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 EmpiricaAI/empirica --skill dispatch-agentgit clone --depth 1 https://github.com/EmpiricaAI/empiricaWrote 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/empiricaai/empirica/dispatch-agent)<a href="https://agentmods.dev/skills/empiricaai/empirica/dispatch-agent"><img src="https://agentmods.dev/badge/skills/empiricaai/empirica/dispatch-agent/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/empiricaai/empirica/dispatch-agent"><img src="https://agentmods.dev/badge/skills/empiricaai/empirica/dispatch-agent.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 | $0.00069 | $0.01115 |
| Opus 5 | $0.00034 | $0.00558 |
| Sonnet 5 | $0.00014 | $0.00223 |
| Haiku 4.5 | $0.00007 | $0.00112 |
Grade A, and why
dispatch-agent 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 10d 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 — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Epistemic Agent Dispatch
Retrieve what this practice already learned about the task, and put it in the subagent's prompt before spawning.
A fresh subagent has the repo and the harness, not your practice's history. It cannot know which approach was already tried and abandoned, because that lives in your epistemic graph and nothing puts it in front of them. The enrichment step is the whole skill; everything below serves it.
subagent_type: "fork" is the exception — a fork inherits your full conversation
context, so enrichment is redundant there. Use fork when the subagent needs what
you know right now; use enrichment when it needs what the practice learned
before this session.
1. Retrieve the graph
Pass the knowledge graph, not a hand-picked subset of it. This is the same surface PREFLIGHT and the post-compact hook already inject into you — reuse it rather than assembling something bespoke:
empirica bootstrap-context --output json # the three circles, all types
empirica project-search --task "<the subagent's task>" --output json # task-scoped pull
bootstrap-context returns active state (open goals, subtasks, recent findings /
decisions / dead-ends / mistakes), persistent reference (decisions with active
outcomes, verified assumptions, sources) and the topic-relevant backlog (open
unknowns and assumptions, relevant dead-ends). project-search narrows to the
subagent's actual task; add --global to reach shared learnings from other
projects. Cortex equivalent: mcp__cortex__investigate({query, limit}).
There is no --list on the *-log verbs: they WRITE, and retrieval is semantic.
2. Trim, don't curate
Pass every type that came back — unknowns and assumptions included. An open unknown tells the subagent what is genuinely undecided; an assumption tells it what is being taken on faith. Dropping those is how a subagent confidently builds on something nobody verified.
The only cut worth making is volume: drop what is plainly about other work. Do not filter by TYPE, and do not apply a similarity cutoff — a fixed threshold drops the one dead-end that matters while admitting four findings that don't.
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.
- 10d ago First seen · 123 lines · 69 tokens per session scan A 223fa09ac01a
dispatch-agent is a skill published in the GitHub repository EmpiricaAI/empirica (247 stars, last pushed today), licensed MIT. It adds 69 tokens to every session and 1,115 once invoked, about $0.0003 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.
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