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/m-t-d-n/agentmemory-codex-windows/recapnpx skills add M-T-D-N/agentmemory-codex-windows --skill recapgit clone --depth 1 https://github.com/M-T-D-N/agentmemory-codex-windowsWrote 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/m-t-d-n/agentmemory-codex-windows/recap)<a href="https://agentmods.dev/skills/m-t-d-n/agentmemory-codex-windows/recap"><img src="https://agentmods.dev/badge/skills/m-t-d-n/agentmemory-codex-windows/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 | $0.00058 | $0.00535 |
| Opus 5 | $0.00029 | $0.00267 |
| Sonnet 5 | $0.00012 | $0.00107 |
| Haiku 4.5 | $0.00006 | $0.00053 |
Grade A, and why
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 5d ago.
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.
What it actually says
The user wants a recap. Time window args: $ARGUMENTS
Quick start
memory_sessions { "project": "my-project", "limit": 30 }
Then per surviving session: memory_recall { "project": "my-project", "query": "<top concepts>", "limit": 3 }.
Expected output:
2026-06-07
7f3a9c2 · "Auth refresh rework" · 14 obs · completed
- [8] Rotate refresh tokens on every use
3 sessions across 2 days, 41 observations.
Why
Only summarize sessions and observations the tools returned. An empty window is a real answer, not a prompt to invent activity.
Workflow
- Parse
$ARGUMENTS:today= current local date;this week= last 7 days;last <n>or bare numeric = most recent N; empty =last 10. - Resolve the exact registered project and call
memory_sessionswith thatproject. Apply the window and sort bystartedAtdescending. - Group survivors by local calendar date (YYYY-MM-DD).
- Per session list id (first 8), title or first prompt, observation count,
status. Indent 2-3 highlights (importance >= 7) from
memory_recall. - End with "N sessions across M days, K observations."
Anti-patterns
WRONG: window is empty, so you summarize "a productive week of auth work" from memory of the conversation.
RIGHT: "No sessions in the last 7 days for this project."
Checklist
- Window parsed correctly from the argument.
- Sessions came from the exact registered project.
- Highlights come from
memory_recall, not paraphrase. - Totals line reflects the actual counts shown.
See also
handoff,session-history,recall: same session data, different lens.
Troubleshooting
See ../_shared/TROUBLESHOOTING.md if memory_sessions or memory_recall is not available.
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.
- 5d ago First seen · 64 lines · 58 tokens per session scan A dfc336586c3d
recap is a skill published in the GitHub repository M-T-D-N/agentmemory-codex-windows (2 stars, last pushed 5d ago), licensed Apache-2.0. It adds 58 tokens to every session and 535 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
engraphis-memory
Give the agent durable, scoped, explainable memory across sessions and repositories through the Engraphis MCP tools. Use when you learn a convention, decision, bug cause/fix, or user preference worth keeping; when prior context would help before you answer or act (to avoid re-asking or re-deriving); when asked "why is…
day-log
Quick timestamped log entry — no conversation, just capture.
period-summary
Period summary (w/m/q/y) — reflect on the period, score it, close it out.
day-start
Day briefing — deliver context, capture what's happened, get moving.
slm-recall
Search and retrieve facts, decisions, and past context from SuperLocalMemory. Use when the user asks to recall, find, search, or "what did we decide/say about X". Triggers multi-channel semantic retrieval with reranking; always call before storing anything new.
slm-remember
Capture durable facts, decisions, constraints, and gotchas into SuperLocalMemory. Use when the user says "remember that", "save this decision", "note this constraint", or when a session produces a conclusion worth persisting across sessions. Always recall first to avoid duplicates.