Fhir Ig Analysis

Measuring a FHIR Implementation Guide

Measures a FHIR IG objectively and reports what it counted. Strictly read-only: it never builds, changes, or publishes anything, and it does not forecast.

Preconditions

Discover the context; do not assume it, and never create it.

1. The tool. python3 must be runnable. The analyser is standard-library only — nothing to install. If python3 is absent, say so and stop; do not fetch an interpreter.

2. The subject. One or more IGs, each given as a local path, a git URL, or a package .tgz. run resolves each itself (shallow clone for a URL, download for a package). Nothing needs to be discovered inside the repository beyond what the tool reads.

   • A local path with neither sushi-config.yaml nor input/ is not an IG project. Report that and stop.
   • A git URL requires network access; if cloning fails, report the failure rather than reporting an empty measurement.

3. Static by default, and know what that costs. Without a build, the analysis is static: sushi-config/package metadata, FSH counts, narrative, directives, dependencies, linguistics, duplication, hygiene. Build-derived metrics (qa.json: errors, warnings, broken links, validation) stay null and are marked Build in the catalog. A package .tgz yields a further reduced analysis — generated resources only.

   State which mode produced the numbers whenever you report them. A null is not a zero.

Procedure

The generated reports are in German; this skill's instructions are in English. That is deliberate and does not follow from either fact on its own — the report prose was inherited and has not been translated. Do not "fix" it silently, and do not translate the numbers.

Resolve the script path first. The commands below name the tool relative to this skill's own directory, not to your working directory — which is the project you are operating on. Set SKILL_DIR to the directory containing this SKILL.md (you just read it, so you know where it is) and use it in every invocation:

SKILL_DIR=<the directory containing this SKILL.md>   # e.g. .claude/skills/fhir-ig-analysis

Running a bare scripts/... from the project root does not merely fail — if the project happens to have its own scripts/ directory with a same-named file, it silently runs that instead.

1. Measure. One IG or several, from the IG's root or anywhere:

   python3 "$SKILL_DIR/scripts/ig-stats.py" run <input…> [-o OUTDIR] [--label a,b]
   

   With two or more inputs this writes one report per IG plus compare-report.md automatically. --label sets the display label (report heading, compare columns), one per input; output filenames always derive from the IG id.

   Two consequences of how inputs resolve, so the numbers are read correctly: a git URL is shallow-cloned, so the git-history metrics (bus factor, maintenance cadence) are reported as not derivable (git.history_complete: false, values null) — measure a full local clone when you need them. And the tool reports counted maturity components without an aggregated score: a 0–100 "readiness" number would be a forecast, which this skill deliberately does not produce.

2. Power-user entry points, when you want the stages separately:

   python3 "$SKILL_DIR/scripts/ig-stats.py" analyze <ig-dir> [-o stats.json]
   python3 "$SKILL_DIR/scripts/ig-stats.py" report  <stats.json>  [-o report.md]
   python3 "$SKILL_DIR/scripts/ig-stats.py" compare <stats.json…> [-o compare.md]
   

3. Read the mandatory-page finding carefully. The page set the tool checks against lives in references/report-content.json under mandatory_pages and is hand-editable on purpose. It is seeded with the MII KDS module template's actual page names.

   If a measured IG legitimately uses a different page set, correct the list rather than reporting its pages as missing — and say in your report which list you used. A page-completeness metric is only as good as the set it compares against.

4. Report both artefacts. The JSON is the durable one: it has a fixed schema, so a series of runs diffs cleanly and shows how a module grows between releases. The Markdown is for people.

5. Compare only on normalised metrics. Absolute counts across IGs of different size say almost nothing; the comparison report aggregates a Σ total for scope but the fair columns are the normalised ones.

