session-log
Summarize the current conversation session and append results to the weekly agent-log. Use when user says "log this", "session log", "summarize this session", or asks to write results to the agent-log.
What this skill does
# Session Log
Summarize the current conversation and append to the weekly agent-log file.
## Output Location
`YYYY-wWW Agent Log.md`
Where `YYYY-wWW` is the ISO week of today's date. Calculate with:
```bash
date +%Y-w%V
```
## Format Rules
1. **Reverse chronological order** — newest day on top
2. **One `##` heading per day** — format: `## YYYY-MM-DD`
3. **Bullets, not subheadings** — inside a day, use plain bullet `- Topic title` as topic separator, not `###`. No bold, no formatting on topic lines.
4. **Details as nested bullets** — one sentence per sub-bullet, can nest if needed for details. No bold. Nesting uses a TAB.
5. **CHUNK markers** — if a topic produced a reusable output (a plan, a summary, a framework, a draft message), add nested bullet: `CHUNK: <descriptive title>`
6. **No explanatory text** — no intros, no "in this session we discussed", no meta-commentary
7. **Append, don't replace** — when a day heading already exists, add new bullets under it without removing existing content
## Example
```markdown
## 2026-02-28
- Analiza strategii X vs framework Y + moje obserwacje
- Strategia jest silna w A i B, słaba w C — brakuje fosy i horyzontu 3+lat.
- Naming produktu "Rescue" implikuje że kupujący jest ofiarą, co blokuje referencje.
- Anty-segment nie jest sprawdzalny z zewnątrz — to opis doświadczenia, nie filtr.
- CHUNK: 3-zdaniowe podsumowanie strategii
- CHUNK: Scorecard po 6 osiach
- Decyzja: follow-up z klientem
- Nie wysyłać feedbacku (nie prosił), wysłać link do artykułu jako wartość bez CTA.
```
## Step-by-Step Workflow
### 1. Determine the target file
```bash
WEEK=$(date +%Y-w%V)
```
Target: `${WEEK} agent-log.md`
### 2. Read existing file (if any)
The file may already have entries from earlier sessions this week. Read it first to avoid overwriting.
### 3. Review the full conversation and determine dates
Scan the entire conversation history. Identify:
- **Topics** — distinct subjects discussed (group related back-and-forth into one topic)
- **Decisions** — what was decided or concluded
- **Outputs** — any reusable artifacts (summaries, plans, draft messages, frameworks, scorecards)
**Date attribution:** A conversation may span multiple days. Determine the correct date for each topic using these signals (in priority order):
1. **System reminders** about date changes ("The date has changed. Today's date is now...")
2. **File names** with dates (e.g., `2026-02-26 client email.md` was created/discussed on that date)
3. **Context from session summaries** — if the session was continued from a compacted conversation, the summary may mention which work happened when
4. **Default** — if no date signal exists, use today's date
Group topics by their actual date, not just "today."
### 4. Write the log entry
For each date that has topics:
- If that date's heading (`## YYYY-MM-DD`) already exists in the file, append new topics under it
- If the date heading is not yet in the file, add it in the correct reverse-chronological position
- If the file doesn't exist, create it
**Cross-week dates:** If a topic belongs to a date in a different ISO week than the target file, note this to the user and ask whether to add it to the current file or the correct week's file.
**Condensation rules:**
- Multiple related exchanges → one topic bullet
- Back-and-forth refinement → only the final conclusion matters
- If user edited/corrected something → use the corrected version, ignore earlier drafts
**Append rules:**
- If today's `## YYYY-MM-DD` heading already has bullets, add new ones at the end — never delete or rewrite existing bullets
- If a topic from this session overlaps with an existing bullet, add the new details as additional nested bullets under a new topic bullet — don't merge into existing text
### 5. Confirm
Tell the user what was logged (topic titles only, one line).
