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BEST-Self-Enhancement-Learning-AI: Instructions file for Codex

AGENTS.md

BEST-Self-Enhancement-Learning-AI AGENTS.md is an instructions file for Codex, OpenCode from subkoks/BEST-Self-Enhancement-Learning-AI. It costs 2,627 tokens per session, scanned A, original, MIT.

Project instructions for BSELA, a Python package and command-line tool that coordinates existing coding agents and stores their working knowledge locally.

In plain words
What is it for?
Use them when developing BSELA, changing its agent rules, organizing memory, or deciding whether a proposed architecture fits the project.
Why use it?
They define the project's boundaries and rules, including its memory structure, local-first design, and requirement to improve agent workflows without changing model weights.

Instructions file for CodexOpenCode

Written for Codex and OpenCode: reads ~/.codex or $CODEX_HOME, but also the file is AGENTS.md. Also seen: reads .claude/ paths; mentions CLAUDE.md; mentions subagents.

This is subkoks/BEST-Self-Enhancement-Learning-AI's own configuration. It tells Codex and OpenCode how to work on BEST-Self-Enhancement-Learning-AI itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything BEST-Self-Enhancement-Learning-AI configures →

not rated 2repo 2d ago A scan 2,627 tokens original MIT
View source ↗ subkoks/BEST-Self-Enhancement-Learning-AI
Reuse

Borrowing it

Nothing to install: this file belongs to subkoks/BEST-Self-Enhancement-Learning-AI. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/subkoks/BEST-Self-Enhancement-Learning-AI/main/AGENTS.md
Clone the repo
git clone --depth 1 https://github.com/subkoks/BEST-Self-Enhancement-Learning-AI

Made for: Codex, OpenCode.

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Per session 2,627 This file is loaded in full into every session.
When invoked 2,627 The same file — it is already loaded in full.
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 $0.02627 $0.02627
Opus 5 $0.01314 $0.01314
Sonnet 5 $0.00525 $0.00525
Haiku 4.5 $0.00263 $0.00263

Measured 10d ago against content hash 9f99c2e60767, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

BEST-Self-Enhancement-Learning-AI AGENTS.md 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 10d 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.

AGENTS.md · 134 lines

How it starts

The opening of the file, as written. The whole thing — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.

BSELA — Project Agent Rules

Overlay on ~/AGENTS.md. This file adds project-specific rules. Global rules still apply unless explicitly overridden here.

Project Identity

  • Name: BSELA (Best Self-Enhancement Learning Agent).
  • Folder: BEST-Self-Enhancement-Learning-AI (kept descriptive; do not rename).
  • CLI + Python package: bsela.
  • Role in the stack: control plane over existing coding agents. Never duplicates them.

Core Invariants

  • Harness + context, not weights. Any suggestion to fine-tune or train a model is out of scope.
  • Reuse agents-md, do not fork it. Rule changes are proposals against ~/Projects/Current/Active/agents-md. Never write rules directly into the synced artifacts (~/.claude/CLAUDE.md, ~/.cursor/rules/gotcha.md, etc.) — always upstream to canonical.
  • Single-agent V1. No LangGraph / CrewAI / autogen. Reach for sub-agents only after V1 metrics justify it. Optional developer-session role prompts (not an in-process framework) live under docs/orchestrator/; see ADR 0008 in docs/decisions/.
  • Local-first. SQLite + filesystem. No Postgres/Redis unless the data shape truly demands it.
  • Haiku-first pipeline. Opus 4.7 only for low-confidence distillation, planning, and audits. Track per-session cost.

Memory Taxonomy (canonical)

Five types, one SQLite DB, typed tables:

  • short-term task — editor-native; BSELA does not persist.
  • project — <repo>/AGENTS.md + <repo>/.bsela/project.db.
  • long-term learning — Lesson rows in ~/.bsela/bsela.db; durable rules ship via agents-md proposal branches.
  • error — ErrorRecord rows in ~/.bsela/bsela.db; 90-day rolling window (bsela prune).
  • decision — Decision rows in ~/.bsela/bsela.db; operator audit trail (separate from repo ADRs under docs/decisions/).

Improvement Loop Contract

  • Trigger → session-end hook / error regex / cron / manual bsela review.
  • Feedback source → transcript + diff + tests + correction markers (stop, no, don't, undo) + retry counts.
  • Evaluation → Haiku 4.5 rubric; low scores promoted to Opus 4.7.
  • Update → proposal branch on agents-md → gate → merge → sync.
  • Rollback → every change is a commit; bsela rollback <lesson-id> reverts; replay harness validates.

Read the full file on GitHub · 134 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. 10d ago First seen · 134 lines · 2,627 tokens per session scan A 9f99c2e60767

Subscribe to this mod's changes

BEST-Self-Enhancement-Learning-AI AGENTS.md is an instructions file published in the GitHub repository subkoks/BEST-Self-Enhancement-Learning-AI (2 stars, last pushed 2d ago), licensed MIT. It adds 2,627 tokens to every session, about $0.0131 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.

Building agentsLLMMemory and contextPythonai-agentsdeveloper-toolsmcpself-improvement
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