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Oma Recap

OrganizationPopular
first-fluke
oma-recap

Summarize AI conversation histories for a specified date or period. Use for daily work recaps and cross-tool activity summaries.

Overview

Publisherfirst-fluke
Repositoryoh-my-agent
Skill nameoma-recap
Stars
1.3K
Forks
149
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by first-fluke on GitHub. Read the source before you install it.

Installation

Install the Oma Recap AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/first-fluke/oh-my-agent.git /tmp/oh-my-agent
mkdir -p .claude/skills
cp -r /tmp/oh-my-agent/skills/oma-recap .claude/skills/oma-recap
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Oma Recap in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Oma Recap on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Oma Recap is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

AI Tool Conversation History Summary

Analyze AI tool conversation histories for a given period and generate themed work summaries.

Scheduling

Goal

Collect AI tool conversation history for a date or window and synthesize it into a themed, project-oriented recap with saved Markdown output.

Intent signature

  • User asks for daily recap, weekly/monthly summary, standup notes, work log, tool usage pattern, or AI conversation history analysis.
  • User wants conversation histories grouped by work content rather than raw chronological logs.

When to use

  • Summarizing a day or period of work activity
  • Understanding the overall flow of work across multiple AI tools
  • Analyzing tool-switching patterns between sessions
  • Preparing daily standups, weekly retros, or work logs

When NOT to use

  • Git commit-based code change retrospective -> use oma retro
  • Real-time agent monitoring -> use oma dashboard terminal
  • Productivity metrics -> use oma stats get

Expected inputs

  • Date, relative date, time window, or tool filter
  • Conversation history available through oma recap --json or fallback sources
  • Desired daily or multi-day recap scope

Expected outputs

  • Markdown recap saved to .agents/results/recap/{date}.md or range filename
  • TL;DR, overview, themes/projects, miscellaneous or side projects, and tool usage patterns
  • User-facing summary in configured response language

Dependencies

  • oma recap --json
  • Optional Claude fallback history at ~/.claude/history.jsonl
  • .agents/oma-config.yaml for language behavior

Control-flow features

  • Branches by date resolution, window length, available tool history, and daily vs multi-day output shape
  • Reads local history data and writes Markdown recap files
  • Groups by content, not by tool

Structural Flow

Entry

  1. Resolve requested date or window.
  2. Collect normalized conversation history.
  3. Decide daily versus multi-day output structure.

Scenes

  1. PREPARE: Resolve time range and tool filters.
  2. ACQUIRE: Collect history through CLI or fallback; retain completion evidence where available.
  3. REASON: Group by content and classify each item as requested, in progress, or completed from its evidence.
  4. ACT: Write recap Markdown in the required format.
  5. VERIFY: Check that every completion claim has direct evidence, then check grouping, language, and output path.
  6. FINALIZE: Save and display summary.

Transitions

  • If no date is specified, use today via --date (bare --window is a rolling window ending now, not calendar-aligned).
  • If window is 3 days or longer, group by project instead of day chronology.
  • If CLI is unavailable, use Claude fallback only and report scope limits.
  • If tasks are under threshold, group them into Miscellaneous or Side Projects.

Failure and recovery

  • If history is unavailable, report missing source and requested range.
  • If timestamps are ambiguous, use configured timezone and state assumption.
  • If extracted data is sparse, produce a concise recap and note limited coverage.

Exit

  • Success: recap file exists and summary is displayed.
  • Partial success: missing tools/history or fallback-only coverage is explicit.

Logical Operations

Actions

ActionSSL primitiveEvidence
Resolve date/windowINFERNatural-language date rules
Collect historyCALL_TOOLoma recap --json or jq fallback
Read extracted recordsREADConversation history
Group and classify themes/projectsINFERTime/content rules plus prompt, progress, completion, receipt, or artifact evidence
Validate output shapeVALIDATEDaily or multi-day template
Write recapWRITE.agents/results/recap/
Report summaryNOTIFYDisplayed recap

Tools and instruments

  • oma recap --json
  • jq fallback for Claude history
  • Markdown output templates

Canonical command path

bash
oma recap --date YYYY-MM-DD --json
oma recap --window 7d --json
oma recap --json  # rolling last 24h, not "today"

Resource scope

ScopeResource target
LOCAL_FSConversation history and recap output files
PROCESSoma recap, jq, date commands
USER_DATAConversation prompts and project activity
MEMORYTheme grouping and summary notes

Preconditions

  • Requested time range can be resolved.
  • At least one history source is available.

Effects and side effects

  • Writes recap Markdown under .agents/results/recap/.
  • Reads local conversation history data.

