Borrowing it
Nothing to install: this file belongs to petrsovadina/CzechMedMCP. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/petrsovadina/CzechMedMCP/main/.claude/skills/speckit-tasks/SKILL.mdgit clone --depth 1 https://github.com/petrsovadina/CzechMedMCPWrote 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/petrsovadina/czechmedmcp/speckit-tasks)<a href="https://agentmods.dev/skills/petrsovadina/czechmedmcp/speckit-tasks"><img src="https://agentmods.dev/badge/skills/petrsovadina/czechmedmcp/speckit-tasks/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/petrsovadina/czechmedmcp/speckit-tasks"><img src="https://agentmods.dev/badge/skills/petrsovadina/czechmedmcp/speckit-tasks.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 | $0.00024 | $0.02144 |
| Opus 5 | $0.00012 | $0.01072 |
| Sonnet 5 | $0.00005 | $0.00429 |
| Haiku 4.5 | $0.00002 | $0.00214 |
Grade A, and why
speckit-tasks 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.
This is a copy
92% identical to speckit-tasks — 6 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 201 lines — stays where its author put it; the contents beside it link to each section on GitHub.
User Input
$ARGUMENTS
You MUST consider the user input before proceeding (if not empty).
Pre-Execution Checks
Check for extension hooks (before tasks generation):
- Check if
.specify/extensions.ymlexists in the project root. - If it exists, read it and look for entries under the
hooks.before_taskskey - If the YAML cannot be parsed or is invalid, skip hook checking silently and continue normally
- Filter out hooks where
enabledis explicitlyfalse. Treat hooks without anenabledfield as enabled by default. - For each remaining hook, do not attempt to interpret or evaluate hook
conditionexpressions:- If the hook has no
conditionfield, or it is null/empty, treat the hook as executable - If the hook defines a non-empty
condition, skip the hook and leave condition evaluation to the HookExecutor implementation
- If the hook has no
- For each executable hook, output the following based on its
optionalflag:- Optional hook (
optional: true):## Extension Hooks **Optional Pre-Hook**: {extension} Command: `/{command}` Description: {description} Prompt: {prompt} To execute: `/{command}` - Mandatory hook (
optional: false):## Extension Hooks **Automatic Pre-Hook**: {extension} Executing: `/{command}` EXECUTE_COMMAND: {command} Wait for the result of the hook command before proceeding to the Outline.
- Optional hook (
- If no hooks are registered or
.specify/extensions.ymldoes not exist, skip silently
Outline
-
Setup: Run
.specify/scripts/bash/check-prerequisites.sh --jsonfrom repo root and parse FEATURE_DIR and AVAILABLE_DOCS list. All paths must be absolute. For single quotes in args like "I'm Groot", use escape syntax: e.g 'I'''m Groot' (or double-quote if possible: "I'm Groot"). -
Load design documents: Read from FEATURE_DIR:
- Required: plan.md (tech stack, libraries, structure), spec.md (user stories with priorities)
- Optional: data-model.md (entities), contracts/ (interface contracts), research.md (decisions), quickstart.md (test scenarios)
- Note: Not all projects have all documents. Generate tasks based on what's available.
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 · 201 lines · 24 tokens per session scan A cb1a926eaf8a
speckit-tasks is a skill published in the GitHub repository petrsovadina/CzechMedMCP (1 stars, last pushed 5mo ago), licensed MIT. It adds 24 tokens to every session and 2,144 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to speckit-tasks, differing in 6 lines, and is treated as a copy.
Other skills, from other repositories
batch-processing-clinical-text
Run large-scale batch NER, PII extraction, or de-identification over many clinical notes on-device with OpenMed, with sharding, checkpointing, resumability, and append-only JSONL output. Use when the user needs to process a corpus or folder of notes, de-identify a dataset, run NER over thousands of documents, build a…
coding-hcc-risk-adjustment
Maps chronic conditions extracted by OpenMed to CMS-HCC V28 risk-adjustment categories and estimates a RAF (Risk Adjustment Factor) score as decision support. Use when the user wants to surface risk-adjustable diagnoses from notes, map ICD-10-CM codes to HCC categories, estimate or reconcile a patient/panel RAF, find…
coding-icd10
Suggests candidate ICD-10-CM diagnosis codes (and ICD-10-PCS procedure codes) for diagnoses and procedures extracted by OpenMed, with rationale and a human-coder caveat. Use when the user wants to code a problem list, map a diagnosis span to a billable ICD-10-CM code, route a finding to the right chapter, cross-walk…
defining-cohort-phenotypes
Authors computable phenotype and cohort definitions in the OHDSI ATLAS / CIRCE style over the OMOP CDM, combining standard concept sets with NLP-derived features that OpenMed extracts. Use when the user wants to define a patient cohort, write a computable phenotype, reuse PheKB or OHDSI Phenotype Library logic, build…
detecting-pv-signals
Computes disproportionality signals — PRR, ROR, EBGM, and IC (BCPNN) — over FAERS / OpenFDA drug-event data to flag potential safety signals. Use when the user wants to mine spontaneous-report data for drug-reaction associations, build a 2x2 contingency table, compute a Proportional Reporting Ratio or Reporting Odds…
etl-to-omop-cdm
Map OpenMed-extracted, terminology-coded conditions, drugs, and measurements into OMOP CDM v5.4 clinical tables (conditionoccurrence, drugexposure, measurement) for OHDSI/ATLAS analytics. Use when the user wants to load NLP-derived facts into an OMOP database, build an OHDSI ETL from clinical notes, populate…