Hermes Agent is an AI assistant that learns from its use by creating and improving skills, retaining knowledge, searching past conversations, and adapting to its users. It is for people who want to run an agent through a terminal or messaging platforms while connecting it to different AI models and scheduled tasks.
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 NousResearch/hermes-agent --skill weekly-review-planninggit clone --depth 1 https://github.com/NousResearch/hermes-agentWrote 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/nousresearch/hermes-agent/weekly-review-planning)<a href="https://agentmods.dev/skills/nousresearch/hermes-agent/weekly-review-planning"><img src="https://agentmods.dev/badge/skills/nousresearch/hermes-agent/weekly-review-planning/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/nousresearch/hermes-agent/weekly-review-planning"><img src="https://agentmods.dev/badge/skills/nousresearch/hermes-agent/weekly-review-planning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk warn
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 80 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00017 | $0.00861 |
| Opus 5 | $0.00009 | $0.00430 |
| Sonnet 5 | $0.00003 | $0.00172 |
| Haiku 4.5 | $0.00002 | $0.00086 |
Grade A, and why
weekly-review-planning 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 8d 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- weekly-review-planning — 100% identical, 0 lines differ
- weekly-review-planning — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Weekly Review and Planning
Run a bounded weekly reset across the user's chosen systems. This is a concrete recurring task, not a generic productivity methodology — the weekly-review Automation Blueprint schedules it as a cron job.
When to Use
- "Run my weekly review."
- "What did I commit to and what is slipping?"
- "Plan next week from my calendar, tasks, and notes."
- "Find stale projects and waiting items."
- A cron tick fires for a scheduled weekly review.
Don't use for: daily briefs (see the google-workspace daily-brief reference) or single-inbox triage (email-inbox-triage).
Procedure
1. Set systems and window
Confirm timezone, review period, planning horizon, authoritative task/project store, calendars, inboxes, and allowed writes. Default to recommendations/drafts, not mutations. Done when source-of-truth conflicts have a declared winner.
2. Review calendar evidence
Load google-workspace or the relevant calendar connector. Inspect the completed week for meetings and commitments, then the next 1-2 weeks for deadlines, travel, preparation, and capacity. Capture follow-ups implied by past events and conflicts ahead. Done when both retrospective and horizon are covered.
3. Clear capture inboxes
Review the task inbox, notes (obsidian, notion), flagged email (email-inbox-triage owns thread-level triage), and other declared capture points. Convert each item to next action, project, waiting, scheduled, someday, reference, archive, or delete proposal. Do not mutate until scope is approved. Done when remaining unprocessed items are counted and stated.
4. Reconcile active projects
For each project identify desired outcome, next action, owner, deadline, blocker, last meaningful activity, and source link. Flag projects with no next action, missed dates, duplicate records, or contradictory status. Done when every active project is actionable or explicitly paused.
5. Review waiting and commitments
Find promises made by the user and items owed by others. Propose follow-ups with dates and channels. Do not infer that silence means completion. Done when each waiting item has an owner and next review/follow-up date.
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.
- 8d ago First seen · 82 lines · 17 tokens per session scan A a689257facb9
weekly-review-planning is a skill published in the GitHub repository NousResearch/hermes-agent (244,603 stars, last pushed today), licensed MIT. It adds 17 tokens to every session and 861 once invoked, about $0.0001 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 2026-09-03.
Other skills, from other repositories
content-hash-cache-pattern
Cache expensive file processing results using SHA-256 content hashes — path-independent, auto-invalidating, with service layer separation.
keybindings-help
Use when the user wants to customize keyboard shortcuts, rebind keys, add chord bindings, or modify /.claude/keybindings.json. Examples: "rebind ctrl+s", "add a chord shortcut", "change the submit key", "customize keybindings".
fewer-permission-prompts
Scan your transcripts for common read-only Bash and MCP tool calls, then add a prioritized allowlist to project .claude/settings.json to reduce permission prompts.
rubyllm
Build and maintain Ruby or Rails applications with the RubyLLM AI framework. Use for chats, agents, tools, structured output, media generation, transcription, OCR, moderation, embeddings, reranking, Rails integration, and RubyLLM upgrades; not for contributing to the framework itself.
build-product-and-tool-interfaces
A guide for designing and building complete interfaces for products and professional tools that people use repeatedly to change objects and reach checkable results. It covers workbenches, task workflows, editing tools, analysis tools, monitoring consoles, and file or asset processors.
shogun-model-switch
A live-switching tool for changing which command-line AI agent, model, and reasoning mode is running. It updates settings, restarts the agent, and refreshes the displayed session information.