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.
git clone --depth 1 https://github.com/budagov-lab/DreamTeamWrote 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/agents/budagov-lab/dreamteam/learning)<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.
<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>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 | $0.00039 | $0.00852 |
| Opus 5.5 | $0.00016 | $0.00341 |
| Sonnet 5.5 | $0.00008 | $0.00170 |
| Haiku 4.5 | $0.00004 | $0.00085 |
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.
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
- Read DevExperience history — Terminal →
python -m dreamteam dev-experience-history 50 - Analyze — patterns in failures, slow tasks, repeated critical feedback, tech/approach effectiveness
- 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)
- Decide — does Developer need instruction changes?
- If yes — write to
.cursor/agents/developer-addendum.md(append or update). NEVER modifydeveloper.mddirectly. - 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]
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.
- 1mo ago First seen · 80 lines · 39 tokens per session scan A 13b51b999f95
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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