Neural Nations · CAMS-CAN · DIY Kit

The DIY Kit, Handed Off

Everything needed to run CAMS-CAN scoring by hand in a Claude Project — and everything needed to decide whether to believe what it produces. Four small files, no API key, no installed app: a project-instructions block and two blank schema templates that turn any Claude Project into a working CAMS scorer.

Rubric: CAMS RAW SCORER v1.2-OPT Framework: Kari McKern · Neural Nations Kit status: independent condensation License: Open Science · Common Property
cams-diy-kit.zip
4 files · ~4 KB · project instructions + two blank schema templates + readme
Download

Or read the pieces first: the plain-text README, the project instructions reproduced in full below, and the two blank schema / envelope CSV templates.

CAMS-CAN models any coordinating human system — a nation, a company, a city — as eight institutional nodes, each scored on four dimensions, to surface whether the system is coordinating well or coming apart. This kit packages the scoring rubric into a Claude Project instead of an API key or installed app.

From the kit's own readme: “Framework by Kari McKern, Neural Nations — neuralnations.org / github.com/KaliBond/wintermute. This kit is an independent condensation for hands-on use, not an official Neural Nations release.”

Every entity is scored on the same eight nodes, split into two coordination loops. The split feeds directly into the Bond Strength formula below: two fast-loop nodes bond at full strength, two slow-loop nodes bond at 0.8, and a fast/slow pair bonds at a discounted 0.6. See the model page for the full framework.

Helm
Fast loop
Executive / strategic leadership — head of state, cabinet, board.
Shield
Fast loop
Defence, coercion, risk — military, police, security, compliance.
Lore
Slow loop
Cultural narrative — religion, ideology, media, brand, education.
Stewards
Slow loop
Capital and asset ownership — landowners, investors, finance.
Craft
Fast loop
Production and technical work — industry, engineering, R&D.
Hands
Fast loop
Labour and collective execution — workforce, operations.
Archive
Slow loop
Institutional memory — records, courts, statistics, systems of record.
Flow
Fast loop
Distribution and markets — trade, commerce, logistics, sales.
Four dimensions, scored 1–10 as integers, or NA if ungrounded
DimensionWhat it measuresAnchors
Coherence (C)Internal alignment and coordination clarity9–10 exceptional · 7–8 strong · 5–6 adequate · 3–4 weak · 1–2 collapsed
Capacity (K)Demonstrated ability to perform the node's function9–10 exceptional · 7–8 strong · 5–6 adequate · 3–4 weak · 1–2 collapsed
Stress (S)Rate of breakdown / entropy production — not raw pressure9–10 rupture · 7–8 strained · 5–6 pressured · 3–4 stable · 1–2 thriving
Abstraction (A)Operational sophistication, learning, symbolic reach9–10 exceptional · 7–8 strong · 5–6 adequate · 3–4 weak · 1–2 collapsed
Relabeling for non-nation entities — same eight nodes, same rules
NodeNation (default)CorporationCity
HelmHead of state, cabinetBoard / CEOMayor & council
ShieldMilitary, policeRisk & compliancePolice & fire
LoreReligion, media, educationBrand & cultureCommunity & media
StewardsLandowners, investors, financeFinanceProperty & developers
CraftIndustry, engineeringR&D & engineeringTrades & utilities
HandsWorkforce, operationsWorkforce & operationsMunicipal workforce
ArchiveRecords, courts, statisticsSystems of recordPlanning & records
FlowTrade, commerce, logisticsMarkets & salesLocal commerce

This is the same v1.2-OPT scoring rubric behind the CAMS Scorer hosted tool and local app, condensed for a Claude Project instead of an API key. Paste the instructions once; every conversation in that project can then score, calculate, and interpret CAMS data on request.

  1. Set up once.Paste the project instructions below into a Claude Project's "Project instructions" field.
  2. “Score [X] from [year]”Runs the SCORE job for that range — CSV only, eight rows per year, no prose.
  3. “Now calculate” / “get node values”Runs the CALC job on the latest SCORE output — adds Node Value and Bond Strength.
  4. “Run five scorers” / “ensemble”Repeats SCORE five times independently, no cross-run peeking, averages per node-year, then runs one CALC pass on the means.
  5. “Switch to corporate/city mapping”Relabels the same eight nodes per the table above and keeps scoring the same entity under that lens.
PROJECT_INSTRUCTIONS.txt — paste this whole block
CAMS-CAN ANALYSIS — PROJECT INSTRUCTIONS
Paste this entire block into a Claude Project's "Project instructions" field.
Any conversation in that project can then score, calculate and interpret CAMS
data without further setup.

## WHAT THIS PROJECT DOES
Models a nation, corporation or city as eight interacting institutional nodes,
each scored on four dimensions, to surface coordination health over time.
Two jobs, always kept separate:
  SCORE  — read history/evidence, emit raw integer scores per node-year.
  CALC   — take raw scores, compute Node Value and Bond Strength. Never
           invent scores while calculating; never skip calculation while scoring.

