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 Tx1207/hello-scholar --skill synthesizegit clone --depth 1 https://github.com/Tx1207/hello-scholarWrote 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/tx1207/hello-scholar/synthesize)<a href="https://agentmods.dev/skills/tx1207/hello-scholar/synthesize"><img src="https://agentmods.dev/badge/skills/tx1207/hello-scholar/synthesize/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/tx1207/hello-scholar/synthesize"><img src="https://agentmods.dev/badge/skills/tx1207/hello-scholar/synthesize.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 | $0.00060 | $0.00739 |
| Opus 5.5 | $0.00024 | $0.00296 |
| Sonnet 5.5 | $0.00012 | $0.00148 |
| Haiku 4.5 | $0.00006 | $0.00074 |
Grade A, and why
synthesize 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 — 36 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Synthesize
Help the reader understand what the material changes about a problem, why that judgment follows, and what can be reused. Do the selection and reasoning in the text so the reader does not reconstruct it from scattered notes or links.
Choose the question worth answering
Infer the reader and purpose from the request; ask only when the missing distinction would materially change the content. A single fragment of thinking is as valid an input as a set of Runs. Identify the problem, the earlier understanding or alternatives, and the uncertainty that matters now. Select content by its effect on understanding or a decision, not by effort spent or chronology. Keep a negative result, contradiction, or limitation whenever omitting it would change the meaning.
Adapt the emphasis to the use:
- Organizing ideas: connect the objective, assumptions, tensions, and options; separate a useful direction from an untested explanation.
- Experiment summary: what comparison was made, whether it was valid, and how far it supports or challenges the claim.
- Research notes: changes in understanding, plausible mechanisms, counterexamples, and methods that may help related problems.
- Weekly report: meaningful progress, findings, and decisions tied to the goal and the next work.
The same evidence keeps the same factual meaning across audiences.
Relate evidence to judgment
Read sources before interpreting them. Identify which observations support each other, answer different parts of the question, conflict, or cannot be compared, and check the conditions that decide this: evaluation criteria, data, baseline, operating assumptions. Do not average incompatible results or keep only the favorable ones.
State the change in understanding when the material supports one: what was learned, which interpretation became more or less credible, which option was excluded, what remains open. If the notes show only completed work or an unchanged uncertainty, say so; do not manufacture a discovery. Treat a source author's conclusion as a claim to check against its evidence. When results disagree without an established reason, keep the disagreement and name what would distinguish the explanations. Insufficient evidence for a benefit does not show a method can never work.
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.
- 9d ago First seen · 36 lines · 60 tokens per session scan A 88374911cb48
synthesize is a skill published in the GitHub repository Tx1207/hello-scholar (10 stars, last pushed 4d ago), licensed MIT. It adds 60 tokens to every session and 739 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-10-21.
Other skills, from other repositories
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…