automate-session
Analyze what was done in the current session and surface concrete ways the workflow could have been automated to remove the user from the loop.
Skill by tomzx
Chalk Labs
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Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/tomzx-automate-session
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
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About
Use cases: productivity
Reviews
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Permissions
What this skill can do and what it asks for, from its SKILL.md and code. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/automate-session
What's inside
- Skill name
automate-session- Description
- Analyze what was done in the current session and surface concrete ways the workflow could have been automated to remove the user from the loop. Use when the user asks /automate-session, "how could this be automated?", or "what could we automate from this session?".
- License
- MIT
- Instructions
- 138 lines in SKILL.md · view on GitHub
Files (1)
- SKILL.md
SKILL.md (first lines)
# Automation Opportunities Reflects on the current session and identifies which steps could have been handled autonomously, a scheduled agent, or a triggered hook, removing the user from the loop entirely or reducing their involvement to review-only. ## Prerequisites - A session with at least some completed work (conversation history, git changes, or both) ## Steps ### 1. Reconstruct the session workflow Build a chronological list of the steps taken during this session. Sources to draw from: - Conversation turns: what did the user ask, decide, or approve? - `git log --oneline` since session start: what changed? - File reads, writes, edits made during the session - Any external tool calls (GitHub, Slack, etc.) Produce a numbered step-by-step trace of the session as if writing a runbook someone else would follow. ### 2. Classify each step by automation potential For each step in the trace, assign one of three labels: | Label | Meaning | |---|---| | **Auto** | Could run fully autonomously with no human input — deterministic, low-risk, well-scoped | | **Review-gate** | An agent could execute it, but a human checkpoint (approve / reject) makes sense before or after | | **Human** | Requires human judgment, creative direction, or irreversible external action with unclear scope | A step qualifies as **Auto** if: - Its inputs are available programmatically (git state, file content, API response) - Its output is verifiable (tests pass, lint clean, diff is small and scoped) - Failure is detectable and recoverable without human intervention - It follows a pattern used repeatedly in prior sessions A step is **Review-gate** if it is automatable but touches shared state (pushes to a remote, sends a message, opens a PR) or produces output a human should sanity-check before it propagates. A step is **Human** if it involves priority trade-offs, novel design decisions, or communication requiring context only the user holds. ### 3. Identify the automation patterns Group the Auto and Review-gate steps into one or more named automation patterns. For each pattern, describe: - **What it would do**: the concrete actions it would take - **Trigger**: what event starts it (commit pushed, PR opened, cron schedule, file saved, manual `/skill`) - **Implementation path**: which agent primitive fits best - *Hook* — `PreToolUse`, `PostToolUse`, `Stop` hook in `settings.json` - *Skill* — a new `/skill-name` the user invokes once - *Scheduled agent* — a routine via `/schedule` that runs on a cron - *Background agent* — a long-running or triggered agent via `Agent(run_in_background: true)` - **Review gate** (if any): what the user would see and approve before the automation continues - **Risk / caveat**: what could go wrong and how it would be detected ### 4. Estimate the time savings For each automation pattern, estimate: - How many minutes this session spent on the steps it would cover - Whether this workflow recurs (daily / weekly / per-PR / ad-hoc) - Rough total minutes saved per week if automated ### 5. Prioritize Rank the patterns by: **impact × frequency ÷ implementation effort**. High-value candidates: high recurrence, low risk, existing skill or hook primitives map cleanly. Low-value candidates: one-off tasks, steps that are mostly thinking, or steps where an agent would need unavailable context. ### 6. Offer to implement For the top-ranked pattern(s), ask: > "Want me to implement [pattern name] now? > I can [create a skill / add a hook / set up a scheduled agent]." If the user says yes, implement it immediately using the appropriate primitive. If the user says no or wants to backlog it, append the suggestion to `~/notes/automation-backlog.md` (create if absent) with today's date and a one-line description. ## Output Format ```markdown ## Automation Opportunities ### Session Trace 1. <Step> — **Auto** / **Review-gate** / **Human** 2. ... --- ### Patterns #### [Pattern Name] - **What it does:** <description> - **Trigger:** <event> - **Implementation:** <Hook | Skill | Scheduled agent | Background agent> - **Review gate:** <what the user approves, or "none"> - **Risk:** <failure mode and detection> - **Time saved:** ~N min/session, recurs <frequency> → ~N min/week --- ### Priority Order 1. [Pattern A] — high impact, low effort, daily recurrence 2. [Pattern B] — medium impact, medium effort, weekly recurrence 3. ... --- ### Recommendation > [One sentence on which pattern to implement first and why.] ```
From tomzx/agents, MIT.
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Signals
- Stars
- 12
- Forks
- 1
- Open issues
- 16
- Last commit (this folder)
- Oct 7, 2026 (23 days ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/tomzx/agents
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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SkillAFreshness: A★ 12Claude Code +9 - 0.639score
communication-guidelines
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Shared guidelines for writing concise, high-signal outbound communication the agent posts or drafts on the user's behalf, covering GitHub PR and issue…
SkillAFreshness: A★ 12Claude Code +9 - 0.634score
audit-usability
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SkillAFreshness: A★ 12Claude Code +9 - 0.634score
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automate-session skill. Agent Mart. https://agentmart.dev/skills/tomzx-automate-session (last updated 2026-10-24)