Guardrails

  1. Evidence status: A prompt alone proves a request, not a result. Mark work completed only with an explicit completion/result message, a receipt, or an artifact that supports the stated outcome. Mark it in progress with progress evidence; otherwise call it requested. Do not infer completion from a tool invocation or elapsed time.
  2. TL;DR required: Top 3 supported outcomes. Use "completed" only when the evidence-status rule permits it; otherwise summarize requested or in-progress work plainly. Project name + status/outcome. No tool names or unnecessary detail.
  3. Overview: After TL;DR, describe the flow. Start with "I" as subject and preserve evidence status.
  4. Daily: themes by time block (15+ min). Rest goes to "Miscellaneous".
  5. Multi-day (3d+): sections by project, ordered by activity. Read like a sprint report, not a daily log.
  6. 2-4 bullets per theme/project: Concise essentials only. Don't enumerate every step.
  7. Themes by content: Group by actual work, not by tool.
  8. Time range (daily only): (AM/PM/Evening HH:MM~HH:MM). AM: 12:00, PM: 12:0018:00, Evening: 18:00~.
  9. Save results: Write markdown to .agents/results/recap/.
  10. Response language: Follows language setting in .agents/oma-config.yaml if configured.
  11. No em dashes: Use commas, periods, or parentheses instead of (em dash).

Process

1. Resolve Date

Determine the target date or window from the user's natural language input. Default is today.

Resolution rules:

  • Relative day references (today, yesterday, day before yesterday, etc.) → calculate --date YYYY-MM-DD
  • Specific date mentions (month + day, or full date) → convert to --date YYYY-MM-DD
  • Relative weekday references (last Monday, this Friday, etc.) → calculate the date
  • Period references (this week, last 3 days, past 2 weeks, etc.) → convert to --window Nd
  • No date specified → today, resolved to --date YYYY-MM-DD (bare --window 1d is a rolling 24-hour window ending now, not the calendar day)
  • The CLI caps windows at 30 days (longer values are trimmed with a warning) — when a requested period gets capped, say so in the recap

2. Collect Data

Extract normalized conversation history via CLI.

bash
# Today (calendar day, all tools)
oma recap --date $(date +%F) --json

# Last 24 hours (rolling window ending now)
oma recap --json

# Time window (rolling, ends now; capped at 30d)
oma recap --window 7d --json

# Specific date
oma recap --date 2026-04-10 --json

# Tool filter (supported: grok, claude, codex, gemini, qwen, cursor, antigravity)
oma recap --tool claude,codex --json

Fallback when CLI is not installed: process Claude history only via inline jq:

bash
# Uses the system timezone; export TZ=<zone> first to override, and state the
# timezone assumption in the recap (per the Failure and recovery rules).
TARGET_DATE=$(date +%Y-%m-%d)
# macOS/BSD date:
start_ts=$(date -j -f "%Y-%m-%d %H:%M:%S" "${TARGET_DATE} 00:00:00" +%s)000
# Linux/GNU date alternative:
# start_ts=$(date -d "${TARGET_DATE} 00:00:00" +%s)000
end_ts=$((start_ts + 86400000))

jq -r --argjson start "$start_ts" --argjson end "$end_ts" '
  select(.timestamp >= $start and .timestamp < $end and .display != null and .display != "") |
  {
    time: (.timestamp / 1000 | localtime | strftime("%H:%M")),
    project: (.project | split("/") | .[-1]),
    prompt: (.display | gsub("\n"; " ") | if length > 150 then .[0:150] + "..." else . end)
  }
' ~/.claude/history.jsonl

3. Theme Analysis and Grouping

Read all extracted data and analyze with the following criteria:

Grouping rules:

  • Only classify as a separate theme if the work spans 15+ minutes (based on timestamp gaps and prompt count)
  • Merge consecutive prompts on the same topic into one theme
  • Collect sub-15-minute tasks into a "Miscellaneous" section
  • Group by work content, not by tool

Cross-tool analysis:

  • Track workflow when multiple tools are used in the same time window
  • Example: "Designed in Antigravity -> Implemented in Claude -> Reviewed in Codex"
  • Derive insights from tool-switching patterns

Extract from each theme:

  • Core work performed
  • Key decisions made
  • Tool combinations used
  • Artifacts produced (docs, code, config, etc.)

4. Output Format

Save results to .agents/results/recap/{date}.md and display simultaneously.

Use the markdown templates in resources/output-formats.md:

  • Daily format (1d or specific date): TL;DR → Overview → time-blocked themes → Miscellaneous → Tool Usage Patterns.
  • Multi-day format (3d+): project-driven sprint-report structure with Side Projects for small (<30 prompts) work; follow the multi-day grouping rules in the same file.

Response language follows language setting in .agents/oma-config.yaml.

5. Save Results

Save to .agents/results/recap/{date}.md. For window ranges, use {start-date}~{end-date}.md format.

bash
# Example paths
.agents/results/recap/2026-04-12.md
.agents/results/recap/2026-04-06~2026-04-12.md

References

  • Output format templates: resources/output-formats.md
  • Recap CLI: oma recap --json
  • Output directory: .agents/results/recap/
  • Language config: .agents/oma-config.yaml
  • Claude fallback history: ~/.claude/history.jsonl

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Oma Recap AI skill do?

Summarize AI conversation histories for a specified date or period. Use for daily work recaps and cross-tool activity summaries.

Why use Oma Recap on TypingMind?

Because you install it once and use it with any model. Oma Recap is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Oma Recap in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/first-fluke/oh-my-agent/tree/main/skills/oma-recap. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Oma Recap?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Oma Recap?

As many as you like. As long as a model supports skills, you can use Oma Recap with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Oma Recap AI skill free?

Yes. It is published on GitHub by first-fluke under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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