pert

A PERT (Program Evaluation and Review Technique) estimation method that combines optimistic, most likely, and pessimistic time estimates into an expected duration and confidence ranges.

In plain words
What is it for?
Use it for sprint planning, task estimates, vendor timelines, and comparing implementation approaches.
Why use it?
It replaces a guess such as “about ten days” with a structured estimate that shows uncertainty and can account for real-world risks.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/lemur47/logic/pert
Any agent
npx skills add lemur47/logic --skill pert
Clone the repo
git clone --depth 1 https://github.com/lemur47/logic

Made for: Claude Code, Codex.

Per session 0 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,057 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00000 $0.02057
Opus 5 $0.00000 $0.01028
Sonnet 5 $0.00000 $0.00411
Haiku 4.5 $0.00000 $0.00206

Measured 2d ago against content hash ef19331da9b8, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

pert scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/pert/SKILL.md · 224 lines

How it starts

The opening of the file, as written. The whole thing — 224 lines — stays where its author put it; the contents beside it link to each section on GitHub.

PERT: Three-Point Estimation with Reality Adjustments

Source: github.com/lemur47/logic

Purpose

Help users produce defensible project estimates using PERT (Program Evaluation and Review Technique) with optional reality adjustments via insight tags. This skill replaces gut-feel single-point estimates with structured three-point estimation and confidence intervals.

When to Use

  • Sprint planning and task estimation
  • Reviewing vendor timelines
  • Comparing implementation approaches
  • Any situation where someone says "it'll take about X days"

Core Formulas

Textbook PERT

Given three estimates:

  • O — Optimistic (everything goes perfectly)
  • M — Most likely (realistic, based on experience)
  • P — Pessimistic (Murphy's Law)
Expected duration:  E = (O + 4M + P) / 6
Standard deviation: σ = (P - O) / 6

Confidence Intervals

68% range:   [E - σ,  E + σ]
95% range:   [E - 2σ, E + 2σ]
99.7% range: [E - 3σ, E + 3σ]

If any lower bound is negative, clamp it to 0.

Reality-Adjusted PERT

Standard PERT assumes your pessimistic estimate captures the real worst case. It rarely does. Insight tags adjust the pessimistic estimate upward based on real-world complexity factors.

Each tag has a multiplier range [min, max]. The user can provide input in two formats:

Format A — Severity (0.0–1.0): Used by the Python API. Interpolates within the tag's range.

tag_multiplier = min + severity × (max - min)

Format B — Direct multiplier (e.g. 1.20×): Used by the web UI at pmo.run. The value is the multiplier itself — no conversion needed. Validate that it falls within the tag's [min, max] range.

How to detect format: If the value is ≤ 1.0, treat it as severity. If the value is > 1.0, treat it as a direct multiplier.

Multiple tags compound:

combined_multiplier = tag1_multiplier × tag2_multiplier × ...
adjusted_P = P × combined_multiplier

Then recalculate E and σ using adjusted_P in place of P.

Read the full file on GitHub · 224 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 224 lines · 0 tokens per session scan A ef19331da9b8

Subscribe to this mod's changes

pert is a skill published in the GitHub repository lemur47/logic (2 stars, last pushed 4d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,057 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.