OpenAgent is a self-hostable personal AI assistant that combines language models, document-based retrieval, and autonomous agent loops. It lets people connect model providers and their own knowledge bases, then have agents browse the web, run code, use computers, and call MCP-compatible tools.
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 skills add the-open-agent/openagent --skill session-logsgit clone --depth 1 https://github.com/the-open-agent/openagentWrote 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/the-open-agent/openagent/session-logs)<a href="https://agentmods.dev/skills/the-open-agent/openagent/session-logs"><img src="https://agentmods.dev/badge/skills/the-open-agent/openagent/session-logs/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/the-open-agent/openagent/session-logs"><img src="https://agentmods.dev/badge/skills/the-open-agent/openagent/session-logs.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 5.1 | $0.00020 | $0.01246 |
| Opus 5.5 | $0.00008 | $0.00498 |
| Sonnet 5.5 | $0.00004 | $0.00249 |
| Haiku 4.5 | $0.00002 | $0.00125 |
Grade A, and why
session-logs 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 1mo 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.
This is a copy
95% identical to session-logs — 14 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 — 152 lines — stays where its author put it; the contents beside it link to each section on GitHub.
session-logs
Search your complete conversation history stored in session JSONL files. Use this when a user references older/parent conversations or asks what was said before.
Trigger
Use this skill when the user asks about prior chats, parent conversations, or historical context that isn't in memory files.
Location
Session logs live under the active state directory:
$OPENCLAW_STATE_DIR/agents/<agentId>/sessions/ (default: ~/.openclaw/agents/<agentId>/sessions/).
Use the agent=<id> value from the system prompt Runtime line.
sessions.json- Index mapping session keys to session IDs<session-id>.jsonl- Full conversation transcript per session
Structure
Each .jsonl file contains messages with:
type: "session" (metadata) or "message"timestamp: ISO timestampmessage.role: "user", "assistant", or "toolResult"message.content[]: Text, thinking, or tool calls (filtertype=="text"for human-readable content)message.usage.cost.total: Cost per response
Common Queries
List all sessions by date and size
AGENT_ID="<agentId>"
SESSION_DIR="${OPENCLAW_STATE_DIR:-$HOME/.openclaw}/agents/$AGENT_ID/sessions"
for f in "$SESSION_DIR"/*.jsonl; do
date=$(head -1 "$f" | jq -r '.timestamp' | cut -dT -f1)
size=$(ls -lh "$f" | awk '{print $5}')
echo "$date $size $(basename $f)"
done | sort -r
Find sessions from a specific day
AGENT_ID="<agentId>"
SESSION_DIR="${OPENCLAW_STATE_DIR:-$HOME/.openclaw}/agents/$AGENT_ID/sessions"
for f in "$SESSION_DIR"/*.jsonl; do
head -1 "$f" | jq -r '.timestamp' | grep -q "2026-01-06" && echo "$f"
done
Extract user messages from a session
jq -r 'select(.message.role == "user") | .message.content[]? | select(.type == "text") | .text' <session>.jsonl
Search for keyword in assistant responses
jq -r 'select(.message.role == "assistant") | .message.content[]? | select(.type == "text") | .text' <session>.jsonl | rg -i "keyword"
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.
- 1mo ago First seen · 152 lines · 20 tokens per session scan A 8db62f0b1eb2
session-logs is a skill published in the GitHub repository the-open-agent/openagent (5,701 stars, last pushed yesterday), licensed Apache-2.0. It adds 20 tokens to every session and 1,246 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 95% identical to session-logs, differing in 14 lines, and is treated as a copy.
Other skills, from other repositories
documents
Create, read, edit, review, comment on, sanitize, render, and validate Microsoft Word DOCX documents. Use whenever a DOCX or Word document is a primary input or deliverable, including reports, proposals, forms, templates, tracked revisions, comments, tables, images, headers, footers, and style-preserving edits.
Read, create, inspect, merge, split, rotate, encrypt, fill, and validate PDF files. Use when the user asks to work with a PDF or convert supported Markdown into a polished PDF in AstrBot.
skill-creator
Create, revise, and validate AstrBot Skills built around SKILL.md instruction bundles. Use when the user asks to create, scaffold, improve, package, or review a Skill for an AstrBot workspace, local installation, or plugin.
spreadsheets
Create, read, edit, analyze, convert, chart, and validate spreadsheet files including XLSX, XLSM, XLS, CSV, and TSV. Use when a spreadsheet is a primary input or deliverable, or when tabular data must remain editable and auditable in workbook form.
session-rag-eval
Run and debug Chatbox session attachment RAG model evaluation with synthetic and real long-file fixtures.
self_awareness
Ground self-referential answers in the agent's actual filesystem. Use when the user asks who the agent is, what it can do, what it knows, what it remembers, what it has built, what services are connected, or what rules it follows.