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 product-on-purpose/thinking-framework-skills --skill think-linear-model-aggregationgit clone --depth 1 https://github.com/product-on-purpose/thinking-framework-skillsWrote 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/product-on-purpose/thinking-framework-skills/think-linear-model-aggregation)<a href="https://agentmods.dev/skills/product-on-purpose/thinking-framework-skills/think-linear-model-aggregation"><img src="https://agentmods.dev/badge/skills/product-on-purpose/thinking-framework-skills/think-linear-model-aggregation/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/product-on-purpose/thinking-framework-skills/think-linear-model-aggregation"><img src="https://agentmods.dev/badge/skills/product-on-purpose/thinking-framework-skills/think-linear-model-aggregation.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 | $0.00078 | $0.01064 |
| Opus 5 | $0.00039 | $0.00532 |
| Sonnet 5 | $0.00016 | $0.00213 |
| Haiku 4.5 | $0.00008 | $0.00106 |
Grade A, and why
think-linear-model-aggregation 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 9d 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 — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Linear-Model Aggregation
For a judgment you make over and over - screening candidates, scoring leads, triaging tickets - holistic expert intuition is unreliable mainly because it is inconsistent: the same expert scores the same case differently on different days. A simple mechanical rule removes that: pick a few predictive cues, weight them (even equal weights work), score each case, combine by a fixed formula, and apply it identically every time. The robust, counterintuitive result is that such rules match or beat holistic judgment, because consistency beats brilliance applied erratically. The output is a scoring model. Two honest limits: it is for repeated judgments (not one-off strategic choices), and it is only as good as its cues.
When to Use
- The same kind of evaluative/predictive judgment recurs (screening, lead/deal scoring, triage, prioritizing a queue).
- Gut calls on these are inconsistent or overconfident.
- A few cues with real predictive signal exist.
When NOT to Use
- A genuinely one-off decision among a few options (use
decision-option-review). - No real predictive cues or data exist - do not invent cues and weights (false precision).
- High-stakes judgments about individuals (hiring, lending, justice) where mechanical scoring raises fairness/legal/ethical issues - flag these, do not silently automate.
- When the point is a single strategic call, not a repeatable rule.
Instructions
When asked to build a scoring model, follow these steps:
- State the recurring judgment and the outcome it predicts (and confirm the outcome is eventually measurable). If it is a one-off, stop and route to a decision review.
- Choose a few predictive cues. 3 to 6 cues that plausibly carry real signal; say why each. Resist adding cues that feel thorough but lack validity.
- Assign weights. Default to equal weights unless real data justifies otherwise - the evidence says simple/equal weights capture most of the benefit; do not fake precision.
- Define the per-cue rubric. How each cue is scored, so two people would score a case the same way.
- Set the formula and threshold. How the cue scores combine, and the decision rule (e.g. above X -> advance).
- Mandate consistency, and flag the caveats. State that the model must be applied the same way every time (overriding it on a hunch reintroduces the noise it removes), that it is only as good as its cues, and any fairness/ethical caveat for judgments about people.
- Emit the scoring model per
references/TEMPLATE.md.
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago First seen · 65 lines · 78 tokens per session scan A 1c576d93334d
think-linear-model-aggregation is a skill published in the GitHub repository product-on-purpose/thinking-framework-skills (15 stars, last pushed 23d ago), licensed Apache-2.0. It adds 78 tokens to every session and 1,064 once invoked, about $0.0004 per session on Opus 5. 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 2026-08-30.
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