AI skills for program / project / delivery managers

Hacker News

A Claude Code and OpenCode skill library that covers the full project and engagement lifecycle through 10 stage-aligned agents and 62 workflows: * Shaping: MEDDIC qualification, ROM estimate, SoW draft * Mobilisation: Charter, RACI, governance, kickoff * Planning: Plan, capacity, budget baseline, estimate challenge * Execution: Weekly status, RAID, standup digests, blocker triage * Governance: Steering packs, exec summaries, stakeholder updates, escalation memos * Risk and change: Risk deep-dives, change requests, mitigation, escalations * Technical-quality oversight: SDLC, QA, systems engineering, secure-SDLC health checks * Commercial: Budget tracking, margin analysis, change orders, invoice backup * People: Team health, attrition risk, ramp plans, one-to-one prep * Closure: Checklist, retrospective, lessons, case study, handover Outputs are audit-ready markdown, each linked back to its charter revision, source documents, model, and prompt hash. The engagement charter is a 'living constitution' that every artifact reconciles to. Anything leaving your machine (steering decks, exec memos, change orders, SoWs) passes through an adversarial red-team gate first. Local-first, and engagement data never leaves your disk. npx @systima/project-delivery-framework https://github.com/systima-ai/project-delivery-framework We built this because the "AI for project management" space is almost entirely tooling for product management. The framework is for the person running the multi-month, multi-stakeholder engagement (budget, the client, the team, and the contract, etc).

Visit product View on Hacker NewsMay 25, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, agents, agent · Missing: macos, cursor, apple

68%68% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

53%53% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

27%27% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

24%24% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: margin · Missing: arr, mrr, revenue

20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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