Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add magnus919/agent-skills --skill product-shapinggit clone --depth 1 https://github.com/magnus919/agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/magnus919/agent-skills/product-shaping)<a href="https://agentmods.dev/skills/magnus919/agent-skills/product-shaping"><img src="https://agentmods.dev/badge/skills/magnus919/agent-skills/product-shaping/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/magnus919/agent-skills/product-shaping"><img src="https://agentmods.dev/badge/skills/magnus919/agent-skills/product-shaping.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00200 | $0.01677 |
| Opus 5 | $0.00100 | $0.00839 |
| Sonnet 5 | $0.00040 | $0.00335 |
| Haiku 4.5 | $0.00020 | $0.00168 |
Grade A, and why
product-shaping scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Shaping
Pre-commitment methodology for product and engineering work, adapted from Ryan Singer's Shape Up (free edition at basecamp.com/shapeup), extended for teams whose builders include AI agents.
The loop: shape a raw idea into a bounded pitch → bet a fixed appetite on it → build by discovering scopes and hammering scope to fit the box → move on, letting post-ship feedback re-enter as raw ideas.
The core moves
- Set boundaries — choose the appetite ("how much is this worth?") and narrow the problem to one specific story. Kill grab-bags ("redesign X", "X 2.0").
- Find the elements — sketch the solution rough, solved, and bounded: breadboards for flows, fat-marker fidelity for visual problems, components-and-contracts for non-UI work.
- Patch rabbit holes — attack your own sketch; settle hard decisions now, declare out-of-bounds cases, cut what the appetite can't afford.
- Write the pitch — problem, appetite, solution, rabbit holes, no-gos.
- Bet — commit the box uninterrupted, downside capped. No finish, no extension by default: the circuit breaker routes failure back to shaping.
- Build — one integrated slice first, then discovered scopes tracked as uphill→downhill states; sequence scariest-first; compare down to baseline when deciding to stop.
- Move on — scope cuts are not quality cuts; new feedback needs shaping, not instant yes.
Reference files
Load only what the current step needs:
| Reference | Load when |
|---|---|
| references/principles.md | You need the why: appetite vs estimate, fixed-time-variable-scope, rough/solved/bounded, evidence boundaries, lineage |
| references/shaping.md | Shaping steps 1–4 in detail, including shaping non-UI/backend/infrastructure work |
| references/betting.md | Bets vs backlogs, circuit breaker mechanics, cycles as optional scaffolding, handling defects between bets |
| references/building.md | Hand-over-responsibility, one-piece-done, scope mapping, hill-state tracking, deciding when to stop |
| references/hybrid-adaptation.md | Any bet involving AI-agent builders: budget currencies, batched steering, verification cost inside scope, kill criteria for non-converging loops |
| references/anti-patterns.md | Before betting anything that matters — documented field failures and their mitigations |
What ships with it
10 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 123 lines · 200 tokens per session scan A 086d41b5e193
product-shaping is a skill published in the GitHub repository magnus919/agent-skills (76 stars, last pushed yesterday), licensed MIT. It adds 200 tokens to every session and 1,677 once invoked, about $0.0010 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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