## THE EIGHT NODES (canonical order)
Helm      Executive / strategic leadership (head of state, cabinet, board)
Shield    Defence / coercion / risk (military, police, security, compliance)
Lore      Cultural narrative (religion, ideology, media, brand, education)
Stewards  Capital and asset ownership (landowners, investors, finance)
Craft     Production and technical work (industry, engineering, R&D)
Hands     Labour and collective execution (workforce, operations)
Archive   Institutional memory (records, courts, statistics, systems of record)
Flow      Distribution and markets (trade, commerce, logistics, sales)

Default mapping is for nations. For a corporation, relabel:
  Helm=Board/CEO   Shield=Risk & Compliance   Lore=Brand & Culture
  Stewards=Finance   Craft=R&D & Engineering   Hands=Workforce & Operations
  Archive=Systems of Record   Flow=Markets & Sales
For a city, relabel:
  Helm=Mayor & Council   Shield=Police & Fire   Lore=Community & Media
  Stewards=Property & Developers   Craft=Trades & Utilities
  Hands=Municipal Workforce   Archive=Planning & Records   Flow=Local Commerce

## THE FOUR DIMENSIONS (score 1-10 integers, or NA if ungrounded)
Coherence (C)    internal alignment and coordination clarity
Capacity (K)     demonstrated ability to perform the node's function
Stress (S)       rate of breakdown / entropy production, not raw pressure
Abstraction (A)  operational sophistication, learning, symbolic reach

Anchors, applied per dimension:
  9-10  exceptional — requires historically unusual evidence
  7-8   strong / integrated / effective
  5-6   adequate — functions despite tension
  3-4   weak / divided / unreliable
  1-2   collapsed / fragmented / unable to perform

## SCORING RULES
- Score operational truth, not formal structure or stated intent.
- Score each node independently; do not smooth for symmetry.
- A score under 5 or over 8 requires a concrete, named piece of evidence for
  that specific node. General sentiment or reputation is not evidence.
- A move of 2+ points from the prior period requires either two distinct
  pieces of node-specific evidence, or one unmistakable structural break
  (regime change, bankruptcy, merger, disaster).
- If evidence is genuinely insufficient for a score, output NA — never guess,
  never copy the previous period's value by default.
- Search the web when the subject is recent, obscure, or thinly documented in
  your training knowledge; do not search to re-confirm well-known history.

## OUTPUT FORMAT — SCORING
Return CSV only, no prose:
Society,Year,Node,Coherence,Capacity,Stress,Abstraction
One row per node, eight rows per year, nodes in the canonical order above.

## FORMULAS — CALCULATION (JUNO v1.2-Final — operators unchanged since v1.0)
Node Value:
  NV_i = C_i + K_i - S_i + 0.5*A_i

Bond Strength (per node, mean bond to the other seven):
  q_i  = clamp((0.6*C_i + 0.4*A_i) / 10, 0, 1)
  B_ij = T_ij * sqrt(q_i * q_j) * 2^(-(S_i+S_j)/10)
  BS_i = mean(B_ij for all j != i)

  T_ij topology weight:
    1.0  both nodes in the fast loop  {Helm, Shield, Craft, Hands, Flow}
    0.8  both nodes in the slow loop  {Lore, Stewards, Archive}
    0.6  one fast, one slow (cross-layer)

Stress is the dominant lever: raising S on any node drives its bond to every
other node down exponentially, regardless of how strong its own Coherence
and Abstraction are.

## OUTPUT FORMAT — CALCULATION
Society,Year,Node,Coherence,Capacity,Stress,Abstraction,Node Value,Bond Strength
Node Value to 1 decimal place, Bond Strength to 4 decimal places.

## EVIDENCE-GATED ENSEMBLE (camnations5n)
A single pass already illuminates. For a claim that needs to survive scrutiny,
run five independent passes under the CAMS RAW SCORER v1.2-OPT rubric (no
cross-pass peeking, no pre-scoring narrative, identical prompt every time),
propagate NA at the dimension level rather than guessing, then run one CALC
pass on the surviving means. Report scorer SD, n_eff and NA_rate alongside
the mean — see schema_template.csv for the envelope columns.

## WHAT TO SAY WHEN ASKED
"Score [X] from [year]"              -> run SCORE for that range, CSV only.
"Now calculate" / "get node values"  -> run CALC on the latest SCORE output.
"Run five scorers" / "ensemble"      -> repeat SCORE five times independently
  (no cross-run peeking, no averaging mid-run), average C/K/S/A per node-year,
  then run one CALC pass on the means.
"Switch to corporate/city mapping"   -> relabel nodes per the mappings above
  and keep scoring the same entity under that lens.

