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Read the OKR card by id from the mesh. Returns the BTABoK 9-section structure as JSON.
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Basé sur la classification professionnelle SOC
| name | knowledge-okr |
| description | Read the OKR card by id from the mesh. Returns the BTABoK 9-section structure as JSON. |
| version | 0.1.0 |
| purity | pure-data |
| runtime | research-runner |
| command | npx @maintainabilityai/research-runner@~0.1.64 skill-knowledge-okr |
The agent's canonical input on every run. Per §5.5.1, every .agent.md MUST extract the okr_id from the issue body's HTML comment marker and call this skill first.
| Field | Type | Required | Default | Description |
|---|---|---|---|---|
okrId | string | yes | — | e.g. OKR-2026Q1-IMDB-001-celeb-api. |
| Field | Type | Description |
|---|---|---|
ok | boolean | False if the OKR is missing or fails Zod validation. |
card | OkrCard | null | The full BTABoK card (see vscode-extension/src/types/okr.ts). |
reason? | string | On ok: false, why. |
echo '{"okrId":"OKR-2026Q1-IMDB-001-celeb-api"}' \
| npx @maintainabilityai/research-runner@~0.1.64 skill-knowledge-okr
Wraps OKRService.readRaw(meshPath, okrId) via a mesh-path env var (MESH_PATH). Read-only, idempotent, cache-friendly. CLI subcommand backend lands in B-PR1a.
{ok: false, reason: 'okr-not-found'} on missing dir; {ok: false, reason: 'schema-validation-failed: ...'} on invalid YAML. Agent stops + posts a PR comment per §5.5.3.
Append one hash-chained audit event to okrs/<id>/audit/events/<run-id>.jsonl. Returns the new chain head.
Replay the hash chain over an existing audit JSONL; returns the chain head on success, the first integrity failure reason on failure.
Query the mesh's threat library for entries matching a concern (e.g. "identity", "data-licensing").
Academic paper search via arXiv. Returns ranked paper results per query envelope.
Aggregated architecture context for a scope — CALM nodes + ADRs + fitness functions + quality attributes. PURE data; the Architect persona is in the parent agent's prompt.
Aggregated quality context for a scope — quality attribute definitions + performance SLOs + reliability targets per BAR.