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 nickroci/ultan --skill epiphanygit clone --depth 1 https://github.com/nickroci/ultanWrote 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/nickroci/ultan/epiphany)<a href="https://agentmods.dev/skills/nickroci/ultan/epiphany"><img src="https://agentmods.dev/badge/skills/nickroci/ultan/epiphany/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/nickroci/ultan/epiphany"><img src="https://agentmods.dev/badge/skills/nickroci/ultan/epiphany.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00154 | $0.03145 |
| Opus 5.5 | $0.00062 | $0.01258 |
| Sonnet 5.5 | $0.00031 | $0.00629 |
| Haiku 4.5 | $0.00015 | $0.00314 |
Grade A, and why
epiphany 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 1mo 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 — 221 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Epiphany
A horizontal connection-finder for the Ultan library. The daemon already does vertical abstraction (rolling children up into a parent rule); this does the thing nothing else does — spots that two distant, unrelated entries share a latent structure, and surfaces the single best one.
The design rationale, and the one rule that makes this worth running: an epiphany is a remote association — a far pair with shared deep structure. Near-neighbours (same folder, same topic, already cross-linked) are obvious and worthless here. The value lives in pairs that are far apart in the graph but structurally rhyme. Everything below is built to find those and reject everything else.
Usage
/epiphany # free-roam the whole library (cross-domain)
/epiphany <seed/topic> # free-roam, biased toward a region or theme
/epiphany <project> # SCOPED: search one project, bridge across its subsystems
Free-roam finds the most surprising pair anywhere in the library. Scoped
(e.g. /epiphany vol-predictor) constrains the search to one project and hunts a
non-obvious connection across its subsystems (model/entry-signal ↔
infrastructure/data-pipeline, evaluation ↔ policy, …), optionally bridging up
to a global/ principle. "Distance" rescales from cross-domain to cross-subsystem —
still genuinely far, and actionable for the project you're working in. Scoped is the
better daily mode.
Method
The agent invoking this skill is the orchestrator. The scouts are the generators — and they do not fire once and stop. They converge over rounds, exchanging findings through a shared pool the orchestrator relays between them. (True peer-to-peer agent chatter isn't available; an orchestrator-mediated blackboard is the realistic — and more faithful — form: it mirrors a global workspace that competing processes read from and write to.) Run every step.
0 — Map the territory.
# free-roam: the whole library
python3 "<this-skill-dir>/inventory.py" --regions
# scoped to a project: partition it into subsystems (those become the scout regions)
python3 "<this-skill-dir>/inventory.py" --region projects/<PROJECT> --depth 3 --regions
What ships with it
1 file 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.
- 1mo ago First seen · 221 lines · 154 tokens per session scan A 1a1e3cc4caa7
epiphany is a skill published in the GitHub repository nickroci/ultan (4 stars, last pushed 3mo ago), licensed MIT. It adds 154 tokens to every session and 3,145 once invoked, about $0.0006 per session on Opus 5.5. 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 2026-08-31.
Other skills, from other repositories
project-knowledge
Use when a task needs shared repository knowledge, project memory recall, durable documentation updates, knowledge routing, trust/safety checks, correction or consolidation of stale docs, crystallizing reusable lessons, or validation of the knowledge core. Use before non-trivial work that depends on project…
research-to-knowledge
Use when a task asks for deep research, current external information, source-backed comparison, claim verification, source quality assessment, adversarial-source screening, repository/tool/practice evaluation, or saving researched findings into the knowledge core. Do not use for routine local code lookup or project…
openlore
Query and publish to an OpenLore knowledge repository over SSH using ordinary shell commands. Use when a task needs project documentation, runbooks, shared team knowledge, or a place to publish findings.
openlore-housekeeping
Audit and maintain a shared OpenLore knowledge repository. Use on a schedule or on request to find stale docs, broken links, unreviewed inbox items, and missing skill coverage, then publish an audit report.
memex-best-practices
Zettelkasten best practices for building a high-quality knowledge graph.
memex-agentic-memory
A-MEM-inspired agentic memory workflow for structured knowledge capture.