## SOURCE
Node architecture, dimensions, and the Node Value / Bond Strength formulas
follow the CAMS framework published by Kari McKern at neuralnations.org (open
repository: github.com/KaliBond/wintermute). This instructions block is an
independent condensation for hands-on use in a Claude Project — not an
official Neural Nations release.
schema_template.csv — one year, eight nodes
Society,Year,Node,Coherence,Capacity,Stress,Abstraction,Node Value,Bond Strength
,,Helm,,,,,,
,,Shield,,,,,,
,,Lore,,,,,,
,,Stewards,,,,,,
,,Craft,,,,,,
,,Hands,,,,,,
,,Archive,,,,,,
,,Flow,,,,,,
envelope_schema_template.csv — for the five-pass ensemble
Society,Year,Node,C_sd,K_sd,S_sd,A_sd,V_range,V_min,V_max,n_eff,NA_rate
,,Helm,,,,,,,,,
,,Shield,,,,,,,,,
,,Lore,,,,,,,,,
,,Stewards,,,,,,,,,
,,Craft,,,,,,,,,
,,Hands,,,,,,,,,
,,Archive,,,,,,,,,
,,Flow,,,,,,,,,

The rubric is built to make a wrong score hard to produce quietly: every extreme value needs a named reason, every jump needs two independent pieces of evidence or a structural break, and missing evidence has to surface as NA, never a guess. Below is what's been independently measured about how well it actually holds up — and what hasn't been tested yet. If you want to check our work yourself, see the peer review & validation invite.

Two independent panels have been run under this family of prompts, each as 18 formal scoring passes nested across three model families (Claude, Grok, Kimi), reduced to intraclass correlation. ICC(2,k) is absolute agreement across the pooled three-model ensemble; ICC(3,k) is consistency — whether the models agree on relative movement even where their absolute scale differs.

Reliability by panel — both under the same three-family, 18-pass design
PanelPromptICC(2,k) absolute95% CIICC(3,k) consistency95% CI
Australia, 2020–2025v1.1
0.710
not reported
0.885
not reported
United States, 2020–2025v1.2-OPT
0.691
0.427–0.813
0.907
0.759–0.962
Read this carefully, not optimistically.
  • This is not a controlled A/B of the two prompt versions — country and prompt version changed together. The overlapping confidence intervals are reassuring, not a clean causal claim.
  • The Australia write-up reported ICC point estimates only; the United States replication is the one with full bootstrap CIs on all four statistics.
  • ICC(2,k) sitting well below ICC(3,k) on both panels means the three model families track each other's direction of movement closely, but disagree on absolute scale.
  • Both panels establish scorer reliability and prompt-sensitivity — not historical criterion validity.

The company and city node mappings above were only brought up to this rubric structure on 2026-08-04, and have not yet had a full reliability study run against them. A smaller 3-pass, single-provider check (Grok, Tesla 2020–2022 and Detroit 2019–2021, 24 targets each) came back clean — pairwise raw-score correlation 0.85–0.92, zero NA cells — but treat that as an early functional signal, not a validated figure on the order of the two panels above.

Not yet established
  • Historical criterion validity — whether scores match actual institutional history, independent of model agreement.
  • Generalisability beyond the two tested country panels, or beyond the small company/city smoke test.
  • A same-panel, same-country controlled comparison of v1.1 against v1.2-OPT.
  • Full reliability study for the company and city variants.

Nothing downstream of the raw scores is hidden. CALC takes eight nodes' worth of C/K/S/A and produces two derived figures per node — both closed-form, both reproducible by hand from the schema template above.

NV_i = C_i + K_i − S_i + 0.5 × A_i

Node Value rewards Coherence and Capacity, penalises Stress directly, and gives Abstraction half weight.

q_i = clamp((0.6 × C_i + 0.4 × A_i) / 10, 0, 1)
B_ij = T_ij × √(q_i × q_j) × 2−(S_i+S_j)/10
BS_i = mean(B_ij for all j ≠ i)

Bond Strength is the mean bond from one node to the other seven. Stress is the dominant lever: raising S on any single node drives its bond to every other node down exponentially, regardless of how strong its own Coherence and Abstraction are.

Topology weight T_ij — the fast/slow split from the node grid above
PairWeight
Both nodes in the fast loop {Helm, Shield, Craft, Hands, Flow}1.0
Both nodes in the slow loop {Lore, Stewards, Archive}0.8
One fast, one slow (cross-layer)0.6

Formula version: JUNO v1.2-Final (Node Value and Bond Strength operators unchanged since v1.0 — see production spec). Node Value reported to 1 decimal place, Bond Strength to 4.

Think you can break it?

Run your own independent pass, audit the evidence rules, or replicate the reliability study. We'd rather know where this framework fails than not know.

Peer Review & Validation Invite