Detecting Pv Signals

What it does

When your agent uses it

• The user wants to mine spontaneous-report data for drug-reaction associations
• Build a 2x2 contingency table
• Compute a Proportional Reporting Ratio
• Reporting Odds Ratio

Example prompts

• “Use the detecting-pv-signals skill to compute disproportionality signals — PRR, ROR, EBGM, and IC (BCPNN) — over FAERS / OpenFDA drug-event data to…”
• “/detecting-pv-signals”

Requirements

• Python 3

Workflow steps

7 steps, taken from the first numbered list in SKILL.md.

1. 1Pick the population. Decide your denominator: all of FAERS, or a
2. 2Resolve the drug field. Prefer patient.drug.openfda.generic_name (RxNorm
3. 3Use .exact for the reaction field so "injection site reaction" counts as
4. 4Build the 2x2 with the cell counts above. Verify a + b + c + d == N.
5. 5Compute PRR and ROR with CIs; add IC025 / EB05 for small counts.
6. 6Apply thresholds (e.g. PRR ≥ 2, χ² ≥ 4, a ≥ 3) — but treat them as a
7. 7Hand flagged pairs to a safety scientist for medical review, confounder

What it can do on your machine

Read from SKILL.md and the folder at commit 34d7b8c. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  No scripts in the folder and no shell commands in SKILL.md (its code samples are python).

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  Hosts in commands or code, which the agent is likely to contact:

  • api.fda.gov

  Also links to:

  • open.fda.gov
  • pubmed.ncbi.nlm.nih.gov
  • tandfonline.com
  • cioms.ch

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Detecting Pv Signals loads about 2.3k tokens when it runs. Until then it costs about 215 tokens; SKILL.md has 821 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~215

When it runs · the whole SKILL.md, loaded when a task matches

~2.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from maziyarpanahi/openmed at commit 34d7b8c, republished under its Apache-2.0 licence (© maziyarpanahi). 821 words, ~2,329 tokens.

name

detecting-pv-signals

description

Computes disproportionality signals — PRR, ROR, EBGM, and IC (BCPNN) — over FAERS / OpenFDA drug-event data to flag potential safety signals. Use when the user wants to mine spontaneous-report data for drug-reaction associations, build a 2x2 contingency table, compute a Proportional Reporting Ratio or Reporting Odds Ratio, run Empirical Bayes (EBGM/EB05) or Information Component shrinkage, or screen a drug for over-reported reactions. Trigger keywords: disproportionality, signal detection, PRR, ROR, EBGM, EB05, IC, BCPNN, MGPS, 2x2 table, signal of disproportionate reporting, SDR, OpenFDA, FAERS. Pairs adjacent to OpenMed: aggregate de-identified, coded cases (from reporting-adverse-events) then query the public OpenFDA /drug/event count API to build the contingency table. Reaction terms are MedDRA PTs (licensed, user-supplied).

license

Apache-2.0

metadata.project

OpenMed

metadata.category

safety-pharmacovigilance

metadata.pairs

adjacent

metadata.version

1.0

import requests

BASE = "https://api.fda.gov/drug/event.json"

def fda_count(search: str | None, count_field: str) -> int:
    """Total reports matching `search` (sum of the .exact histogram)."""
    params = {"count": count_field}
    if search:
        params["search"] = search
    r = requests.get(BASE, params=params, timeout=30)
    if r.status_code == 404:        # OpenFDA returns 404 for an empty result set
        return 0
    r.raise_for_status()
    return sum(row["count"] for row in r.json()["results"])

def cell_count(search: str | None) -> int:
    """Number of reports matching `search` (use meta.results.total via limit=1)."""
    params = {"limit": 1}
    if search:
        params["search"] = search
    r = requests.get(BASE, params=params, timeout=30)
    if r.status_code == 404:
        return 0
    r.raise_for_status()
    return r.json()["meta"]["results"]["total"]

# Build the 2x2 for warfarin x "gastrointestinal haemorrhage".
DRUG = 'patient.drug.openfda.generic_name:"warfarin"'
RXN  = 'patient.reaction.reactionmeddrapt.exact:"gastrointestinal haemorrhage"'

a = cell_count(f"{DRUG}+AND+{RXN}")          # drug & reaction
b = cell_count(DRUG) - a                      # drug, not reaction
c = cell_count(RXN) - a                       # reaction, not drug
N = cell_count(None)                          # total reports in FAERS
d = N - a - b - c

import math

def prr(a, b, c, d):
    return (a / (a + b)) / (c / (c + d))

def ror(a, b, c, d):
    return (a * d) / (b * c)

def ror_ci(a, b, c, d):
    lnror = math.log((a * d) / (b * c))
    se = math.sqrt(1/a + 1/b + 1/c + 1/d)     # Woolf's method
    lo, hi = math.exp(lnror - 1.96 * se), math.exp(lnror + 1.96 * se)
    return lo, hi

def ic(a, b, c, d):
    n = a + b + c + d
    expected = (a + b) * (a + c) / n
    return math.log2(a / expected) if a and expected else float("nan")

print("PRR", round(prr(a, b, c, d), 2))
print("ROR", round(ror(a, b, c, d), 2), "95% CI", ror_ci(a, b, c, d))
print("IC",  round(ic(a, b, c, d), 2))

© maziyarpanahi, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/detecting-pv-signals of maziyarpanahi/openmed.

Open the folder on GitHubat commit 34d7b8c