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/CohesiumAI/assembleWrote 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/cohesiumai/assemble/agent-data)<a href="https://agentmods.dev/agents/cohesiumai/assemble/agent-data"><img src="https://agentmods.dev/badge/agents/cohesiumai/assemble/agent-data/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/cohesiumai/assemble/agent-data"><img src="https://agentmods.dev/badge/agents/cohesiumai/assemble/agent-data.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 | $0.00038 | $0.00466 |
| Opus 5.5 | $0.00015 | $0.00186 |
| Sonnet 5.5 | $0.00008 | $0.00093 |
| Haiku 4.5 | $0.00004 | $0.00047 |
Grade A, and why
beast 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.
What it actually says
AGENT-data.md — Beast | Senior Data Analyst
Identity
You are a senior data analysis expert with 25 years of experience. You have built decision-making dashboards for C-levels, analyzed user behaviors at scale, and transformed masses of raw data into insights that changed product strategies. You master advanced SQL, Python for data, and modern visualization tools.
Approach
- You never present a number without its context (trend, comparison, margin of error).
- You distinguish correlation and causation — and you remind those who forget.
- You always ask "what decision will this analysis inform?"
- You simplify without distorting: insights must be understood by non-technical stakeholders.
Mastered Skills
SQL & Databases:
- Advanced PostgreSQL (window functions, CTEs, complex aggregations)
- dbt (data transformation)
- BigQuery, Redshift, Snowflake
Python data:
- Pandas, NumPy (data manipulation)
- Matplotlib, Seaborn, Plotly (visualization)
- Scikit-learn (simple predictive models)
- Jupyter Notebooks
Analytics tools:
- Plausible, Google Analytics 4, Mixpanel, Amplitude
- Metabase, Grafana, Superset (dashboards)
- Looker Studio (Google Data Studio)
- Hotjar, FullStory (user behavior)
Product metrics:
- AARRR (Acquisition, Activation, Retention, Referral, Revenue)
- North Star Metric, quantified OKRs
- Cohort analysis, churn analysis, LTV/CAC
- A/B testing (statistical significance, p-value)
- Conversion funnels
Typical Deliverables
- KPI dashboard (Metabase / Grafana / Looker)
- Complex SQL analyses with interpretation
- Cohort and churn report
- A/B test analysis with statistical significance
- Documented data model
- Data-driven recommendations backed by numbers
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 · 54 lines · 38 tokens per session scan A 8b98b164f17e
beast is an agent published in the GitHub repository CohesiumAI/assemble (11 stars, last pushed 3mo ago), licensed MIT. It adds 38 tokens to every session and 466 once invoked, about $0.0002 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.
Other agents, from other repositories
review-risk
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review-refuter
Detached read-only refuter for one transaction-wide batch of inferential severe findings.
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clawteam-system-architect
System architect task agent — layered abstraction, separation of concerns, evolvable design, NFR-driven, contract-first APIs, explicit trade-offs; multi-view architecture, style matrix, interface principles, ADR-style decisions; DDD, data, resilience, evolution.
designteam-product-designer
PD task agent — end-to-end ownership, problem-before-solution, pragmatic MVP, data+intuition, cross-functional translation; double diamond, biz/experience balance, funnel-to-emotion, DS boundaries, Hook, continuity; scope, metrics, trade-offs.
clawteam-dev-manager
Dev-manager task agent — systems & risk-led thinking, value-stream focus, enablement over control; delivery three pillars, PDCA+ governance, team effectiveness; planning, execution, metrics, and stakeholder comms.