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 pipeline-devicesgit 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/pipeline-devices)<a href="https://agentmods.dev/skills/agentii-ai/agentii-investment-intelligence/pipeline-devices"><img src="https://agentmods.dev/badge/skills/agentii-ai/agentii-investment-intelligence/pipeline-devices/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/pipeline-devices"><img src="https://agentmods.dev/badge/skills/agentii-ai/agentii-investment-intelligence/pipeline-devices.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 | $0.00042 | $0.01469 |
| Opus 5.5 | $0.00017 | $0.00588 |
| Sonnet 5.5 | $0.00008 | $0.00294 |
| Haiku 4.5 | $0.00004 | $0.00147 |
Grade A, and why
pipeline-devices 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 — 152 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Methodology inspired by publicly taught medtech pipeline frameworks; all text is an original paraphrase.
Defaults
| Parameter | Default Value | Rationale |
|---|---|---|
| asset_scope | all disclosed devices | Full-pipeline enumeration first |
| stage_lens | feasibility + pivotal | Design-iteration stages, not drug-phase labels |
| reimbursement_aware | true | Value framed against coverage paths, not raw approval |
| post_market_scan | true | Obligations and surveillance state surfaced |
| value_frame | risk-adjusted | Modeled value = unadjusted peak x POS, both shown |
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). Confirm ticker resolution via search_companies before asset queries.
Triggers
- "Analyze [ticker]'s device pipeline."
- "What is in [ticker]'s feasibility and pivotal portfolio?"
- "Where is each of [ticker]'s devices in its design cycle?"
- "Which of [ticker]'s programs are approaching submission?"
- "What RWE studies support [ticker]'s devices?"
- "What post-market obligations does [ticker] carry?"
- "Risk-adjust [ticker]'s device pipeline."
- "What is the next catalyst for each of [ticker]'s devices?"
- "How does [ticker]'s device pipeline compare to peers?"
- "Which device classifications (PMA/De Novo/510(k)) map to [ticker]'s pipeline?"
- "Map [ticker]'s devices by clinical stage and indication."
- "What does [ticker]'s pipeline say about its reimbursement path?"
Production Grounding
- Device stage ladder: feasibility (first-in-human, small N) → pivotal (registrational) → submission (PMA / De Novo / 510(k)) → post-market. Devices iterate through design versions; design freezes and iteration cycles are the pipeline milestones, not drug-style phases.
- Pivotal design varies by classification: PMA demands the highest evidence bar, De Novo novel classification, 510(k) equivalence — the pipeline's value path depends on the expected track.
- RWE studies (registries, claims analyses) increasingly support coverage and label expansion; treat them as pipeline assets with their own timelines.
- Post-market obligations (surveillance studies, MDR reporting) shape long-term liability and the re-approval path.
- Reimbursement-aware framing: pipeline value assumes a coverage path; an asset without one is discounted.
- Grounding detail lives in
references/knowledge-frameworks.md.
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 329b0593e310
- 1mo ago First seen · 152 lines · 42 tokens per session scan A 0cd01e7e8d1d
pipeline-devices is a skill published in the GitHub repository agentii-ai/agentii-investment-intelligence (208 stars, last pushed yesterday), licensed Apache-2.0. It adds 42 tokens to every session and 1,469 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 2026-09-15.
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