6. Same-module verification is automatic. When every compared input carries the same packageId, the inputs are states of ONE module (typically: the platform source vs its migrated copy) and the report switches to Same-Module-Verifikation: identity-field equality, published-artifact-set equality (internal FSH constructs reported separately — template adoption adds scaffold rulesets legitimately), canonical-URL equality (read from fsh-generated when both inputs have it), and narrative per language level (default / translations / retained platform pages) with word-coverage indicators. The Σ column is dropped (meaningless for one module). Every verdict is counted, none forecast. Order matters: the FIRST input is the reference.

Pre-flight aspects (migration Gate 0)

From v0.17.0 every measurement carries a preflight section with migration-decisive aspects, measured OFFLINE from the tree alone:

• Licence evidence, tiered — every place a licence is declared (sushi-config/package.json, LICENSE file, narrative copyright statements), with absence and contradiction as first-class findings.
• Canonical-space census — resources whose url lies outside the IG canonical, plus id↔url mismatches inside it: together the special_url_prediction, the publisher's special-url list a migration will need; example.org canonicals are called out separately. A sushi-config-vs-package.json canonical disagreement is reported as config_contradiction (measured live: PROs' package.json carries a modul-dok canonical).
• Dependency-pin health — old-style/virtual packages, whether THO/Extensions-Pack are pinned DIRECTLY (absent → the IG Publisher injects the latest release at build time), and external parent canonicals. The parser reads BOTH SUSHI dependency forms (flat and nested id/version sub-keys); dependency_block_unparsed: true flags a block that exists but yielded no entries — a parser finding, never a dependency-free source.
• Package layout — for distributed packages: .index.json presence (absent → index-based oracles run dry) and nested resource directories (flat globs lose them; the census here is recursive).
• Narrative sources — which trees carry prose (pagecontent, intro-notes, implementation-guides/); dual_source: true plus each tree's last commit date makes the authoritative-copy decision a measured one instead of a rank default.
• QA baseline — a rendered qa.json in the tree, if any. None means: obtain it (build the unmigrated source, or fetch its rendered qa) — never assume it.

Three census honesty rules sit behind these aspects: instances of canonical base types (ConceptMap, Measure, ObservationDefinition, …) are counted in an open artifacts.other bucket instead of being absorbed as "examples"; where FSH declarations AND generated resources both exist, their counts are cross-checked and every mismatch is reported (generated_crosscheck — the generated resourceType census is the authoritative one for page decisions); and a repository whose SUSHI project nests below the root (strukturdaten's Resources/) is re-rooted with a page-one warning instead of yielding an empty identity without a finding.

None values name what this instrument cannot measure from the tree — the consumer obtains it elsewhere, never assumes it (the "a null is not a zero" rule). The mii-ig-migration skill runs this measurement on the unmigrated source as its REQUIRED Gate-0 pre-flight.

Verification

python3 "$SKILL_DIR/scripts/ig-stats.py" run <ig-dir> -o /tmp/igstats
python3 -c "import json;d=json.load(open('/tmp/igstats/<name>-stats.json'));print(d['mode'],d['schemaVersion'])"

• The run exits 0 and writes one *-stats.json and one *-report.md per input.
• With ≥2 inputs, compare-report.md exists.
• mode in the JSON matches what you claim (static / reduced), and schemaVersion is present.
• Every metric you quote is either a counted number or null — never an estimate. If a field is null, report it as not derivable statically rather than as zero.
• The mandatory-page result is plausible against the IG you measured. All-missing usually means the wrong page set, not a broken IG.
• On a same-module comparison, the Befund block states IDENTISCH/DIVERGIERT per aspect — a migration is only faithful when identity, published artifact set and canonical URLs are all IDENTISCH; the migration skill's Definition of Done requires exactly that.

Scope and delimitation

Covers measurement and comparison: what is in an IG, how clean it is, how it compares, how it changes over time.

