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.
npx skills add lemur47/logic --skill pertgit clone --depth 1 https://github.com/lemur47/logicWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/lemur47/logic/pert)<a href="https://agentmods.dev/skills/lemur47/logic/pert"><img src="https://agentmods.dev/badge/skills/lemur47/logic/pert/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/lemur47/logic/pert"><img src="https://agentmods.dev/badge/skills/lemur47/logic/pert.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.02057 |
| Opus 5.5 | $0.00000 | $0.00823 |
| Sonnet 5.5 | $0.00000 | $0.00411 |
| Haiku 4.5 | $0.00000 | $0.00206 |
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 1mo 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.
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.
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.
- 1mo ago First seen · 224 lines · 0 tokens per session scan A ef19331da9b8
pert is a skill published in the GitHub repository lemur47/logic (2 stars, last pushed 1mo 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.
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