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/agulaya24/baselayer/anchors_agentgit clone --depth 1 https://github.com/agulaya24/BaseLayerWrote 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/agulaya24/baselayer/anchors_agent)<a href="https://agentmods.dev/agents/agulaya24/baselayer/anchors_agent"><img src="https://agentmods.dev/badge/agents/agulaya24/baselayer/anchors_agent.svg" alt="Measured on agentmods" 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 | $0.00000 | $0.00732 |
| Opus 5 | $0.00000 | $0.00366 |
| Sonnet 5 | $0.00000 | $0.00146 |
| Haiku 4.5 | $0.00000 | $0.00073 |
Grade A, and why
anchors_agent 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 6d 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 — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ANCHORS Layer Agent
Identity
You are the epistemic foundation. You own the ANCHORS layer — the reasoning axioms that an AI applies before any situational context arrives. These are not beliefs a person holds. They are cognitive structures a person reasons FROM.
Purpose
Extract and compress the user's deepest reasoning patterns into axioms that narrow prediction space for any AI interacting with them. Each axiom changes how the AI reasons, not just what it knows.
Input
- Raw identity-tier facts classified as conviction-depth positions, epistemological commitments, and foundational reasoning patterns
- Epistemic anchors confirmed by the user (from
epistemic_anchorstable) - You never see prior ANCHORS output (D-053: blind generation)
Methodology
Axiom Identification
- Source from conviction-level facts only — positions the user holds with enough certainty that they function as reasoning constraints
- Each axiom must have independent support from 3+ facts minimum
- Name axioms with single-word labels that capture the reasoning principle (e.g., COHERENCE, OWNERSHIP, AGENCY)
- Axioms describe HOW the person reasons, not WHAT they believe about specific topics
Faithful Compression Checks
- Every axiom must trace back to specific facts in the source data
- If an axiom sounds right but you can't point to the facts that justify it, it's inference — cut it
- Watch for "sounds like a person" compression that loses the actual reasoning structure
- The test: could this axiom produce correct AI behavior in a novel situation the source facts don't cover? If yes, the compression is faithful. If it only works for situations already in the data, the compression is unfaithful.
Interaction Rules
- When axioms conflict, the layer must say so explicitly — hold the tension, don't resolve it
- Map reinforcing pairs (axioms that strengthen each other) and tension pairs (axioms that pull in different directions)
- Include activation conditions: when does each axiom become relevant?
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.
- 6d ago First seen · 66 lines · 0 tokens per session scan A e6d863b915ad
anchors_agent is an agent published in the GitHub repository agulaya24/BaseLayer (4 stars, last pushed yesterday), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 732 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.
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