"current_focus": { "initiative": "...", "session_goal": "...", "notes": null },
"last_session": { "date": "...", "focus": "...", "key_levers": [...], "open_items": [...], "next_steps": [...] },
"last_session_note": "narrative paragraph capturing the session's thread"
}Also update active_goals, reminders, and per-user recurring_tasks if they changed.
Never write personal session state to the shared state file, that file is the shared company source of truth (company block plus shared recurring tasks only). Each user owns their own session state in their per-user file.
Step 5: Memory files + knowledge-base mirror#
Review the session for information that should persist across conversations.
| Signal | Memory action | Knowledge-base mirror |
|---|---|---|
| New strategic decision | Create/update project_{slug}.md | Write to <vault>/projects/{slug-no-underscores}.md |
| User corrected your approach | Create/update feedback_{slug}.md | Write to <vault>/references/{slug}.md + add candidate |
| Learned about external resource/tool | Create/update reference_{slug}.md | Write to <vault>/references/{slug}.md |
| Metrics, targets, team changed | Update existing file | Update mirror |
| Memory now stale or completed | Remove file + index entry | Remove mirror |
Mirror frontmatter:
---
type: project | reference
workspace: [workspace]
source_memory: <original memory filename, with .md>
aliases:
- <original memory filename, without .md>
last_updated: YYYY-MM-DD
---Body: verbatim copy of memory file content (drop original frontmatter).
Slug rule: filename without prefix, underscores → hyphens, no .md. For example project_ph_takeover.md → ph-takeover.md.
Why the alias (load-bearing): memory-file bodies link with the full slug, for example [[reference_cac_canonical]], but the mirror filename is hyphenated, for example cac-canonical.md. A linked knowledge base like Obsidian resolves [[slug]] against a note's aliases, so the alias line is what keeps those links from breaking. A mirror written without it lands as an orphan. Note: feedback_* files mirror to references/ with type: reference.
Connect it, don't just drop it. After writing a mirror, link it so it is not an orphan:
- Add it to the matching domain map-of-content (MOC) index.
- Make sure the daily-log block forward-links it under Projects touched or a References touched line.
A mirror with no inbound link is debt that a graph-health check will flag.
Memory file rules:
- The memory index stays small; one line per file, kept short
- Check for existing memory before creating new
- Update the index when files are added or removed
Step 6: Candidate detection#
Scan for promotion candidates. Append to your inbox candidates.md when any of these fire:
- Decision logged with explicit rationale
- New or updated
feedback_*file - Framework or mental model applied and worked
- User said "remember this," "important," or "worth noting"
- Memory flagged stale and the concept needs re-examination
Append under ## Pending:
## [YYYY-MM-DD workspace] {short title}
**Source:** memory/{filename} OR session log
**Snippet:** {1-3 lines, trimmed}
**Why surfaced:** decision | feedback | theme | explicit-flag
**Status:** pendingIn-flow capture (the user's own words). When the session contains a passage where the user articulated a rule, framework, or tradeoff in their own words (chat or dictation), quote it back in the candidate entry verbatim, not paraphrased. Mark it so a later promotion pass can promote it without a fresh dictation pass:
## [YYYY-MM-DD workspace] {short title}
**Source:** session log (user's words, [HH:MM])
**Snippet:** "{verbatim quote, exact}"
**Why surfaced:** explicit-flag
**Status:** pending
**Promotion:** eligible for direct promotion (user's words)Apply mechanical AI-tell strips only (no em dashes); never rephrase the quote.
Step 7: Surface promotion reminder#
Count Status: pending entries in candidates.md. If >5, end with:
N candidates waiting for a voice pass. Run the promotion skill when you want to turn them into permanent notes.
In-flow articulations. If this session produced any candidates marked **Promotion:** eligible for direct promotion (user's words), offer to act now rather than only queuing them.
Step 8: Permanent-instructions flag (optional)#
If a learning should become permanent instructions (not just memory), ask:
"Should any of these be added to the project instructions file for all future sessions?"
What NOT to do#
- Never re-synthesize the full session narrative. The memory plugin has the semantic summary. Extract key levers only.
- Never auto-write permanent voice notes. Those are the user's voice only, via a dedicated promotion pass.
- Never auto-edit the MOC index files. That is a separate synthesis step.
- Never rephrase memory file content when mirroring. Verbatim copy preserves voice.
Output format#
## Session Wrapped: [Date]
### Logged
- Daily log: `<vault>/daily/YYYY-MM-DD.md`
- Local pointer: `logs/sessions/YYYY-MM-DD.md`
- (Memory plugin semantic summary: automatic)
**Key levers:** [1-3 items]
**Open loops:** [Items]
**Next focus:** [Focus]
**Memory updated:**
- [Created/Updated/Removed: file, reason → mirror path]
- (or "No memory changes this session")
**Candidates surfaced:** N
[If pending > 5] **N candidates waiting for a voice pass.**
---
**Agent state updated.** Next session will start with this context.Cross-references#
- session-start, next session; trusts the memory plugin injection for history
- A promotion skill, turn pending candidates into permanent voice-pass notes
- A synthesis skill, refresh the workspace index and MOCs
FAQ
07 QUESTIONS
01What is the Session end agent skill?
End-of-session capture: knowledge-base daily log, memory mirroring, candidate surfacing. Delegates session-summary synthesis to a memory plugin. Use when wrapping up a work session. It is listed under general marketing in the 100xLab agent skills directory.
02How do I install Session end in Cursor, Claude Code, or Codex?
Run npx skills add retieedra-profound/skills-marketing-library --skill session-end in the project where you want it, then ask your agent for the skill by name. The --skill flag installs only Session end, not every skill in the repository.
03What does the Session end SKILL.md cover?
The SKILL.md includes sections on How this skill relates to a memory plugin, Step 1: Gather curated session context, Step 2: Daily log (primary record), Step 3: Thin local pointer, Step 4: Update agent state and Step 5: Memory files + knowledge-base mirror. Read the full text on this page or in plain markdown at /resources/agent-skills/wispr-flow-claude-skills--wispr-flow-session-end--12sjnn6/raw.
04Which agents can use Session end?
Session end installs with the cross-agent skills installer (npx skills add), which sets it up for agents such as Claude Code, Cursor and Codex. Each agent loads the SKILL.md when you ask for the skill by name.
05What license is Session end under?
The retieedra-profound/skills-marketing-library repository declares the MIT license. Read the license file in the repository before you copy or redistribute the skill.
06Is Session end free to use?
Reading, copying and installing Session end costs nothing: it is published in a public GitHub repository under the MIT license. Reuse and redistribution follow that license.
07Where is the source of Session end?
Session end comes from https://github.com/retieedra-profound/skills-marketing-library. The SKILL.md is at https://github.com/retieedra-profound/skills-marketing-library/blob/HEAD/wisprflow/session-end/SKILL.md. The author is retieedra-profound.
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ATTRIBUTION
Session end was written by its original authors and is published in retieedra-profound/skills-marketing-library (opens GitHub in a new tab) under the MIT license. All rights stay with the original authors.
100xLab did not write, maintain or endorse this skill. This page lists it for discovery. Repo stars belong to the source repository, not to the skill. Review any skill before you install it.