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 hollandkevint/data-product-operator --skill research-synthesis-datagit clone --depth 1 https://github.com/hollandkevint/data-product-operatorWrote 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/hollandkevint/data-product-operator/research-synthesis-data)<a href="https://agentmods.dev/skills/hollandkevint/data-product-operator/research-synthesis-data"><img src="https://agentmods.dev/badge/skills/hollandkevint/data-product-operator/research-synthesis-data/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/hollandkevint/data-product-operator/research-synthesis-data"><img src="https://agentmods.dev/badge/skills/hollandkevint/data-product-operator/research-synthesis-data.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 | $0.00056 | $0.00849 |
| Opus 5 | $0.00028 | $0.00425 |
| Sonnet 5 | $0.00011 | $0.00170 |
| Haiku 4.5 | $0.00006 | $0.00085 |
Grade A, and why
research-synthesis-data 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 10d 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 — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Evidence Hierarchy
Not all evidence is equal. Rank sources in this order:
- Observed workarounds (strongest) — Someone built something to solve this problem. Documented in
data-consumer-discoveryworkaround archaeology format. Time invested = validated demand. - Usage data — Query logs, dashboard access patterns, API call frequency. Behavior over opinions.
- Quality incidents — Support tickets, data bug reports, escalations. Pain that generated action.
- Consumer quotes — Direct statements from interviews. 3+ independent quotes on the same theme = a pattern.
- Stakeholder requests (weakest) — What someone asked for. Often a solution masking a different problem.
CRITICAL: When usage data contradicts interview data, usage data wins. People describe aspirational workflows. Logs show actual ones.
Atomic Research Chain
Build insights from the bottom up. Every level must trace to the one below it.
Nuggets
Raw observations tagged with source and date. One fact per nugget.
Example: [Interview: Sarah, Analytics Lead, 2024-01-15] Spends 4 hours every Monday rebuilding the regional performance report from 3 separate data exports.
Tag each nugget: source type (interview, log, ticket, observation), consumer segment (Explorer, Reporter, Decision-maker, Builder), and topic.
Patterns
Three or more nuggets from independent sources pointing to the same conclusion. Less than three is anecdotal.
Example: 3 of 5 analytics leads manually combine data from 3+ sources weekly. Average time: 3.5 hours. All distrust the automated report because "the numbers don't match what I pull manually."
Insights
Patterns interpreted in context. Answers "so what?"
Example: Regional reporting is the highest-pain workaround across analytics. The root cause is inconsistent metric definitions across source systems, not missing data. Fixing the semantic layer would eliminate 15+ hours/week of manual reconciliation.
Recommendations
Insights translated into action. Each recommendation links to a specific skill or command:
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.
- 10d ago First seen · 77 lines · 56 tokens per session scan A a3f2539d2e7d
research-synthesis-data is a skill published in the GitHub repository hollandkevint/data-product-operator (3 stars, last pushed yesterday), licensed MIT. It adds 56 tokens to every session and 849 once invoked, about $0.0003 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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