## Important Notes
- The user may have a preferred topic title style — if they edited a previous entry, match that style
- Keep bullets ruthlessly short — one sentence, no semicolons chaining multiple thoughts
- CHUNKs reference outputs that exist in the conversation, not in files — they're bookmarks for the user to find later
- Do NOT include the full chunk content in the log — just the marker
- Language: match the language the conversation was conducted in (use the user's language)
Related in AI Agents
skill-development
IncludedComprehensive meta-skill for creating, managing, validating, auditing, and distributing Claude Code skills and slash commands (unified in v2.1.3+). Provides skill templates, creation workflows, validation patterns, audit checklists, naming conventions, YAML frontmatter guidance, progressive disclosure examples, and best practices lookup. Use when creating new skills, validating existing skills, auditing skill quality, understanding skill architecture, needing skill templates, learning about YAML frontmatter requirements, progressive disclosure patterns, tool restrictions (allowed-tools), skill composition, skill naming conventions, troubleshooting skill activation issues, creating custom slash commands, configuring command frontmatter, using command arguments ($ARGUMENTS, $1, $2), bash execution in commands, file references in commands, command namespacing, plugin commands, MCP slash commands, Skill tool configuration, or deciding between skills vs slash commands. Delegates to docs-management skill for official documentation.
reprompter
IncludedTransform messy prompts into well-structured, effective prompts — single or multi-agent. Use when: "reprompt", "reprompt this", "clean up this prompt", "structure my prompt", rough text needing XML tags and best practices, "reprompter teams", "repromptception", "run with quality", "smart run", "smart agents", multi-agent tasks, audits, parallel work, anything going to agent teams. Don't use when: simple Q&A, pure chat, immediate execution-only tasks. See "Don't Use When" section for details. Outputs: Structured XML/Markdown prompt, quality score (before/after), optional team brief + per-agent sub-prompts, agent team output files. Success criteria: Single mode quality score ≥ 7/10; Repromptception per-agent prompt quality score 8+/10; all required sections present, actionable and specific.
adaptive-compaction
IncludedAdaptive add-on policy and recovery layer that decides WHEN to compact, prune, snapshot, or fork -- replacing fixed-percent auto-compaction across Claude Code, Codex, and MCP-capable hosts. Trigger on auto-compact timing or damage: "when should I compact", "is it safe to compact now or start a fresh session", "auto-compact fires too early/mid-task", "switching to an unrelated task but the window still has space", "context rot", "answers get worse the longer the session runs", "the agent forgot the plan or my decisions after it summarized", "add a layer on top that manages context without changing the agent", raising autoCompactWindow to give the policy room, or installing/tuning a cross-tool compaction policy or PreCompact hook -- even when "compaction" is never said but the problem is context-window pressure or post-summarization memory loss. Do NOT use to summarize a conversation, build RAG, write a summarization prompt (decides WHEN not HOW), or answer max-context-length trivia.
agent-skill-creator
IncludedCreate cross-platform agent skills from workflow descriptions. Activates when users ask to create an agent, automate a repetitive workflow, create a custom skill, or need advanced agent creation. Triggers on phrases like create agent for, automate workflow, create skill for, every day I have to, daily I need to, turn process into agent, need to automate, create a cross-platform skill, validate this skill, export this skill, migrate this skill. Supports single skills, multi-agent suites, transcript processing, template-based creation, interactive configuration, cross-platform export, and spec validation.
llm-wiki
IncludedUse when building or maintaining a persistent personal knowledge base (second brain) in Obsidian where an LLM incrementally ingests sources, updates entity/concept pages, maintains cross-references, and keeps a synthesis current. Triggers include "second brain", "Obsidian wiki", "personal knowledge management", "ingest this paper/article/book", "build a research wiki", "compound knowledge", "Memex", or whenever the user wants knowledge to accumulate across sessions instead of being re-derived by RAG on every query.
skill-master
IncludedAgent Skills authoring, evaluation, and optimization. Create, edit, validate, benchmark, and improve skills following the agentskills.io specification. Use when designing SKILL.md files, structuring skill folders (references, scripts, assets), ingesting external documentation into skills, running trigger evals, benchmarking skill quality, optimizing descriptions, or performing blind A/B comparisons. Keywords: agentskills.io, SKILL.md, skill authoring, eval, benchmark, trigger optimization.