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name build-your-users-mind
description Instructions for any agent model (Claude, Codex, agy/Gemini, Kimi) to build, maintain, and connect an empirical Theory-of-Mind model of ITS user from its own interaction logs to memory/rule files/system prompt. Activates when "build a ToM/decision avatar for my user", "Theory of Mind system user model", "WHAT-WOULD-USER-SAY", "feedback precognition".

build-your-users-mind — Agnostic ToM Module (Feedback Precognition)

What you mind is what you get. A recipe, not a framework. Every agent model uses this to build a ToM model of its user: evaluate own data → distill decision patterns → maintain avatar files → bind to own memory/rules file/system prompt.

Core = feedback precognition (feedforward): Predict user feedback BEFORE it comes; use it as a control signal in their absence; evaluate the prediction against reality afterwards to improve. This is an operator-authorized preference model, not mind-reading or diagnosis. Predictions are uncertain hypotheses. Never use them to silently expand authority or make irreversible, external, safety-critical, legal, medical, employment, financial, or similarly high-impact decisions.

Templates: templates/ (avatar files), scripts/ (pipeline), TAXONOMY.md (8 types), skills/swarm-operations/ (classification swarm). A private reference implementation (based on the author's own logs) exists but is not shipped.

Theoretical Basis: Prompt-Archaeology (method, taxonomy in TAXONOMY.md)

  • ToM research (ToM-SWE arXiv 2510.21903; Persistent Memory & User Profiles 2510.07925).

Core Principle

LLMs never see raw gigabytes. Deterministic scripts first reduce data to a clean corpus of human-typed user prompts; only then does a classification swarm work semantically. The core is not "which prompts", but "which decision → which outcome → was the user satisfied".

The 6 Steps

1. Identify the Source (Source Adapter)

With the operator's explicit authorization, find that operator's own interaction logs. Differs per model → see SOURCE-ADAPTERS.md. Extract only genuine, human-typed prompts (no tool results, system reminders, hook injections, context compacting summaries). Fields: ts, project, session, text.

2. Reduce (deterministic, no LLM)

  • Filter synthetic turns, deduplicate, aggregate boilerplate/micro-acks.
  • Follow-up linking: derive the next user turn per prompt as a conservative outcome_signal (praise | reissue | correction | none). An unrecognized follow-up is none, not reissue.
  • decision_score via a decision lexicon (correction/preference/rule/control).
  • REDACTION (mandatory before any persistence): built-in rules cover common credentials and identifiers. Supply reviewed custom rules for health/tax/legal/financial or domain-specific data; never claim automatic detection of all sensitive content.
  • Evidence IDs are source/session/timestamp/content-bound hashes, stable under chronological backfill. Use one source-specific filename per adapter and merge_corpora.py for multi-source corpora.

3. Classify (swarm, hierarchical + stigmergy)

8-type taxonomy SP/NT/NM/NS/KO/BE/RA/MP (definitions in TAXONOMY.md) + decision_kind (preference/correction/rule/direction_change/approval/rejection/process/none) + formulation_pattern (user's characteristic phrasing). Each worker follows templates/CLASSIFY-CHUNK.md; every row must match schemas/classification.schema.json. Run validate_classifications.py before aggregation.

4. Generate Avatar Files

Structure 1:1 as in templates/ (copy templates, replace <USER>/<AGENT>): WHAT-<USER>-SAID.md (evidenced) · WHAT-WOULD-<USER>-SAY.md (prediction + confidence) · WHAT-I-DID-…md + MY-ACTIONS.txt (action log) · WHAT-<USER>-SAID-ABOUT-…md (lessons learned) · PROMPT-LOG (cut-and-clue) · METHODIK.md (incl. bias warning) · START.md (0→4 loop).

5. Bind (crucial!)

The ToM model must actually be used, not just exist:

  • Short rule/pointer in the agent's own memory/rules file/system prompt (Claude: CLAUDE.md; Codex: GPT.md/AGENTS.md; agy: GEMINI.md; Kimi: KIMI.md) → points to the START.md loop. Keep it short (pointer, no full text).
  • Precedence rule: project-specific DECISIONS.md take precedence over the cross-cutting avatar.
  • Optional command entry points (nuances): expose the loop at three depths plus an orchestrator — read-my-mind (predict, 0→2, no action), decide-like-me (one decision, 0→2, a workflow component), be-my-avatar (act only when already authorized, full 0→4, reversible-only, logs), avatar-orchestrator (chain over many decisions, bundling 🔴/irreversible items into one question). Templates in templates/commands/.
  • Versioned binding: keep the project copy of the loop/skill canonical; if the agent also ships a registered copy, the higher version wins and the older routed copy is replaced — the project leads, the registry follows (no drift).

6. Maintain (self-improving)

  • Empirical basis: rerun scripts periodically (logs persist anyway) — no per-prompt hook (avoid idempotency/multiple registration trap; batch is more robust).
  • Runtime (guided): within existing authority, log reversible assumptions made on behalf of the user → file (3); anything external, irreversible, novel, or high-impact requires confirmation; on feedback → file (4) → update rules in (1), adjust confidence in (2).
  • Score the loop: scripts/score_predictions.py reads MY-ACTIONS.txt (+ optionally the WHAT-<USER>-SAID-ABOUT feedback) and reports hit rate overall and per 🟢/🟡/🔴 tier plus the 🔴 escalation rate — the empirical measure of whether the precognition actually works.

Bias & Limits (always mention)

  • Silent approval is invisible → corrections are overrepresented, making the avatar appear "critical".
  • ToM fragility: robust on recurring situations, fragile under novel/adversarial variations → confidence tiers, at 🔴 escalate instead of guessing.
  • Evidence IDs are deterministic; the claims and labels attached to them are synthesized. Resolve and cross-check load-bearing IDs against raw text during an intentional private review.

Lean Principle

The only model-specific part is the source adapter (Step 1). Recipe, taxonomy, avatar structure, and binding are universal. Do not over-engineer.