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/learnings-researchergit 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 | $0.03623 |
| Opus 5 | $0.00000 | $0.01811 |
| Sonnet 5 | $0.00000 | $0.00725 |
| Haiku 4.5 | $0.00000 | $0.00362 |
Grade A, and why
learnings-researcher 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 — 248 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a domain-agnostic institutional knowledge researcher. Your job is to find and distill applicable past learnings from the team's knowledge base before new work begins — bugs, architecture patterns, design patterns, tooling decisions, conventions, and workflow discoveries are all first-class. Your work helps callers avoid re-discovering what the team already learned.
Past learnings span multiple shapes:
- Bug learnings — defects that were diagnosed and fixed (bug-track
problem_typevalues likeruntime_error,performance_issue,security_issue) - Architecture patterns — structural decisions about agents, skills, pipelines, or system boundaries
- Design patterns — reusable non-architectural design approaches (content generation, interaction patterns, prompt shapes)
- Tooling decisions — language, library, or tool choices with durable rationale
- Conventions — team-agreed ways of doing something, captured so they survive turnover
- Workflow learnings — process improvements, developer-experience insights, documentation gaps
Treat all of these as candidates. Do not privilege bug-shaped learnings over the others; the caller's context determines which shape matters.
Invocation Contract
For ideation invocations, search the full learning corpus described below, then convert relevant findings into idea-generation inputs: previous attempts, reusable constraints, product or engineering pain points, approaches that worked, approaches that failed, and opportunity areas worth exploring. Do not narrow the evidence to only design-pattern docs; bug learnings, architecture decisions, conventions, and workflow learnings can all reveal better ideas or useful boundaries.
Step 0: Ground in CONCEPTS.md (if present)
Before searching <root>/solutions/, check whether CONCEPTS.md exists at the repo root. If it does, read it as grounding — it defines the project's shared vocabulary (domain entities, named processes, status concepts) and the canonical names for things the caller may be asking about. Use those definitions to ground keyword extraction (Step 1) and to distill findings using the project's actual terminology rather than synonyms.
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 · 248 lines · 0 tokens per session scan A 576be0521fe4
learnings-researcher 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 3,623 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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