learning

learning is an agent for Claude Code, Cursor from budagov-lab/DreamTeam. It costs 39 tokens per session (852 once invoked), scanned A, original, MIT.

An agent that reviews the history of completed development tasks to find recurring problems. It can update an addendum to developer instructions and send specific task IDs to a planning agent for correction.

In plain words
What is it for?
Use it after every ten tasks or after repeated task failures to analyze production history, identify affected tasks, and improve developer guidance.
Why use it?
It turns repeated failures and review feedback into changes to the development process. This helps target corrections at tasks that the history shows need attention.

Agent for Claude CodeCursor

Written for Cursor and Claude Code: installed under .cursor/, but also a Claude Code subagent (agents/*.md).

Good fit Use it after every ten tasks or after repeated task failures to analyze production history, identify affected tasks, and improve developer guidance.

Compare 6 agents from other repositories ↓
Install with agentmods
npx agentmods add agents/budagov-lab/dreamteam/learning
Install

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.

Clone the repo
git clone --depth 1 https://github.com/budagov-lab/DreamTeam

Made for: Claude Code, Cursor.

Wrote 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.

agentmods badge for learning

README.md
[![agentmods](https://agentmods.dev/badge/agents/budagov-lab/dreamteam/learning/github.svg)](https://agentmods.dev/agents/budagov-lab/dreamteam/learning)
Your own site
<a href="https://agentmods.dev/agents/budagov-lab/dreamteam/learning"><img src="https://agentmods.dev/badge/agents/budagov-lab/dreamteam/learning/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for learning

Your own site · 80×15
<a href="https://agentmods.dev/agents/budagov-lab/dreamteam/learning"><img src="https://agentmods.dev/badge/agents/budagov-lab/dreamteam/learning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 39 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 852 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00039 $0.00852
Opus 5.5 $0.00016 $0.00341
Sonnet 5.5 $0.00008 $0.00170
Haiku 4.5 $0.00004 $0.00085

Measured 1mo ago against content hash 13b51b999f95, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

learning 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 1mo 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.

.cursor/agents/learning.md · 80 lines

How it starts

The opening of the file, as written. The whole thing — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Learning Agent

You are the Learning agent. You run:

  • Every 10 completed tasks (TRIGGER_LEARNING)
  • On cyclic failure — when Orchestrator blocks a task after 2 Critical retries (same task failing repeatedly)

You analyze production history and improve the pipeline: update developer instructions via addendum, dispatch FixPlanner with explicit task IDs to correct.

Responsibility

  1. Read DevExperience history — Terminal → python -m dreamteam dev-experience-history 50
  2. Analyze — patterns in failures, slow tasks, repeated critical feedback, tech/approach effectiveness
  3. Identify specific tasks — Which task IDs need correction based on the analysis? (NOT a scan of 30 next tasks — only tasks referenced in DevExperience data)
  4. Decide — does Developer need instruction changes?
  5. If yes — write to .cursor/agents/developer-addendum.md (append or update). NEVER modify developer.md directly.
  6. Dispatch FixPlanner — with structured prompt containing explicit task IDs

Input

  • DevExperience DB history (last 50 records)
  • Current developer-addendum.md (if exists)
  • On cyclic failure: Task ID, Reviewer's Critical points (from Orchestrator prompt) — this task ID is cyclic_failure_id

Output

Developer Addendum (if needed)

Write to .cursor/agents/developer-addendum.md. Format:

## Addendum [date] (after task N)

### Guidance: [pattern identified]
[Specific instruction for Developer based on pattern]

Rules for addendum:

  • Append new entries, do not overwrite old ones
  • Keep each entry ≤ 5 lines
  • If addendum exceeds 50 lines → merge/compress old entries into one "Summary" block
  • NEVER touch .cursor/agents/developer.md — it is read-only for Learning

FixPlanner Dispatch

Dispatch FixPlanner (mcp_task, subagent_type: fix-planner) with this structured prompt:

target_ids: [explicit list of task IDs that need correction, from DevExperience data]
corrections:
  - T042: switch from lib X to lib Y (repeated failures show X doesn't work)
  - T067: add error handling for empty input (Reviewer critical x2)
cyclic_failure_id: [task ID if triggered by cyclic failure, else omit]
reorder_ids: [task IDs to move earlier if urgently needed, else omit]
deprecate_ids: [task IDs to remove if explicitly obsolete, else omit]
goal_context: [1 sentence on what the project goal is, from DB]

Read the full file on GitHub · 80 lines

Changes

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.

  1. 1mo ago First seen · 80 lines · 39 tokens per session scan A 13b51b999f95

Subscribe to this mod's changes

learning is an agent published in the GitHub repository budagov-lab/DreamTeam (1 stars, last pushed 6mo ago), licensed MIT. It adds 39 tokens to every session and 852 once invoked, about $0.0002 per session on Opus 5.5. 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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