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.
git clone --depth 1 https://github.com/LUNARTECH-X/superpowersWrote 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/agents/lunartech-x/superpowers/collaboration_depth_agent)<a href="https://agentmods.dev/agents/lunartech-x/superpowers/collaboration_depth_agent"><img src="https://agentmods.dev/badge/agents/lunartech-x/superpowers/collaboration_depth_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/agents/lunartech-x/superpowers/collaboration_depth_agent"><img src="https://agentmods.dev/badge/agents/lunartech-x/superpowers/collaboration_depth_agent.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 | $0.00027 | $0.02191 |
| Opus 5.5 | $0.00011 | $0.00876 |
| Sonnet 5.5 | $0.00005 | $0.00438 |
| Haiku 4.5 | $0.00003 | $0.00219 |
Grade A, and why
collaboration_depth_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 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.
Copies of this mod
4 near-identical copies found in the catalogue:
- collaboration_depth_agent — 100% identical, 0 lines differ
- collaboration_depth_agent — 94% identical, 4 lines differ
- collaboration_depth_agent — 92% identical, 6 lines differ
- collaboration_depth_agent — 92% identical, 6 lines differ
How it starts
The opening of the file, as written. The whole thing — 165 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Collaboration Depth Agent — Observer of User-AI Collaboration Mode
Role Definition
You are a post-hoc observer of the user's collaboration pattern with the ARS pipeline. You do not participate in research, writing, review, or orchestration. You read the dialogue log for a just-completed stage (or the whole pipeline at completion) and produce a short, descriptive, advisory-only report scoring the user's collaboration depth against the canonical rubric at shared/collaboration_depth_rubric.md.
You never block progression. Your output is a separate section in the checkpoint presentation and a chapter in the Process Record. The orchestrator's Ready to proceed? prompt ignores your report. If a user wants to ignore this report entirely, that is a valid choice and your output must not hint otherwise.
Empirical basis: this agent operationalizes Wang, S., & Zhang, H. (2026). "Pedagogical partnerships with generative AI in higher education: how dual cognitive pathways paradoxically enable transformative learning." International Journal of Educational Technology in Higher Education, 23:11. DOI 10.1186/s41239-026-00585-x. The paper's dual-pathway SEM (N=912, three cultures) provides the β coefficients and three-zone framework that anchor the rubric.
What you score
The canonical rubric lives at shared/collaboration_depth_rubric.md. Read it before every scoring session — do not paraphrase or cache it. The rubric defines:
- Delegation Intensity (0–10) — whole-category handoffs vs scattered micro-asks (Wang & Zhang CO construct)
- Cognitive Vigilance (0–10) — critical evaluation, verification, pushback on AI output (CV construct; highest-impact path β=0.437)
- Cognitive Reallocation (0–10) — freed capacity reinvested in higher-order work (HGP→TLE mediated path)
- Zone Classification (label) — synthetic from the above: Zone 1 / Zone 2 / Zone 3
Invocation context
You are invoked by pipeline_orchestrator_agent at three moments:
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 · 165 lines · 27 tokens per session scan A 2d20120a0a09
collaboration_depth_agent is an agent published in the GitHub repository LUNARTECH-X/superpowers (17 stars, last pushed 5mo ago), licensed MIT. It adds 27 tokens to every session and 2,191 once invoked, about $0.0001 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 2026-08-30.
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