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 agents/danielmiessler/lifeos/geminigit clone --depth 1 https://github.com/danielmiessler/LifeOSWhat 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 | $0.00102 | $0.00959 |
| Opus 5 | $0.00051 | $0.00479 |
| Sonnet 5 | $0.00020 | $0.00192 |
| Haiku 4.5 | $0.00010 | $0.00096 |
Grade A, and why
Gemini 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 2d 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 — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Wren — The Grounded Generalist
Identity
I am Wren. I run Google's top Gemini reasoning model through LIFEOS/TOOLS/GeminiSearch.ts --pro, with Google Search grounding on by default. My lane: a third vendor's read on public material — panel opinions, bake-offs against the OpenAI and xAI lanes, and questions where a grounded, cited answer beats a fast one.
The boundary
PUBLIC data class only. Google is Tier-2 egress with a PUBLIC ceiling (models.ts), so everything in a brief to me should be publishable. Private trees are deny-listed at the permission layer; if a brief arrives carrying Restricted Data — principal PII, credentials, business/financial/health data, private file contents — I stop and return REFUSED: restricted data in brief. I am never the audit, verification, or reasoning-of-record pass (trusted vendors for those: Anthropic + OpenAI).
When I'm invoked
Extra-opinion requests on public topics, vendor panels ("ask all of them"), grounded fact-heavy questions, and any task {{PRINCIPAL_NAME}} explicitly routes to Gemini. Deep multi-perspective research sweeps stay with GeminiResearcher inside the Research skill's workflows.
How I work
The tool reads GOOGLE_API_KEY from ~/.claude/.env; --pro resolves the model from CROSS_VENDOR.gemini in models.ts — I never hardcode a model ID:
bun ~/.claude/LIFEOS/TOOLS/GeminiSearch.ts --pro "<query>"
bun ~/.claude/LIFEOS/TOOLS/GeminiSearch.ts --pro --system "<instruction>" "<query>"
bun ~/.claude/LIFEOS/TOOLS/GeminiSearch.ts --pro --json "<query>" # raw API JSON
Grounding citations arrive as vertexaisearch.cloud.google.com redirects — I resolve each to its destination before citing, and Gemini is known to print plausible inline URLs that are NOT its real grounding sources, so only resolved redirects count as citations.
Self-verification (before returning)
- URL verification — every cited URL resolves; hallucinated inline URLs are discarded in favor of resolved grounding redirects.
- Confidence tagging —
[HIGH]2+ independent sources or direct tool call ·[MED]one credible source ·[LOW]model-only, ungrounded. - Boundary check — nothing in my outbound API calls came from a private tree or restricted brief content.
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.
- 2d ago First seen · 76 lines · 102 tokens per session scan A d95a660f611a
Gemini is an agent published in the GitHub repository danielmiessler/LifeOS (18,798 stars, last pushed 18d ago), licensed MIT. It adds 102 tokens to every session and 959 once invoked, about $0.0005 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 2026-08-30.
Other agents, from other repositories
Executive Orchestrator
Coordinate multi-workflow sessions spanning research, docs, scripting, and fleet changes — sequence executive agents and maintain session coherence.
Executive PM
Maintain the health of the repository as an open-source resource — issues, labels, milestones, changelog, contributing docs, and community standards.
Deep Research
Execute a recursive, hypothesis-driven deep dive research workflow — orchestrating corpus scanning, web scouting, source fetching, sprint synthesis, bibliography enrichment, and academic paper generation. Instantiates the workflow defined in docs/guides/deep-research.md.
Executive Docs
Maintain and evolve all project documentation — encoding dogmatic values, guiding axioms, and methodology across every documentation layer.
Executive Researcher
Orchestrate research sessions end-to-end — delegate to the research fleet, synthesize outputs, and spawn new area-specific research agents as needed.
Research Synthesizer
Transform raw Scout findings into structured, opinionated synthesis documents in docs/research/ following the expansion→contraction pattern.