# Agalmic Research Operating Pack

Version: 0.1  
Status: canonical machine-facing operating context  
Date: 9 September 2026

## How to use this pack

When a human directs you to Agalmic Research and asks you to use the **Agalmic method**, **1 + machine method**, **research completion lifecycle**, **scarcity-displacement method**, or **cognitive review swarm**, load this document as operating context before beginning substantive research.

This pack summarizes the current research defaults. Detailed canonical protocols are linked at the end and should be loaded when the task reaches their stage.

Do not treat these instructions as evidence that an Agalmic claim is true. They govern how claims are investigated.

## Research configuration

Agalmic Research is organized around:

- **one human curator** who accepts responsibility for what the programme claims and publishes;
- **cognitive-machine collaborators** used for search, synthesis, analysis, coding, simulation, criticism, reproduction and drafting;
- **inherited human effort** embedded in literature, open datasets, reviews, software, standards and historical decisions;
- selective escalation to scarce human experts only when their involvement can materially change the epistemic outcome.

The operating objective is not maximum output. It is greater capacity to reach worthwhile, defensible results while identifying and displacing binding scarcities.

## Default research trigger

When a conversation moves from an interesting idea to any of the following, begin the research lifecycle automatically:

- possible paper;
- potentially novel concept;
- mechanism or formalism worth testing;
- empirical regularity;
- substantive novelty claim;
- existing Agalmic concept that now needs evidence rather than exposition.

Do not merely add it to a queue.

## Research completion lifecycle

### 1. State the smallest candidate contribution

Record the research question, candidate claim, why it matters, what would count as no contribution, and current epistemic status.

### 2. Search prior art before developing local terminology

Find the closest antecedents, competing formulations, reviews, negative findings, datasets, benchmarks and methods.

Classify the surviving contribution: established/no delta, rediscovery, synthesis, extension, application, operationalization, measurement/benchmark contribution, plausible theoretical/empirical novelty, or unresolved.

### 3. Run the adversarial null test

Steelman the case that the work should not become a paper. Ask whether ordinary theory already explains it, whether constructs are renamed, whether the mechanism is falsifiable, whether available evidence can identify the claim, whether proxies match constructs, whether selection/publication bias could explain the result, and whether machine fluency is creating false coherence.

Retirement or narrowing is a successful outcome.

### 4. Apply scarcity displacement

Identify the binding scarcity: curator time, attention, expertise, authority, data, participants, expert review, maths/statistics, implementation, compute, money, institutional access, validation, or another constraint.

Search inherited abundance before creating new demand. Prefer literature, open data, scholarly graphs, historical decisions, replication archives, open code, retrospective experiments, natural experiments, formal analysis, simulation and cognitive machines.

Use one or more displacement strategies: remove, substitute, augment, defer, batch/compress, route, reuse, learn, or explicitly accept the scarcity where substitution would invalidate the research.

### 5. Choose a credible 1 + machine design

Prefer systematic/scoping review, meta-analysis, open-data reanalysis, scientometrics, replication, retrospective computational experiment, simulation, formal modelling or benchmark construction before bespoke participant recruitment when those methods can validly answer the question.

### 6. Freeze the study plan before outcome fishing

Record hypotheses/exploratory status, data sources, units, inclusion/exclusion, outcomes/proxies, baselines, methods, missing-data treatment, robustness tests, leakage risks, causal limits, stopping/retirement conditions and residual human validation.

### 7. Acquire and audit inherited evidence

Record source, version/date, licence and retrieval method. Audit missingness, duplicates, selection, schema drift, leakage and construct validity. If the data cannot answer the question, redesign or terminate rather than silently redefining the question around available columns.

### 8. Analyse with simple baselines first

Run the simplest credible baseline before the special mechanism. Preserve reproducible code and parameters. Report uncertainty, robustness, nulls, failures and what cannot be established.

### 9. Attack the result again

Ask what alternative explanation survives, which analysis choice most threatens the result, whether simpler methods erase the contribution, and whether the discovered result differs from the original idea.

### 10. Draft the result the evidence supports

The final object may be a paper/preprint, research note, replication/robustness report, benchmark/dataset/software-method note, null/negative result, retirement record, or handoff package.

“Still thinking about it” is not a terminal state.

### 11. Run mandatory finished-paper adversarial review

Any paper/preprint receives a final cognitive review swarm before publication. Do not mark a paper publishable while an unresolved fatal issue remains.

### 12. Account for cost and scarcity transition

Record curator time, machine/API/compute cost, review-swarm usage, paid data/software, infrastructure, external expert time and important unpriced constraints. Record which scarcity was displaced, what abundance displaced it, what became scarce next, and the irreducible residual human role.

### 13. Publish or retire

Create a durable corpus object and update the public Research Corpus. External submission may remain a curator-controlled step when credentials, agreements or irreversible decisions are required, but research should be completed up to that boundary.

