ml4t-multi-agent-forecasting skillA
ml4t-multi-agent-forecasting is agent-read markdown (skill) from ml4t/skills: Multi-agent probability forecasting with diversity, aggregation, and debate controls. Use when combining several agent forecasts or evaluating forecast ensembles..
Indexed from public GitHub and served as immutable, content-addressed versions. Install it pinned to an exact SHA-256 with the mdr CLI, and every file is verified against the hash recorded here before it reaches your agent. The deterministic audit below grades the latest version, and the same file always earns the same grade.
What the file says
# Multi-Agent Forecasting
Multiple agents help only when they add independent information or structured disagreement. Re-running the same prompt at higher temperature usually produces correlated forecasts, not a useful ensemble.
## The Problem
Forecast pipelines often report "agent consensus" from several identical agents. On well-specified macro questions, those agents read the same evidence and return nearly identical probabilities. Averaging correlated forecasts gives false confidence unless the system measures diversity, calibrates probabilities, and stress-tests the consensus with opposing arguments.
## The Pattern
### WRONG
```python
forecasts = [agent.run(question, temperature=0.7) for _ in range(5)]
p_yes = sum(f.p_yes for f in forecasts) / len(forecasts)
print(f"consensus={p_yes:.2%}")
```
### CORRECT
```python
import math
from statistics import mean
def logit(p: float) -> float:
p = min(max(p, 1e-6), 1 - 1e-6)
return math.log(p / (1 - p))
def inv_logit(x: float) -> float:
return 1 / (1 + math.exp(-x))
def neyman_aggregate(probs: list[float], diversity: float) -> float:
avg_logit = mean(logit(p) for p in probs)
…Read the whole file at its exact version.
How to install
mdr add ml4t/skills/ml4t-multi-agent-forecasting@git:20260901.c415df0mdr add ml4t/skills/ml4t-multi-agent-forecasting@sha256:d942cf57d4fe38ffPin to a label to follow the author's releases, or to a sha256 to freeze the exact bytes forever. Either way the resolved hash is written to mdr.lock, and mdr install reproduces it on any machine.
[](https://markdownregistry.com/a/art_rwcav6kwntdhdmkc)
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Versions
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| git:20260901.c415df0 latest | 2026-09-01 | c415df0 | 3,194 B | A | view · diff |
| git:20260528.303089e | 2026-05-28 | 303089e | 3,198 B | A | view |
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (3194 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
ml4t/skills · 19 stars · license Apache-2.0 · pushed 2026-09-24 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_rwcav6kwntdhdmkc GET https://markdownregistry.com/api/v1/resolve?ref=ml4t/skills/ml4t-multi-agent-forecasting GET https://markdownregistry.com/api/v1/blob/d942cf57d4fe38ff0fc765ce862aaca5d37cb8843fe7df54cee9b265f26b9bb3
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