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 allenhutchison/obsidian-gemini --skill recall-sessionsgit clone --depth 1 https://github.com/allenhutchison/obsidian-geminiWrote 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/allenhutchison/obsidian-gemini/recall-sessions)<a href="https://agentmods.dev/skills/allenhutchison/obsidian-gemini/recall-sessions"><img src="https://agentmods.dev/badge/skills/allenhutchison/obsidian-gemini/recall-sessions/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/allenhutchison/obsidian-gemini/recall-sessions"><img src="https://agentmods.dev/badge/skills/allenhutchison/obsidian-gemini/recall-sessions.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 | $0.00040 | $0.00628 |
| Opus 5.5 | $0.00016 | $0.00251 |
| Sonnet 5.5 | $0.00008 | $0.00126 |
| Haiku 4.5 | $0.00004 | $0.00063 |
Grade A, and why
recall-sessions 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 2mo 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.
How it starts
The opening of the file, as written. The whole thing — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Recall Past Sessions
Find and retrieve past agent conversations using the recall_sessions tool. This enables recalling prior discussions, decisions, and context from earlier sessions.
When to Use
Use this skill when the user:
- Asks "what did we discuss about..." or "last time we talked about..."
- Wants to resume or continue previous work
- References a decision or conversation from a past session
- Asks "have we worked on..." or "do you remember when..."
- Needs context from a prior session about a specific file or project
How to Use
Call the recall_sessions tool with these parameters:
query(optional) — Search term to match against session titles (case-insensitive substring match).filePath(optional) — Find sessions that accessed a specific file (e.g.,notes/meeting.md).project(optional) — Find sessions linked to a specific project name.limit(optional) — Maximum results to return. Default is 10, maximum is 50.
At least one of query, filePath, or project should be provided for meaningful results.
Search Strategies
- Topic search — Use
querywhen the user asks about a discussion topic:recall_sessions(query="refactoring") - File-based recall — Use
filePathwhen the user references a specific file:recall_sessions(filePath="projects/website-redesign.md") - Project recall — Use
projectwhen the user mentions a project:recall_sessions(project="website-redesign") - Combined search — Use multiple parameters to narrow results:
recall_sessions(query="API design", project="backend")
Progressive Disclosure
The tool returns session summaries only — title, date, files accessed, project linkage, and a historyPath.
To see the full conversation from a past session, use read_file on the returned historyPath. This two-step approach avoids loading unnecessary conversation data.
Workflow
- Call
recall_sessionsto find relevant sessions - Review the returned summaries with the user
- Use
read_fileon thehistoryPathof the session(s) the user is interested in - Summarize or reference the relevant parts of the conversation
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.
- 2mo ago First seen · 57 lines · 40 tokens per session scan A e37a5f8c2216
recall-sessions is a skill published in the GitHub repository allenhutchison/obsidian-gemini (534 stars, last pushed today), licensed MIT. It adds 40 tokens to every session and 628 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-08-30.
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