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 aavaz-ai/enterpret-claude-plugins --skill report-enginegit clone --depth 1 https://github.com/aavaz-ai/enterpret-claude-pluginsWrote 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/aavaz-ai/enterpret-claude-plugins/report-engine)<a href="https://agentmods.dev/skills/aavaz-ai/enterpret-claude-plugins/report-engine"><img src="https://agentmods.dev/badge/skills/aavaz-ai/enterpret-claude-plugins/report-engine.svg" alt="Measured on agentmods" 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 | $0.00067 | $0.01794 |
| Opus 5 | $0.00034 | $0.00897 |
| Sonnet 5 | $0.00013 | $0.00359 |
| Haiku 4.5 | $0.00007 | $0.00179 |
Grade A, and why
report-engine 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 — 171 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Report Engine — 4-Phase Workflow with Multi-Format Output
Overview
Every report follows a mandatory 4-phase workflow. The user always reviews a markdown draft before the final output is generated. This prevents wasted effort and ensures accuracy.
Brand Loading
Before generating any output, load brand tokens:
- Check for
brand/custom.json— if it exists, load it as the primary brand - Fall back to
brand/enterpret.json— the Enterpret default palette - Shallow merge — if
custom.jsonexists, merge its fields overenterpret.json(custom overrides only the fields it specifies; unspecified fields use Enterpret defaults) - Resolve template variables:
{ORG_NAME}→context/organization.json→name{ORG_SLUG}→context/organization.json→slug- Apply to all brand fields:
name,footer.dashboardUrl,citations.baseUrl, etc.
The resolved brand object is referred to as BRAND throughout all format-specific reference files.
The 4 Phases
Phase 1: Scope
Purpose: Confirm what the report should cover before querying any data.
Ask the user (conversationally, not as a checklist):
| Parameter | Question | Default |
|---|---|---|
| Audience | "Who will read this?" | Mixed (leadership + ops) |
| Time period | "What date range?" | Last 7 days |
| Region | "Any region filter?" | All regions |
| Goal | "What question are you trying to answer?" | General overview |
| Focus | "Any specific topics or categories to focus on?" | None |
Rules:
- Ask 2-3 questions max. Don't interrogate.
- If the user provides context in their command (e.g.,
/customer-digest US last week), extract parameters from that and confirm. - Always confirm the scope before proceeding: "I'll pull [time range] data for [region/topic], aimed at [audience]. Sound right?"
Phase 2: Query
Purpose: Pull data from the Wisdom Knowledge Graph.
- Load the
wisdom-kgskill for query patterns and rules - Execute queries based on the report type (see
wisdom-kgSKILL.md → Query Strategy) - Collect: theme volumes, sentiment, WoW changes, verbatim evidence with citation IDs
- If a query fails, simplify and retry (never present failed query results)
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 · 171 lines · 67 tokens per session scan A 7e741ce85198
report-engine is a skill published in the GitHub repository aavaz-ai/enterpret-claude-plugins (2 stars, last pushed 5mo ago), licensed MIT. It adds 67 tokens to every session and 1,794 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 2026-08-31.
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