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 athola/claude-night-market --skill summongit clone --depth 1 https://github.com/athola/claude-night-marketWrote 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/athola/claude-night-market/summon)<a href="https://agentmods.dev/skills/athola/claude-night-market/summon"><img src="https://agentmods.dev/badge/skills/athola/claude-night-market/summon/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/athola/claude-night-market/summon"><img src="https://agentmods.dev/badge/skills/athola/claude-night-market/summon.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 | $0.00028 | $0.03124 |
| Opus 5 | $0.00014 | $0.01562 |
| Sonnet 5 | $0.00006 | $0.00625 |
| Haiku 4.5 | $0.00003 | $0.00312 |
Grade A, and why
summon 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 7d 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 — 375 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Table of Contents
- Overview
- When To Use
- When NOT To Use
- Orchestration Loop
- Pipeline-to-Skill Mapping
- Context Overflow Protocol
- Token Budget Protocol
- Failure Handling
- Module Reference
Summon
Overview
Summon is the egregore orchestration loop.
It reads the manifest (.egregore/manifest.json), selects
the next active work item, maps the current pipeline step to
a specialist skill, and invokes that skill.
After each step it advances the pipeline, checks context and
token budgets, and repeats until all items are completed or
the budget is exhausted.
The orchestrator never re-implements phase logic.
Each pipeline step delegates to an existing skill via
Skill() calls.
Summon only manages state transitions, retries, and budget
guards.
When To Use
- Processing one or more work items through the full intake-build-quality-ship pipeline.
- Resuming an interrupted egregore session (manifest already exists with active items).
- Running autonomously under a watchdog that relaunches on exit.
When NOT To Use
- Running a single skill in isolation (call the skill directly instead).
- Exploratory work where the pipeline does not apply.
- When human review is needed before every step (use manual skill invocations).
Launching the Orchestrator
Always launch the orchestrator agent in the FOREGROUND.
Do not use run_in_background: true. The main session
becomes the egregore: it blocks on the orchestrator agent
until the egregore finishes or is dismissed.
Agent(
subagent_type: "egregore:orchestrator",
prompt: "<context about work items and current state>",
run_in_background: false // Required
)
If you launch the orchestrator in the background, the main session will have nothing to do and will stop. This defeats the entire purpose of the egregore. The stop hook cannot prevent this because background agents are detached.
What ships with it
5 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.
- 7d ago First seen · 375 lines · 28 tokens per session scan A 44f17ba81cdc
summon is a skill published in the GitHub repository athola/claude-night-market (337 stars, last pushed today), licensed MIT. It adds 28 tokens to every session and 3,124 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
autonomous
Use when five specialized autonomous agents (code, deploy, planning, research, review) working as a coordinated pipeline. From spec to shipped code with automated planning, research, review, and deployment gates. Use when working with autonomous agents.
sales-director
The Chief Revenue Officer (CRO) — manages ALL sales activities across 1ai-affiliate and 1ai-career. Delegates to lead generation, pipeline management, closing, and customer success. Has memory via 1ai-hub brain, follows 1ai-rules, and coordinates with Marketing Director for leads and Content Director for sales…
worker-planner
Cortex delegated specialist only: Planning specialist for work breakdown, dependencies, risks and verification.
sparc-pipeline
Use when you need to run the full 5-phase SPARC methodology (Specification→Pseudocode→Architecture→Refinement→Completion) for a complex feature. Each phase has explicit done-criteria gates.
drug-design
End-to-end drug discovery pipeline orchestration. Deterministic Python script that auto-chains structure prediction, pocket detection, de novo design, docking, scoring, and ADMET filtering into reproducible workflows.
dispatch
Use when a task file exists in .hyperflow/tasks/ and workers need dispatching. Fans out parallel workers under per-batch Reviewers, runs a final integration review, and commits per sub-task. Endpoint of the auto-chain — no auto-deploy. Trigger with /hyperflow:dispatch, "run the plan", "execute the task", "build it"…