Borrowing it
Nothing to install: this file belongs to morluto/jacobian. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/morluto/jacobian/main/.agents/skills/learn-from-math-agent-trajectories/SKILL.mdgit clone --depth 1 https://github.com/morluto/jacobianWrote 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/morluto/jacobian/learn-from-math-agent-trajectories)<a href="https://agentmods.dev/skills/morluto/jacobian/learn-from-math-agent-trajectories"><img src="https://agentmods.dev/badge/skills/morluto/jacobian/learn-from-math-agent-trajectories/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/morluto/jacobian/learn-from-math-agent-trajectories"><img src="https://agentmods.dev/badge/skills/morluto/jacobian/learn-from-math-agent-trajectories.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Rogue Agent · line 61 Skill modifies its own code, configuration, or behavior at runtime. Self-modification enables an agent to escalate privileges, disable safety constraints, or install persistent backdoors.Fix: Prevent the skill from modifying its own code, SKILL.md, or configuration files. Treat skill files as read-only at runtime.
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 | $0.00025 | $0.00618 |
| Opus 5 | $0.00013 | $0.00309 |
| Sonnet 5 | $0.00005 | $0.00124 |
| Haiku 4.5 | $0.00003 | $0.00062 |
Grade A, and why
learn-from-math-agent-trajectories 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 4d 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 — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Learn from Math Agent Trajectories
Extract reusable Jacobian lessons from a completed or paused investigation; do not resume solving the problem. A correct answer can expose workflow defects, and an unsuccessful search can reveal useful mathematical vocabulary.
Establish the evidence
Record the intended and actual outcome, stopping condition, transcript coverage, and available revision/catalog context. Use observable sources, calls, code, artifacts, corrections, and final claims. Narration alone is not execution evidence; current-main capabilities were not necessarily available in the trace.
Reconstruct decisions that changed correctness, cost, progress, or confidence. For a finding that depends on mathematical claims, numerical or symbolic work, solver semantics, or bespoke code, consult mathematical evidence. Preserve decisive claims and later corrections, with their hypotheses and evidence scope.
Attribute the lesson
Distinguish working capabilities, environment limitations, discovery/selection, execution friction, representation/interoperability, contract/scale defects, missing operations, handoff failures, and caller reasoning. Compare needed postconditions with the session-visible catalog when available; check current source before proposing new work. Handwritten code and tool non-use are leads, not automatic evidence of missing operations.
Separate a reusable operation gap from public-catalog admission. An existing postcondition with a narrow envelope is a scale/backend question. A convenience or theorem-specific assembly does not become a public operation solely because it occurred in the trace. Use the admission contract when proposing publication.
Route only when the requested follow-up needs a deeper workflow:
evaluate-mcp-tool-adoptionfor controlled availability, discovery, or selection;audit-mcp-tool-frictionfor problems after selecting a tool;audit-public-operation-contractsfor a particular mathematical contract; orrecent-conjecture-evaluationsfor a new held-out reliability probe.
What ships with it
2 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.
- 4d ago Changed · -180 lines · -62 tokens per session 09785fa7e25b
- 10d ago First seen · 246 lines · 87 tokens per session scan A 052862ef672b
learn-from-math-agent-trajectories is a skill published in the GitHub repository morluto/jacobian (190 stars, last pushed today), licensed MIT. It adds 25 tokens to every session and 618 once invoked, about $0.0001 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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