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 agentmods add commands/aavaz-ai/enterpret-claude-plugins/analyzegit 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/commands/aavaz-ai/enterpret-claude-plugins/analyze)<a href="https://agentmods.dev/commands/aavaz-ai/enterpret-claude-plugins/analyze"><img src="https://agentmods.dev/badge/commands/aavaz-ai/enterpret-claude-plugins/analyze.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 | $0.00030 | $0.05135 |
| Opus 5 | $0.00015 | $0.02567 |
| Sonnet 5 | $0.00006 | $0.01027 |
| Haiku 4.5 | $0.00003 | $0.00513 |
Grade A, and why
analyze 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 5d 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 — 534 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/analyze
You are running a deep multi-query analysis of a customer feedback topic. This command builds a comprehensive picture: volume trends, sentiment breakdown, theme structure, co-occurring patterns, taxonomy placement, and verbatim evidence. It is the full investigation — not a quick scan.
With --rootcause, the same queries power a structured root cause analysis — severity assessment, blast radius, hypothesis clustering, and an evidence chain focused on diagnosing issues.
Pre-Flight
- Check if
context/organization.jsonexists. If not, tell the user: "Run/startfirst to connect to your organization's Knowledge Graph." Stop. - Read
context/organization.jsonfor org name, slug, andcitationBaseUrl. - Call
get_organization_detailsfrom theenterpret-wisdom-mcpMCP server as a connectivity check. If it fails with an auth error, tell the user to run/startand stop. - Read
.claude/enterpret-customer-insights.local.mdif it exists for user preferences (role, focus, output style).
Mode Detection
Determine which mode to run:
- Explicit flag:
/analyze checkout --rootcause→ rootcause mode - Implicit detection: If the input looks like a bug report, Jira paste, or contains words like "broken", "failing", "regression", "outage", "crash", "error", "down" → suggest rootcause mode:
"This looks like an issue investigation — want me to include severity assessment and root cause hypotheses? (y/n)"
- If yes → rootcause mode
- If no → standard mode
- Default: standard analysis mode
Set the mode variable {MODE} to either standard or rootcause for use in subsequent steps.
Skills (reference during execution, not upfront)
wisdom-kg— read if you need schema details or a query failsevidence-synthesis— read when synthesizing quotes and writing the narrative- User context is loaded from
.local.mdabove; defaults are in thewisdom-kgskill
Process
Step 1: Parse Input
Extract the topic from the user's input. Accept messy input — Jira ticket titles, vague descriptions, feature names, support escalation subjects.
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.
- 5d ago First seen · 534 lines · 30 tokens per session scan A 243bb80e1414
analyze is a command published in the GitHub repository aavaz-ai/enterpret-claude-plugins (2 stars, last pushed 4mo ago), licensed MIT. It adds 30 tokens to every session and 5,135 once invoked, about $0.0002 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.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.