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 OpenHCSDev/openhcs --skill use-openhcsgit clone --depth 1 https://github.com/OpenHCSDev/openhcsWrote 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/openhcsdev/openhcs/use-openhcs)<a href="https://agentmods.dev/skills/openhcsdev/openhcs/use-openhcs"><img src="https://agentmods.dev/badge/skills/openhcsdev/openhcs/use-openhcs/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/openhcsdev/openhcs/use-openhcs"><img src="https://agentmods.dev/badge/skills/openhcsdev/openhcs/use-openhcs.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 | $0.00055 | $0.02184 |
| Opus 5.5 | $0.00022 | $0.00874 |
| Sonnet 5.5 | $0.00011 | $0.00437 |
| Haiku 4.5 | $0.00006 | $0.00218 |
Grade A, and why
use-openhcs 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 11d 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 — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Use OpenHCS
- Call
openhcs_health_checkbefore relying on other tools. Report a bootstrap or stale-server failure instead of working around it. - Call
openhcs_get_authoring_contextwithkind="first_use"when choosing or resuming an OpenHCS workflow, then request exactly the task context it names. For multisite assembly, registration, normalisation, segmentation review, or image-result QC,kind="image_analysis_workflow"is the canonical operating guide; follow it rather than reconstructing those rules from this skill. - Treat Python pipeline source as one complete
PipelineDocument:pipeline_configplus orderedpipeline_steps. Never send a steps-only fragment, mirror configuration through a second argument, or strip source bindings from the reviewed document. UI-visible and headless routes use these same declarations even though their process and state owners differ. - Call
openhcs_search_capabilitieswith task-relevant workflow, target, or text filters. Its currentsurface_profile, registry-owned workflow metadata, side effects, and security metadata—not a remembered tool list—decide whether to use a UI-visible or exposed headless route. Useopenhcs_list_capabilitiesonly when the complete selected surface is required. Before the first cold local function-catalogue search, discovercatalog preparationcapabilities and follow the existing responsive preparation procedure (openhcs_custom_function_workflow) on the intended existing endpoint. Observe its exact handle until READY; keep cold warming separate from ordinary 10-second tool observations. A timeout is not permission to restart or replay. - Use validated OpenHCS/CellProfiler benchmark examples as working contracts, especially the first-class Official30 corpus. Search the biological task plus
OpenHCS Python, retrieve the exactopenhcs_official30_benchmark_recipessection withmax_chars=50000, and inspect its native reference, pipeline, input/channel assumptions, settings, and case-specific parity evidence. Benchmark parity is meaningful validation for that reference scope; it is not biological raw/overlay acceptance for a different assay. Record the example source and evidence tier before adapting it. CellProfiler image, object, measurement, relationship, and export semantics lower into the same OpenHCS declarations and runtime. For a compact, structured example of those fields, retrieveopenhcs_official30_examplehuman_nuclei_recipe_cardwhen the task involves ExampleHuman-style nuclei segmentation; its selected-value parity and unassessed new-assay QA are deliberately separate. When the task is unfamiliar or raw morphology conflicts with an example, follow pre-first-authorship learning retrieval (openhcs_analysis_learning) before choosing its method. Retrieve an applicable conditional lesson through existing knowledge search; benchmark defaults alone do not resolve contradictory raw evidence. Before authoring, retain the example-selection record described in the autonomous analysis strategy (openhcs_autonomous_analysis_strategy); reading this instruction is not evidence of retrieving a pipeline. Preserve failed checkpoints and continue authorised development; apply that guide's distinction between self-directed repair and external assistance, judging autonomous quality on the final frozen result rather than the first candidate. For an unfamiliar assay or an unresolved biological failure, use that guide's task-specific routes to image interpretation, preprocessing, segmentation diagnostics, measurements and recipe/error learning. Retrieve only the relevant guide; infer a provisional strategy from acquisition and matched raw channels before asking the user for missing information. These guides adapt Agentic-J's domain knowledge, not its Fiji command strings or model defaults. - Inspect real plate data and registered function declarations before authoring. If the microscope is unsupported, set
PipelineConfig.source_bindings_configwithLazySourceBindingsConfigfromopenhcs.core.configto filter files, extract metadata, name semantic sources, and project a virtual workspace; make each consumingFunctionStepselect those aliases through its step-local source bindings. Never parse filenames inside processing functions. Reflectglobalorpipelineconfiguration withopenhcs_describe_config_schemabefore setting non-obvious fields. Keep filesystem operations inside configured read and write roots. When importing an external metadata table, retrieveopenhcs_image_sources#joining-an-external-metadata-tablebefore declaring its join keys; plane-specific metadata and logical source-set metadata have different scopes. Before multi-channel photometry or consuming labels produced on another channel, retrieveopenhcs_image_sources#plan-image-stacks-and-source-bound-labels-before-authoring; image-stack assembly and typed label input context are separate declarations. - Validate and compile before execution. Do not infer that source code, UI state, or an earlier validation result implies a current compiled plan.
- Start read-only. Continue routine bounded work within existing task authorisation without asking again for each edit, execution or viewer action. Follow Task authorization in
openhcs_architecture_quick_startfor actions outside that authority; capability-registry metadata, path policy, declared confirmations and fresh revision/request tokens still apply. - Preserve the active ownership route. Discover the GUI bridge before UI tools and apply code/state changes with current tokens. Use headless tools only when their workflow group is exposed. Treat structured errors and recovery hints as authoritative; never bypass path policy, stale-process checks, compile requirements, or bridge authentication.
What ships with it
12 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.
- agents/openai.yaml 416 B
- references/analysis-learning.md 8.3 KB
- references/analysis-strategy.md 20 KB
- references/biological-image-analysis-evidence.md 4.9 KB
- references/blind-recipe-promotion.md 5.8 KB
- references/custom-function-authoring.md 15 KB
- references/image-interpretation.md 4.8 KB
- references/image-preprocessing.md 23 KB
- references/installation.md 5.0 KB
- references/measurement-interpretation.md 40 KB
- references/segmentation-diagnostics.md 27 KB
- references/viewer-qa.md 21 KB
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.
- 11d ago Changed · +3 lines ffb73f78ae93
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- 14d ago Changed · +1 lines 328600bf182d
- 15d ago Changed d8e48c44735e
- 17d ago Changed 3e40142fa7d4
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- 1mo ago Changed 644b1ce6de31
- 1mo ago First seen · 17 lines · 55 tokens per session scan A 0d6ee760ec69
use-openhcs is a skill published in the GitHub repository OpenHCSDev/openhcs (6 stars, last pushed 2d ago), licensed MIT. It adds 55 tokens to every session and 2,184 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 2026-08-31.
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