findings-to-tickets

Estrelas4

Forks0

Atualizado18 de julho de 2026 às 15:25

Convert lean-findings.json into a ready-to-import sprint backlog — GitHub Issues, Jira, or Linear tickets with severity labels, effort estimates, acceptance criteria, and pattern links. Use this skill whenever the user wants audit findings as tickets/issues/backlog items, asks to "make this actionable" or "put these in Jira/GitHub", or wants remediation work assigned to a sprint. Part of Lean Agentic AI Skills; a transformer from findings to work items.

Instalação

Instalar com Codex ou Claude Copie este prompt, cole no Codex, Claude ou outro assistente e deixe que ele revise a página da skill e instale para você.

Fonte

navveenb

navveenb/lean-agentic-ai-skills

Abrir repositório GitHub Ver repositórios do creator

Download

Ocupações relacionadasSOC

Baseado na classificação ocupacional SOC

Desenvolvedores de softwareInformática e Matemática·SOC 15-1252

Explorador de arquivos

2 arquivos

SKILL.md

readonly

name

findings-to-tickets

description

Findings to Tickets

Transformer skill — the actionability bridge. Input: one or more findings files, plus the target tracker (GitHub/Jira/Linear) and any team conventions (labels, components, estimate scale). Output: importable ticket definitions (JSON/CSV for the tracker's import, or gh/jira CLI commands, or direct creation when a tracker connector is available and the user confirms).

An audit that ends as a PDF decays; an audit that ends as a backlog gets burned down. The mapping is mechanical because the contract already carries everything a good ticket needs.

Ticket mapping

Per finding:

Title — imperative rewrite of the finding title ("Serve hero images in modern formats" not "Hero images served as PNG").
Body — issue + evidence verbatim (the evidence is what convinces the assignee it's real), the fix as the proposed approach, the pattern URL as reference, and a one-line "why it matters" from the impact field — directional language preserved, never embellished into savings claims.
Acceptance criteria — derived from the fix, checkable ("images in the first viewport served as AVIF/WebP with srcset; verified in a network trace"). Where the finding pairs with a measurement, the criterion says measured-before/after, not "feels faster".
Labels — sustainability, severity (sev:high), SCI term (sci:E), producer skill (audit:web-carbon-audit).
Estimate — from the effort field mapped to the team's scale (low→S/1pt etc.), flagged as a starting point for the team's own sizing.
Grouping — quick wins (effort low, severity ≥ medium) batched into one clearly-labeled early milestone; structural items (effort high) as epics with the related findings as children when several share a cause.

Rules

Fidelity over polish: tickets never claim more than the finding does; the methodology caveats travel in the epic/milestone description so context survives the export.
passed items are not tickets. out_of_scope items become at most one "investigate" ticket each, clearly typed as investigation, and only when the user opts in.
Dedup on re-export: stable finding id goes in the ticket body as a marker (lean-id: hero-images-legacy-format) so re-running the export after a re-audit updates rather than duplicates — and closes the loop with findings-diff (resolved findings → suggested ticket closures, listed for human confirmation, never auto-closed).
Direct tracker writes only with explicit confirmation and a dry-run list shown first.

Cost signal rendering

Include the cost signal. Where a finding carries cost_signal, include one line in the ticket body: "Cost driver: {driver} — currently {observed}. Direction: {direction}." This gives assignees the honest business case without inventing figures.

Not this skill's job

Producing findings (producers), deciding priorities beyond the severity/effort data (the team's call), gating merges (lean-regression-gate).

Mais deste repositório

mesmo repositório

agentic-loop-auditor

navveenb/lean-agentic-ai-skills

Audit agent frameworks and agentic workflows for token-burning loop pathologies — unbounded retries, excessive planning depth, tool-call thrashing, context snowballing, missing loop budgets and kill switches. Use this skill whenever the user shares agent configs or traces (LangChain/LangGraph, CrewAI, AutoGen, custom loops), complains an agent is slow or expensive, or is designing any autonomous/multi-step agent. Part of Lean Agentic AI Skills; emits lean-findings.json.

2026-07-184

ai-eval-efficiency-audit

navveenb/lean-agentic-ai-skills

Audit AI evaluation infrastructure for wasted compute — full benchmark suites re-run on unchanged cases, LLM-as-judge grading without caching, oversized judge models, redundant eval passes per commit, and missing result reuse. Use this skill whenever the user shares eval harness configs or CI eval steps, complains that evals are slow or expensive, mentions LLM-as-judge costs, or runs benchmark suites on every change. Part of Lean Agentic AI Skills; emits lean-findings.json.

