Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/gracefullight/docusaurus-plugins/oma-recapnpx skills add gracefullight/docusaurus-plugins --skill oma-recapgit clone --depth 1 https://github.com/gracefullight/docusaurus-pluginsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/gracefullight/docusaurus-plugins/oma-recap)<a href="https://agentmods.dev/skills/gracefullight/docusaurus-plugins/oma-recap"><img src="https://agentmods.dev/badge/skills/gracefullight/docusaurus-plugins/oma-recap.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00045 | $0.02229 |
| Opus 5 | $0.00023 | $0.01115 |
| Sonnet 5 | $0.00009 | $0.00446 |
| Haiku 4.5 | $0.00005 | $0.00223 |
Grade A, and why
oma-recap scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
92% identical to oma-recap — 22 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 236 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 - Productivity metrics -> use
oma stats
Expected inputs
- Date, relative date, time window, or tool filter
- Conversation history available through
oma recap --jsonor fallback sources - Desired daily or multi-day recap scope
Expected outputs
- Markdown recap saved to
.agents/results/recap/{date}.mdor 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.yamlfor 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
- Resolve requested date or window.
- Collect normalized conversation history.
- Decide daily versus multi-day output structure.
Scenes
- PREPARE: Resolve time range and tool filters.
- ACQUIRE: Collect history through CLI or fallback.
- REASON: Group by content, infer themes/projects, decisions, artifacts, and tool-switching patterns.
- ACT: Write recap Markdown in the required format.
- VERIFY: Check TL;DR, grouping, language, and output path.
- FINALIZE: Save and display summary.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 236 lines · 45 tokens per session scan A a0a909047f61
oma-recap is a skill published in the GitHub repository gracefullight/docusaurus-plugins (22 stars, last pushed 2mo ago), licensed MIT. It adds 45 tokens to every session and 2,229 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to oma-recap, differing in 22 lines, and is treated as a copy.
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