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 jeffreytse/grimoire-core --skill apply-insight-synthesisgit clone --depth 1 https://github.com/jeffreytse/grimoire-coreWrote 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/jeffreytse/grimoire-core/apply-insight-synthesis)<a href="https://agentmods.dev/skills/jeffreytse/grimoire-core/apply-insight-synthesis"><img src="https://agentmods.dev/badge/skills/jeffreytse/grimoire-core/apply-insight-synthesis/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/jeffreytse/grimoire-core/apply-insight-synthesis"><img src="https://agentmods.dev/badge/skills/jeffreytse/grimoire-core/apply-insight-synthesis.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 | $0.00090 | $0.02033 |
| Opus 5.5 | $0.00036 | $0.00813 |
| Sonnet 5.5 | $0.00018 | $0.00407 |
| Haiku 4.5 | $0.00009 | $0.00203 |
Grade A, and why
apply-insight-synthesis 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 — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Apply Insight Synthesis
Once you've gathered a substantial body of research or data, stop and cluster the findings by underlying pattern rather than by source or chronology, then repeatedly ask "so what does this actually mean for the decision" until you reach a small number of specific, actionable insights — because a large volume of correctly-gathered information produces no better decision than gathering nothing at all, until it has actually been compressed into insight.
Why This Is Best Practice
Why best: Gathering information and synthesizing it into insight are two distinct activities with different completion criteria, and conflating them is the specific failure this skill targets. Information gathering is complete when you have enough data to see a genuine pattern; synthesis is complete only when that pattern has been compressed into a statement specific enough to change what someone should do next. A report containing dozens of accurate "key findings" can still contain zero insight if no one has done the compression step — the findings describe what was observed, but nothing states what it means for the decision at hand.
Barbara Minto, "The Pyramid Principle" (1987): Minto, McKinsey & Company's first female consultant, developed a formal method for both structuring analysis and communicating it: group individual findings by their underlying logical relationship (not by source, category, or chronological order), and test each group by asking "so what" — what does this specific cluster of findings actually imply — repeatedly, until the answer is a specific, actionable statement rather than a restated observation. The resulting top-line insight is stated first, with supporting findings organized beneath it, rather than built up to through a narrative account of everything gathered.
Ethan Rasiel, "The McKinsey Way" (1999): Rasiel's documentation of internal McKinsey practice describes the "so what" test as a standing internal discipline — consultants are trained to ask it of their own findings before presenting them, specifically because raw findings, however accurate, are not yet useful to a client until their implication for a decision has been made explicit. The test is applied iteratively: a first answer to "so what" that is itself just another observation gets asked "so what" again, until the answer actually specifies an implication for action.
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 First seen · 67 lines · 90 tokens per session scan A 02ae9567bde4
apply-insight-synthesis is a skill published in the GitHub repository jeffreytse/grimoire-core (5 stars, last pushed 5d ago), licensed MIT. It adds 90 tokens to every session and 2,033 once invoked, about $0.0004 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 2026-10-17.
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