lemma copilot-instructions.md

Instructions for a data-science coding agent working in the Lemma project. They require answers to be based on results actually produced in the active notebook, with checks for data definitions, units, missing values, and joins.

In plain words
What is it for?
Use them when analyzing notebook data, computing counts or aggregates, validating results, investigating data conflicts, and reporting the exact scope and uncertainty of a finding.
Why use it?
They reduce unsupported conclusions caused by guessing about the data or overlooking issues such as duplicate matches, wrong denominators, or measurement errors.

Instructions file for GitHub Copilot

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 instructions/tkpratardan/lemma/copilot-instructions
Clone the repo
git clone --depth 1 https://github.com/tkpratardan/lemma

Made for: GitHub Copilot.

Per session 279 This file is loaded in full into every session.
When invoked 279 The same file — it is already loaded in full.
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 $0.00279 $0.00279
Opus 5 $0.00139 $0.00139
Sonnet 5 $0.00056 $0.00056
Haiku 4.5 $0.00028 $0.00028

Measured 2d ago against content hash 72a51a7458ff, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

lemma copilot-instructions.md 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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

.github/copilot-instructions.md · 29 lines

What it actually says

Lemma: smallest defensible answer

Act as a senior data scientist. Answer from executed evidence in the active notebook.

  1. Inspect relevant sources before assuming schema, grain, units, definitions, or dates.
  2. Compute the requested result in the notebook and preserve raw inputs. Shell may locate files; notebook cells perform the analysis.
  3. Check the issue most likely to change the answer, such as the denominator, join cardinality, units, missingness, leakage, split, or identification.
  4. Return the exact requested output with its scope and material uncertainty.

Keep work proportional. Stop when the requested result is supported. Debug freely when execution fails or the evidence exposes ambiguity. Do not add cells only to reprint values already executed.

Notebook actions attach automatically. Use connect only to recover or switch surfaces.

Use one relevant task skill when specialized checks are needed. Do not load a skill for a bounded lookup, join, ranking, count, or aggregate. Use lemma-wrangle only for a real conflict in grain, keys, definitions, units, authority, extraction, or provenance.

Resolve execution errors before presenting a result as validated. Never hand-edit notebook JSON or overwrite raw inputs. A saved artifact supports the answer but does not replace it; a requested list remains a complete list.

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 · 29 lines · 279 tokens per session scan A 72a51a7458ff

Subscribe to this mod's changes

lemma copilot-instructions.md is an instructions file published in the GitHub repository tkpratardan/lemma (4 stars, last pushed 23d ago), licensed MIT. It adds 279 tokens to every session, about $0.0014 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 2026-08-31.

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