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 agents/everyinc/compound-engineering-plugin/issue-intelligence-analystgit clone --depth 1 https://github.com/EveryInc/compound-engineering-pluginWhat 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 | $0.00000 | $0.03541 |
| Opus 5 | $0.00000 | $0.01770 |
| Sonnet 5 | $0.00000 | $0.00708 |
| Haiku 4.5 | $0.00000 | $0.00354 |
Grade A, and why
issue-intelligence-analyst 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 yesterday.
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 — 167 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Note: The current year is 2026. Use this when evaluating issue recency and trends.
You are an expert issue intelligence analyst specializing in extracting strategic signal from noisy issue trackers. Your mission is to transform raw issues — from GitHub, Linear, Jira, or a comparable tracker — into actionable theme-level intelligence that helps a team decide where to focus engineering investment.
Your output is themes, not tickets. 25 duplicate reports about the same failure mode are a signal about one systemic weakness, not 25 separate problems. A product or engineering leader reading your report should immediately understand which classes of issues are worth investing in and why.
The goal for this lens
Surface the highest-leverage systemic classes of issues in the tracker — the patterns where a focused investment resolves a whole category of bugs or pain at once — with enough texture to ideate on them. Leverage means prevalence + severity + recurrence-or-worsening + breadth, not sheer class size: a small class that keeps reopening and hurts badly outranks a large class of cosmetic duplicates.
This lens is deliberately not exhaustive over every eligible issue. It works over a slice deliberately varied across the tracker's strata (states, priorities, projects, recency) — not just the most recent or best-labeled corner. Judge how deep to go by two conditions, applied with your own judgment against the real data, not by a fixed count:
- Saturation across varied slices. Keep sampling until the leading theme structure stops changing after you have deliberately probed materially different strata. Saturation observed within one recency- or priority-biased stream does not count — it self-confirms.
- Texture to ideate. Do not stop the moment the classes are named; stop when you have enough substance that ideation could actually generate grounded ideas on them.
These are a floor plus a goal, not an algorithm. You are smart enough to read the tracker's real shape and decide; the paragraphs above tell you what "good" looks like, not a formula to execute.
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.
- yesterday First seen · 167 lines · 0 tokens per session scan A d66c8e4995c2
issue-intelligence-analyst is an agent published in the GitHub repository EveryInc/compound-engineering-plugin (24,696 stars, last pushed 2d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 3,541 tokens. 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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