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/pyros-projects/limitless/hivemindnpx skills add pyros-projects/limitless --skill hivemindgit clone --depth 1 https://github.com/pyros-projects/limitlessWrote 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/pyros-projects/limitless/hivemind)<a href="https://agentmods.dev/skills/pyros-projects/limitless/hivemind"><img src="https://agentmods.dev/badge/skills/pyros-projects/limitless/hivemind.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 | $0.00241 | $0.04904 |
| Opus 5 | $0.00120 | $0.02452 |
| Sonnet 5 | $0.00048 | $0.00981 |
| Haiku 4.5 | $0.00024 | $0.00490 |
Grade A, and why
hivemind scanned grade A with 1 finding 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 4d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s -m 5 "http://localhost:8888/search?q=test&format=json" -o /dev/null -w "%{http_code}" # web: expect 200 (403 = json format disabled — see web-playbook) How it starts
The opening of the file, as written. The whole thing — 380 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hivemind — Ask the Collective Brain
Overview
Orchestrates the twitter CLI (X), rdt CLI (Reddit), gh CLI
(GitHub), SearXNG (web), and the OpenAlex/arXiv APIs (papers) into a
disciplined evidence search: resolve venues, classify the chain, search
scoped, triage by relevance before engagement, deep-read where the
knowledge lives, synthesize answer-first with receipts. The collective
brain includes what people post (social), build (GitHub),
publish (papers), and write (web) — each venue carries an
evidence grade, and the brief always says which is which. Social is a
weak-evidence surface: good for friction, language, pointers,
sentiment; never factual validation. Papers and repo metadata are what
validation looks like inside the skill.
Hivemind is read-only. The CLIs can post, like, and reply; this skill never does.
Phase 0 — Preflight (always, before anything else)
Probe every venue the ask might touch. The result is the live venue set — an input to chain classification (Phase 2): never plan a chain over a tool that already failed its probe.
command -v twitter rdt gh
twitter status; rdt status
gh auth status # SAY which account is active — recipes that
# touch specific orgs/repos depend on it
curl -s -m 5 "http://localhost:8888/search?q=test&format=json" -o /dev/null -w "%{http_code}" # web: expect 200 (403 = json format disabled — see web-playbook)
curl -s -m 5 "https://api.openalex.org/works?search=test&per-page=1" -o /dev/null -w "%{http_code}" # papers
- Missing/broken tool → offer the 10-second fix FIRST:
uv tool install twitter-cli/uv tool install rdt-cli/ docker start for SearXNG (web-playbook has the recipe). Ask via AskUserQuestion if interactive. Non-interactive runs: the fix offer leads the reply — it is stated BEFORE any degraded or proxy work is performed, not appended to the result. - Do not build scraping workarounds (proxies, mirror instances, raw JSON endpoints, search-engine site-scoping as a venue substitute) before the install offer has been made.
- Unauthenticated → point at
rdt login/ twitter's auth flow. - A venue dead and user opts not to fix → degradation rules in "Tool failure semantics" below. Degrade, never block — but named venues are never silently substituted.
What ships with it
8 files 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.
- 4d ago First seen · 380 lines · 241 tokens per session scan A 2562e11fd708
hivemind is a skill published in the GitHub repository pyros-projects/limitless (9 stars, last pushed 21d ago), licensed MIT. It adds 241 tokens to every session and 4,904 once invoked, about $0.0012 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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skill-trigger-tester
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dag-recall
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memory-dag-compactor
Builds hierarchical summary DAGs from MEMORY.md with depth-aware prompts — leaf summaries preserve detail, higher depths condense to durable arcs, preventing information loss during compaction.