2026-07-184

api-efficiency-audit

navveenb/lean-agentic-ai-skills

Audit API design for chattiness and over-fetching — REST endpoints requiring N calls per screen, GraphQL resolvers with N+1 fan-out, default-everything payloads, missing pagination and field selection, provider-side polling where webhooks serve, and uncompressed responses. Use this skill whenever the user shares API schemas (OpenAPI, GraphQL SDL), resolver code, endpoint designs, or asks why clients make too many calls or payloads are heavy. Part of Lean Agentic AI Skills; emits lean-findings.json.

2026-07-184

caching-strategy-designer

navveenb/lean-agentic-ai-skills

Design caching layers so work is done once, not per request — HTTP/CDN caching, application-level caches (Redis/Memcached), computed-result caching, and cache-invalidation strategy. Use this skill whenever the user wants to add caching, asks why the same work is recomputed, designs a read-heavy system, or an audit skill (db-efficiency-audit, web-carbon-audit) flags uncached repeated work. Part of Lean Agentic AI Skills; emits a cache design plus lean-findings.json.

2026-07-184

carbon-aware-scheduler

navveenb/lean-agentic-ai-skills

Design time-shifting of flexible workloads (batch jobs, CI, training, backups, crons) to hours and regions when grid electricity is cleanest, using carbon-intensity signals like Electricity Maps, WattTime, or the Carbon Aware SDK. Use this skill whenever the user mentions carbon-aware computing, scheduling flexible/deferrable jobs, demand shifting, running workloads when energy is clean, or asks how to reduce the carbon intensity (not just amount) of their compute. Part of Lean Agentic AI Skills; emits schedule designs plus lean-findings.json.

2026-07-184

carbon-budget-designer

navveenb/lean-agentic-ai-skills

Design per-feature and per-service efficiency budgets — page-weight budgets, token budgets per AI feature, query budgets per endpoint, GPU-hour budgets per training project — with owners, review checkpoints, and enforcement hooks. Use this skill whenever the user wants sustainability targets for teams, asks how to keep efficiency from regressing organizationally, mentions performance-budget-style governance for carbon/energy, or is setting engineering standards. Part of Lean Agentic AI Skills; an advisor emitting budget definitions plus lean-findings.json.

2026-07-184

name

findings-to-tickets

description

Findings to Tickets

An audit that ends as a PDF decays; an audit that ends as a backlog gets burned down. The mapping is mechanical because the contract already carries everything a good ticket needs.

Ticket mapping

Per finding:

Title — imperative rewrite of the finding title ("Serve hero images in modern formats" not "Hero images served as PNG").
Body — issue + evidence verbatim (the evidence is what convinces the assignee it's real), the fix as the proposed approach, the pattern URL as reference, and a one-line "why it matters" from the impact field — directional language preserved, never embellished into savings claims.
Acceptance criteria — derived from the fix, checkable ("images in the first viewport served as AVIF/WebP with srcset; verified in a network trace"). Where the finding pairs with a measurement, the criterion says measured-before/after, not "feels faster".
Labels — sustainability, severity (sev:high), SCI term (sci:E), producer skill (audit:web-carbon-audit).
Estimate — from the effort field mapped to the team's scale (low→S/1pt etc.), flagged as a starting point for the team's own sizing.
Grouping — quick wins (effort low, severity ≥ medium) batched into one clearly-labeled early milestone; structural items (effort high) as epics with the related findings as children when several share a cause.

Rules

Fidelity over polish: tickets never claim more than the finding does; the methodology caveats travel in the epic/milestone description so context survives the export.
passed items are not tickets. out_of_scope items become at most one "investigate" ticket each, clearly typed as investigation, and only when the user opts in.
Dedup on re-export: stable finding id goes in the ticket body as a marker (lean-id: hero-images-legacy-format) so re-running the export after a re-audit updates rather than duplicates — and closes the loop with findings-diff (resolved findings → suggested ticket closures, listed for human confirmation, never auto-closed).
Direct tracker writes only with explicit confirmation and a dry-run list shown first.

Cost signal rendering

Not this skill's job

Producing findings (producers), deciding priorities beyond the severity/effort data (the team's call), gating merges (lean-regression-gate).