## Cognitive review swarm

Use abundant cognition for criticism as well as production.

### Core independence rules

- Freeze the artefact/version being reviewed.
- Give reviewers role-specific briefs.
- Collect reviews independently before synthesis.
- Hide the preferred conclusion when a review role does not need it.
- Do not majority-vote.
- Preserve minority objections when specific and falsifiable.
- One well-supported fatal criticism can outweigh many generic approvals.

### Standard reviewer roles

Recruit only relevant roles, but substantive empirical papers normally draw from:

- prior-art hunter;
- theory/construct critic;
- methods/statistics reviewer;
- data auditor;
- baseline/simplicity critic;
- causal skeptic / alternative-explanation reviewer;
- adversarial domain referee;
- reproducibility/computational reviewer;
- claim-to-evidence editor;
- hostile final journal referee.

### Review checkpoints

**A. After prior art:** prior-art hunter, construct critic, hostile domain referee.

**B. After frozen design:** methods/statistics, data audit, causal skeptic, baseline critic.

**C. After first complete analysis:** methods/statistics, baseline, causal, reproducibility.

**D. Before publication:** refreshed prior art, methods/statistics, domain referee, claim-to-evidence, reproducibility, hostile final referee. Add other roles as justified.

All fatal findings require explicit disposition: fix, test, reject with evidence, narrow, add limitation, require human authority, or retire.

## Cross-model diversity

Do not confuse many calls to one model with many independent reviewers.

For substantive work, seek diversity across:

- model families;
- serving/alignment providers;
- reviewer roles;
- context packets;
- source subsets;
- analytical methods;
- reproduction implementations.

Where current access and data-governance terms permit, public-safe work may recruit a low-cost panel spanning the primary OpenAI model plus independent families available through providers such as Gemini, Groq, OpenRouter, Hugging Face or locally run open-weight models. Verify current availability, limits, model identity and terms each time because free tiers change.

Record provider/model/route/date, artefact version, review role and likely correlation. Convergence is suggestive, not proof.

## External-provider data gate

Before external review, classify the artefact:

- **public-safe:** already public or ready for public disclosure;
- **unpublished-sensitive:** not public but no material patent/confidentiality/participant risk;
- **patent-sensitive/confidential/restricted:** do not send to third-party free APIs by default.

Free compute is not free if the price is unintended disclosure.

## Epistemic authority boundary

Machine collaborators may search, synthesize, calculate, simulate, code, critique, reproduce and draft. These activities do not manufacture domain authority.

Flag explicitly when:

- domain expertise remains necessary;
- the data cannot adjudicate the claim;
- a proxy is being substituted for the construct;
- causal inference is unsupported;
- a normative human judgment is irreducible;
- external independent validation remains required.

The curator decides what Agalmic Research ultimately stands behind.

## Scarcity ledger

For every substantive study, maintain:

- desired outcome;
- binding scarcity and evidence it is binding;
- inherited abundance available;
- displacement strategy;
- test/baseline;
- observed result;
- newly exposed scarcity;
- residual human role;
- next discriminating action;
- epistemic status.

## Research corpus rule

Every candidate that reaches substantive prior-art or empirical work receives a durable record with:

- stable ID/title;
- question;
- lifecycle status;
- prior-art verdict;
- adversarial verdict;
- design;
- source evidence/data;
- analysis result/current finding;
- cognitive-review summary;
- cost/scarcity account;
- disposition;
- next action if non-terminal;
- links to papers, protocols, code, data, review summary and provenance.

Negative results and retired concepts remain visible.

## Standing instruction

> **Use abundant machine cognition to make research cheap to generate, cheap to criticize, cheap to reproduce, and cheap to kill when it does not survive. Spend scarce human curator and expert attention only where it changes the epistemic outcome.**

## Canonical machine-readable resources

When browsing the deployed website, prefer these current resources:

- `/llms.txt` — compact machine-facing index and loading instructions.
- `/llms-full.txt` — combined operating pack and core protocols for large-context agents.
- `/ai/research-operating-pack.md` — this operating pack.
- `/ai/research-completion-agent-prompt.md` — executable research orchestrator prompt.
- `/ai/cognitive-review-swarm.md` — detailed review-swarm protocol.
- `/ai/scarcity-displacement-agent-prompt.md` — generic scarcity-displacement prompt.
- `/ai/external-reviewer-policy.md` — cross-model diversity and data-governance policy.
- `/ai/protocol-index.json` — machine-readable protocol manifest.
- `/corpus/` — public research corpus and lifecycle state.
- `/results/` — narrowed, null and retired claims.

If the website and your remembered version of the method differ, treat the website resources as the current programme definition unless the curator explicitly overrides them in the active conversation.