Deliberately not covered:

• Effort forecasting. The tool reports what it counted. It does not estimate person-days, cost or a readiness score, and an earlier version of this skill that framed the numbers as migration scoping was narrowed on purpose. Do not reintroduce it: a measurement dressed as a forecast is the least trustworthy thing this skill could produce.
• Migration onto the MII KDS module template — see mii-ig-migration. Verifying a migrated guide against its source belongs there too, in its scripts/verify-migration.py: artefact reachability, menu structure, content placement, rendered header metadata and toolchain provenance are pass/fail gates that read the migration's page map and run log — inputs that are not properties of an IG and would break this skill's "a path, a URL or a .tgz" contract. The same-module verification below is the measurement that gate consumes, not a substitute for it: it proves an artefact is PRESENT, never that anything links to it.
• Translation — see fhir-ig-translation.
• Convention and naming checks against the MII meta wiki or the metadata contract. Those are a different check, and the module template ships its own skill for them.
• Building or publishing anything. Read-only is a guardrail, not a default.

If a skill of this name is provided both by this catalog and locally, the local one wins.

Guardrails

• Read-only. Never modify the analysed IG; never force a build.
• Measurement, not forecasting. See above.
• Fair comparison only via normalised metrics.
• No invention. A missing input yields null, never a guess. Heuristic metrics are marked as heuristics in the catalog and must be reported as such.

References

• references/metrics-catalog.md — the parameter catalog (groups A–N), each metric with its source and its use. Hand-extensible; this is the SSOT for what is measured.
• references/ig-stats-schema.json — the schema of ig-stats.json.
• references/report-content.json — plain-language texts, glossary, directive patterns, metric explanations, and the mandatory_pages list. Hand-editable.
• references/triggers.md — the Gate 3 prompt set.
• scripts/ig-stats.py — the analyser (run / analyze / report / compare).

Provenance

Derived from skills/ig-analyze in medizininformatik-initiative/mii-kds-module-template at commit b5beedb17a66a4397d597429668c7b6d54202c62, which in turn adapted the ig-analyze skill of forschungsgruppe-digital-health/mii-kds-sample-ig-inoffiziell (CC-BY-4.0). Both steps of that lineage are recorded deliberately.

Reworked on 2026-07-31 for this catalog. Beyond the catalog contract, three substantive changes:

• The analyser now ships with the skill. It previously lived in the source repository's scripts/ and was referenced by parent traversal, so the skill pointed at a file it could not reach once installed elsewhere.
• scripts/ig-stats.py locates references/report-content.json relative to its own path. It previously built that path from a computed repository root plus a hard-coded skills/ig-analyze/references/…, which broke twice here: there is no repository root to compute, and the skill was renamed.
• The mandatory-page list moved out of the code into references/report-content.json and was corrected. The inherited list named eleven pages of which six do not exist in the template modules are built from (use-cases, data-sets, uml, context, references, security-privacy), so every measurement reported six false missing pages.

Revised on 2026-08-01 after the skill's first real-task exercise (a dry run against kerndatensatz-dokument + kerndatensatz-basis). Four output defects were fixed, all in the direction of "measurement, not forecasting": the aggregated Reifegrad/"Freigabe-Indikator" score was removed (components are now reported unaggregated — schemaVersion 1.4); the "Empfehlungen" table's migration-scoping prose became neutral "Befunde & Einordnung" measurement explanations and its metric-less "Arbeitsweise" row was dropped; git-history metrics on shallow clones (including every clone the tool makes itself) now report history_complete: false and null instead of a fictive "100 % top author"; and references/ig-stats-schema.json was regenerated from the real output after drifting (mandatory_missing was declared but never emitted). The dry-run findings live in the mii-kds-dokument-ig-inoffiziell sandbox under docs/reports/dry-run-2026-07-31/.

Original licence: CC-BY-4.0, as declared by both source repositories. scripts/ is Apache-2.0, matching this repository's code licence.

Promoted to stable on 2026-08-05: repeated real-task use: module measurement/comparison across four KDS modules, and the same-module verification gate of two completed migrations. The trigger set in references/triggers.md was exercised by those runs.