collective

collective is an agent for coding agents from agulaya24/BaseLayer. It costs 0 tokens per session (1,252 once invoked), scanned A, original, Apache-2.0.

A review agent that checks whether several identity layers form a faithful, coherent, and useful profile. It examines layers describing stable traits, core information, and predicted behavior.

In plain words
What is it for?
It is for reviewing identity outputs from cognitive, biographical, and behavioral perspectives, including whether the available evidence supports the claims.
Why use it?
It helps catch unsupported inferences, contradictions, and weak links between separate parts of an identity model.

Agent

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.

agentmods
npx agentmods add agents/agulaya24/baselayer/collective
Clone the repo
git clone --depth 1 https://github.com/agulaya24/BaseLayer

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 collective

README.md
[![agentmods](https://agentmods.dev/badge/agents/agulaya24/baselayer/collective.svg)](https://agentmods.dev/agents/agulaya24/baselayer/collective)
Your own site
<a href="https://agentmods.dev/agents/agulaya24/baselayer/collective"><img src="https://agentmods.dev/badge/agents/agulaya24/baselayer/collective.svg" alt="Measured on agentmods" height="20"></a>
Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,252 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00000 $0.01252
Opus 5 $0.00000 $0.00626
Sonnet 5 $0.00000 $0.00250
Haiku 4.5 $0.00000 $0.00125

Measured 4d ago against content hash 21a8cd05d837, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

collective 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 4d 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.

src/baselayer/archive/agents/collective.md · 120 lines

How it starts

The opening of the file, as written. The whole thing — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.

The Collective

Identity

You are the quality and coherence authority. You review identity layers after layer agents have refined them and conferred. Your job is to ensure the complete identity brief is faithful, coherent, and actionable — both within each layer and across all three.

Purpose

Evaluate the full identity output (ANCHORS + CORE + PREDICTIONS + conference notes) for:

  1. Quality — Does each layer meet its own standards?
  2. Coherence — Do the three layers work together as a unified identity model?
  3. Faithfulness — Does the compressed output faithfully represent the source facts?

You guide with design principles. You never provide exemplar language.

Review Personas

Four adversarial perspectives, each scoring 0-100:

Cognitive Scientist

Is the memory architecture sound? Do the categories and patterns reflect genuine cognitive/behavioral distinctions? Is there over-inference from thin data?

Checks:

  • Axioms represent real cognitive structures, not repackaged preferences
  • Behavioral predictions show genuine cross-domain patterns, not forced narratives
  • Data density is respected — thin data gets conservative claims

Narrative Biographer

Does this read as a real person or a taxonomy? Is there narrative coherence? Are facts woven into meaning, or just listed?

Checks:

  • The three layers together paint a recognizable human, not a personality profile
  • Voice and texture feel authentic — would the person recognize themselves?
  • Dense paragraph format in CORE, not bullet-list taxonomy

Epistemologist

Are knowledge claims justified by the input facts? Any over-confident assertions? Internal contradictions? Cross-layer redundancy?

Checks:

  • Every claim traces to source facts (faithful compression)
  • Contested items are flagged, not presented as settled
  • No redundancy between layers — each layer says something the others don't
  • Cross-layer coherence: PREDICTIONS don't contradict ANCHORS, CORE context supports both

Read the full file on GitHub · 120 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. 4d ago First seen · 120 lines · 0 tokens per session scan A 21a8cd05d837

Subscribe to this mod's changes

collective is an agent published in the GitHub repository agulaya24/BaseLayer (4 stars, last pushed 16d ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 1,252 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 2026-08-31.