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LISTENING phase via the `agf` CLI — capture feedback, persist learning, seed the next cycle. Use post-deploy or for sprint retrospective.
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Basierend auf der SOC-Berufsklassifikation
Accessibility compliance audit using WCAG 2.2 AA standards, ARIA validation, screen reader testing, keyboard navigation, color contrast analysis, and i18n readiness
Execute the ANALYZE phase of the lifecycle via the `agf` CLI — PRD creation, requirements, Definition of Ready (7 checks), cross-project learning
API governance and design audit using OpenAPI/Swagger spec generation, REST maturity model, contract validation, and breaking change detection
Architecture governance using C4 Model, ADR lifecycle, Architecture Fitness Functions, layer boundary enforcement, and drift detection
Human-in-the-loop PLANNING skill — investigates the project (graph + git + harness/gaps) and runs the whole ANALYZE→DESIGN→PLAN chain in one faceted loop to produce a COMPLETE PRD injected as graph backlog (epics, tasks, testable AC) for a separate agent to implement. Applies the project's planning methodologies — Impact Mapping + OKR per epic, JTBD, MoSCoW, WSJF/Cost-of-Delay, User Story Mapping, Example Mapping (Rules/Examples → Given-When-Then AC), SPIDR splitting, INVEST, Definition of Ready, Risk Matrix; the full catalogue lives in the skill body. Stops for the human after each complete PRD and iterates the next cycle from the project's own findings (dogfood). Does NOT implement. Triggers — graph-backlog-generation, gerar backlog, criar PRD, planejar feature, detalhar épico, novo ciclo, "plan the next thing", "what should we build next".
Automated bug discovery through static analysis, LSP diagnostics, pattern detection, regression hotspot analysis, and error catalog mining
| name | graph-listening |
| description | LISTENING phase via the `agf` CLI — capture feedback, persist learning, seed the next cycle. Use post-deploy or for sprint retrospective. |
| triggers | ["graph-listening"] |
| version | 2.0.0 |
| author | auto-generated |
| date | "2026-06-16T00:00:00.000Z" |
| category | LISTENING |
| phase | LISTENING |
| tokens | ~621 |
| phases | ["DEPLOY","ANALYZE"] |
LISTENING phase: feedback, persisted learning, next-cycle seed. Drive everything via the agf CLI — zero MCP. Load context with agf context <id> before changing anything.
agf learning stats → agf insights → agf node add --type feedback → agf import-prd <new>
LISTENING-phase agf commands:
| Command | What it does |
|---|---|
agf learning stats | Per-agent performance + learned routing |
agf node add --type feedback | Capture feedback as a traceable node |
agf insights | Backlog health (aging, distribution) |
agf import-prd <new> | Open the next cycle from feedback |
agf learning stats (per-agent perf, learned routing)agf insights (aging, distribution, health grade)agf node add --type feedback (bugs, improvements, learnings)agf forecast (compare pre- vs post-deploy baseline)agf search + flag stale entries for pruneagf import-prd <new> (or agf node add for a new epic)Close the loop before the next turn: agf savings / agf metrics --economy-report → agf learning → calibrate. This closes LISTENING and re-opens ANALYZE.
Phase: LISTENING → ANALYZE (next cycle)
Feedback: N nodes captured
Learning: per-agent performance, routing insights
Backlog: health grade X, aging Y days
DORA: delta from baseline (pre vs post-deploy)
Knowledge: M stale entries flagged for prune
Next Cycle: seeded with new epic/requirement
Status: Listening complete
LISTENING → ANALYZE: agf import-prd <new> then $graph-analyze opens the next cycle.
agf skill show graph-deployagf skill show graph-analyzeAGENTS.md and use apply_patch for manual edits.Economia de tokens. Os levers compartilhados por todas as skills —
--select,agf retrieve-command,agf exec chain, reuso antes de criação — vivem em_shared.md→ Token Economy. Fonte única: um parágrafo repetido em trinta arquivos é o trigésimo primeiro que envelhece sozinho.