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/findgit 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/find)<a href="https://agentmods.dev/commands/aavaz-ai/enterpret-claude-plugins/find"><img src="https://agentmods.dev/badge/commands/aavaz-ai/enterpret-claude-plugins/find.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 | $0.00021 | $0.00930 |
| Opus 5 | $0.00010 | $0.00465 |
| Sonnet 5 | $0.00004 | $0.00186 |
| Haiku 4.5 | $0.00002 | $0.00093 |
Grade A, and why
find 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 — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/find
Quick lookup of customer feedback on a topic. Fast, focused, 1-2 queries max.
Setup
- Read
context/organization.jsonforcitationBaseUrland org name. If it doesn't exist, tell the user to run/startand stop. - If the user provided no topic, ask: "What topic should I look into?"
Execution
Step 1: Use search_knowledge_graph from the enterpret-wisdom-mcp MCP server with the user's topic (try 2-3 keyword variations). Use the EXACT theme names returned.
Step 2: Run this query for volume + sentiment (substitute the exact theme name and dates):
MATCH (nli:NaturalLanguageInteraction)-[:SUMMARIZED_BY]->(fi:FeedbackInsight)-[:HAS_SENTIMENT]->(sp:SentimentPrediction)
MATCH (fi)-[:HAS_TAGS]->(cft:CustomerFeedbackTags)-[:HAS_THEME]->(t:Theme)
WHERE t.name CONTAINS "{THEME_NAME}"
AND nli.record_timestamp >= "{START_DATE}" AND nli.record_timestamp < "{END_DATE}"
RETURN t.name AS theme, sp.label AS sentiment, COUNT(DISTINCT fi.feedback_record_id) AS volume
ORDER BY volume DESC
LIMIT 20
Use execute_cypher_query with parameter name cypher_query. Default window: 7 days if user said "last week", otherwise 30 days. Compute ISO dates.
Note: Use CONTAINS (not =) for theme matching to handle partial name matches consistently across all commands. The search_knowledge_graph step already validates theme names, so CONTAINS catches slight variations without false positives.
Step 3: Run this query for quotes:
MATCH (nli:NaturalLanguageInteraction)-[:SUMMARIZED_BY]->(fi:FeedbackInsight)-[:HAS_TAGS]->(cft:CustomerFeedbackTags)-[:HAS_THEME]->(t:Theme)
WHERE t.name CONTAINS "{THEME_NAME}"
AND nli.record_timestamp >= "{START_DATE}" AND nli.record_timestamp < "{END_DATE}"
RETURN fi.feedback_record_id AS record_id, nli.content AS verbatim, nli.record_timestamp AS date
ORDER BY nli.record_timestamp DESC
LIMIT 10
Pick 3-5 diverse quotes (different angles, not repetitive).
Output — YOU MUST PRESENT THIS
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 · 91 lines · 21 tokens per session scan A 46901dcadcf4
find is a command published in the GitHub repository aavaz-ai/enterpret-claude-plugins (2 stars, last pushed 4mo ago), licensed MIT. It adds 21 tokens to every session and 930 once invoked, about $0.0001 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.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
constitution
Create or update the project constitution from interactive or provided principle inputs.
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.