Three native skills that run the Diolog Tasks issue pipeline — triage, plan, and worker — entirely inside your interactive Claude Code session (no Agent SDK, so usage draws from your standard interactive allowance, not the separate Agent SDK credit). Formerly linear-issue-pipeline; now targets the diolog-tasks MCP (mcp.diolog.com.au). tasks-triage runs a technical + Specification Sentinel product review and posts a non-technical readiness comment; tasks-plan classifies a tier, investigates the codebase, writes docs/plans/{id}.md and comments the repo-relative path (no file upload); tasks-worker implements a planned issue in an isolated worktree via dynamic ultracode workflows (understand → implement → rebase → acceptance review → resolve), with no remote PR. The plan and worker fan out parallel subagents via the Workflow tool to speed up large investigations. Recalibrated for current-model behaviour (Anthropic's Claude Opus 5 prompting guidance), ported from the generalized twin: tasks-worker's per-phase gate is explicitly a conformance check (evidence-shaped — the clause, the file:line, the gate you ran) rather than a second correctness review stacked on a phase that already reviewed itself; adversarial finding-verification is aimed at Criticals, structural fixes, and locked-decision reversals rather than blanket 1:1, and no reviewer is briefed to be conservative; the completion comment keeps to tables/counts/drops/gates rather than narrating the phases; and tasks-triage/tasks-plan gain the effort dial plus length budgets. All three point at the twin's new pipeline-wide canonical feature-spec-pipeline/skills/work/references/model-and-effort.md. v2.4.0 (from the WEB-4905 110-ticket audit): a fourth skill, tasks-verify — an independent fresh-session acceptance verifier that re-derives requirements from the ticket and closes them on typed behavioural evidence (browser measurements, exercised requests, stored-row counts) before a ticket is treated as done; evidence-typ
4 skills
Four native skills that run an issue-tracker-free, markdown-doc feature pipeline — /triage, /plan, /work, /gap-fix — entirely inside your interactive Claude Code session. /triage turns a feature idea (inline text or a notes file) into a versioned spec at docs/specs/spec-DIO-0001.md (id allocated from docs/feature-specs/LEDGER.md), running a codebase grounding pass plus a Specification Sentinel product/UX/compliance review and appending a non-technical readiness section. /plan classifies a tier, investigates the codebase, writes docs/plans/plan-DIO-0001.md and links it from the spec. /work implements the plan in an isolated worktree via dynamic ultracode workflows (understand → implement → rebase → acceptance review → resolve), appends a progress section to the spec, and leaves the branch local — no remote PR. /gap-fix runs after /work as the remediation step: it re-enters the work branch, ALWAYS re-audits the delivered code against the original spec (merging in any human/QA gaps you provide), and implements the fixes in code (file-disjoint fan-out + typecheck gates), looping audit→fix until only optional Low items remain, then commits locally and appends a gap-fix progress note — no remote PR. The plan, work, and gap-fix skills fan out parallel subagents via the Workflow tool (in small batches to avoid throttling). A doc-driven sibling of diolog-tasks-pipeline that needs no Diolog Tasks MCP. Recalibrated for current-model behaviour (Anthropic's Claude Opus 5 prompting guidance) with a new pipeline-wide canonical reference, skills/work/references/model-and-effort.md: the model × effort lane calibration (per-lane effort levels, `low` as the level built for subagents, effort-before-model because REVIEWER ≥ WRITER is about capability class not spend, `xhigh` + 64k `max_tokens` for long-horizon runners, hold-effort-constant for the prompt cache, never hardcode a dated model id in a wire self-check); the oracle-checks-vs-re-reading-your-own-work distinction that says whic
4 skills
Quality-assurance automation for the diolog-swe-bench benchmark corpus: judge whether a task is difficult enough and fairly verified (bench-task-judge), re-run the whole-corpus review with any model (bench-corpus-review), and author new gated tasks including ui design tasks (bench-task-author).
3 skills
Three skills that write publish-ready, on-brand content in Diolog's brand voice (Australian investor-communication SaaS; 'the workspace for everything investor-facing'; tagline 'Disclosure, without doubt.'), all driven by one bundled brand-voice persona and gated by one deterministic voice-lint. create-diolog-article writes long-form articles / blog posts / thought-leadership across the investor-education, IR-best-practice, thought-leadership and announcement registers. create-diolog-marketing-copy writes website marketing copy and marketing emails (benefit-first heroes, feature-to-'so what' bullets, problem-callback CTAs, subject/preview/body emails). create-diolog-business-case writes buyer-facing ROI / justification documents for the CFO / Company Secretary / board (status-quo cost -> what changes -> proof/security/compliance posture -> measurable outcomes -> next step). Every skill hard-encodes the house rules: no em or en dashes (spaced hyphen only), Australian English, sentence-case headings (except product feature names), every statistic carries an inline (Source, Year) with zero fabrication, compliance confidence capped at 95%, no financial advice or guaranteed outcomes, the Diolog narrative arc, correct register selection, and critique the old way of IR never the reader. The deterministic diolog_voice_lint.py hard-fails on em/en dashes and banned/AI-tell phrases and advises on American spellings, over-claim/guarantee language and citation coverage. The fuller brand counterpart to the deployed diolog-content-writer / diolog-linkedin-writer generation prompts; personal-voice LinkedIn posts remain with create-luke-content.
3 skills
Three macOS-native UI skills sharing one bundled knowledge corpus (the full macOS 26 'Tahoe'/Liquid Glass HIG library + a token-level DESIGN.md + the macOS 26 UI Extraction & Native Translation framework). `macosify` refits an existing UI to feel authentically AppKit-native on macOS 26, correcting iOS-derived looks (Mac Catalyst / 'Designed for iPad' on Apple Silicon) toward true native. `macos-ui-analyst` (the GlassReader persona) analyses a static macOS 26 screenshot into a structured Native Translation Report (AX-tree inventory + SwiftUI/AppKit mapping) and merges durable learnings into one accumulating knowledge file. `macos-storyboard` reads the entire corpus, then builds an interactive StyleX + Storybook storyboard of a specification's user flows — a reusable design system (tokens → elements → composites) with every screen rendered across happy / empty / loading / error / AI states, using the design-craft and frontend-design skills for bespoke, accessible direction.
3 skills
Convert PowerPoint (.pptx) ⇄ the lecturn.deck/1 JSON slide schema, and author/validate that JSON, with the bundled spec and a stdlib-only Python two-way converter. Import a .pptx to an editable deck/slide/element tree (media extracted, fidelity F0–F3 reported, no silent loss), or build a structurally valid .pptx from a deck JSON. Includes a specialist agent for multi-step convert-validate-verify loops.
1 skill · 1 agent