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 agentii-ai/agentii-investment-intelligence --skill trial-readout-analysisgit clone --depth 1 https://github.com/agentii-ai/agentii-investment-intelligenceWrote 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/agentii-ai/agentii-investment-intelligence/trial-readout-analysis)<a href="https://agentmods.dev/skills/agentii-ai/agentii-investment-intelligence/trial-readout-analysis"><img src="https://agentmods.dev/badge/skills/agentii-ai/agentii-investment-intelligence/trial-readout-analysis/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/agentii-ai/agentii-investment-intelligence/trial-readout-analysis"><img src="https://agentmods.dev/badge/skills/agentii-ai/agentii-investment-intelligence/trial-readout-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00077 | $0.01632 |
| Opus 5.5 | $0.00031 | $0.00653 |
| Sonnet 5.5 | $0.00015 | $0.00326 |
| Haiku 4.5 | $0.00008 | $0.00163 |
Grade A, and why
trial-readout-analysis 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 1mo 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 — 159 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Methodology inspired by publicly taught clinical-trial frameworks; all text is an original paraphrase.
Defaults
| Parameter | Default Value | Rationale |
|---|---|---|
| scrutiny_axes | all six + tolerability | Safety/stats/subgroups/missing data/endpoints/benefit-risk + tolerability/persistence as co-equal axis |
| lattice_comparators | SoC + class peers | Every readout is placed against standard of care and same-class peers |
| safety_imbalance | defer to outcomes | Small-N safety imbalances defer the verdict to a larger outcomes trial |
| conflict_policy | materiality-rated | Conflicting readings surfaced verbatim, rated by materiality or deferred |
| outcome_framing | base/bull/bear | Binary readouts need scenario sizing |
| reaction_context | historical cases | Size moves from past analogues |
Preflight
Run canonical pre-flight per contracts/preflight.md. Include the X-Agentii-Trace header on every tool call per contracts/x-agentii-trace-header.md — carry the _run_id from your first tool result and name yourself (and your parent, if you were spawned).
Triggers
- "Evaluate [ticker]'s upcoming trial readout."
- "What should I look for in [trial]'s data?"
- "Size the readout for [drug] phase 3."
- "What did the AdCom-style scrutiny say about similar trials?"
- "Base/bull/bear for [ticker]'s readout."
- "Which endpoints matter for [trial]?"
- "How has the market reacted to similar readouts?"
- "Readout checklist for [ticker]."
- "Is this trial design adequate?"
- "What are the red flags in [trial]'s design?"
Production Grounding
- Readout ≠ approval: phase-3 success is necessary but not sufficient; FDA re-analyzes sponsor data.
- Apply the six scrutiny axes (safety signals, statistical adequacy, subgroup analyses, missing data, endpoint appropriateness, benefit-risk) plus tolerability/persistence as a co-equal axis — discontinuation rates, dose reductions, and AE-driven dropout often decide commercial uptake. The 道/法 frameworks in
references/knowledge-frameworks.mdare the authoritative checklist. - Cross-trial lattice: every readout is placed against standard of care and same-class peers on aligned endpoints; a readout judged in isolation is incomplete.
- Safety-imbalance deferral: a safety imbalance seen at readout scale defers the verdict to a larger outcomes trial; never over-weight small-N imbalances.
- Materiality-rated conflicts: conflicting characterizations of the same data are surfaced verbatim and rated by materiality or deferred — never averaged.
- Readout framing: readout design, then stock sizing (binary-risk expected value), then historical analogue comparison.
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.
- 1mo ago Changed 4d79eb77778b
- 1mo ago Changed · +11 lines · +21 tokens per session 2183d3b37777
- 1mo ago Changed · +1 lines 47dd82675774
- 1mo ago First seen · 147 lines · 56 tokens per session scan A 019a0805f4b6
trial-readout-analysis is a skill published in the GitHub repository agentii-ai/agentii-investment-intelligence (208 stars, last pushed yesterday), licensed Apache-2.0. It adds 77 tokens to every session and 1,632 once invoked, about $0.0003 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 2026-09-05.
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