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-lifecycle-learninggit 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-lifecycle-learning)<a href="https://agentmods.dev/skills/magnus919/agent-skills/product-lifecycle-learning"><img src="https://agentmods.dev/badge/skills/magnus919/agent-skills/product-lifecycle-learning/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-lifecycle-learning"><img src="https://agentmods.dev/badge/skills/magnus919/agent-skills/product-lifecycle-learning.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 | $0.00142 | $0.03755 |
| Opus 5 | $0.00071 | $0.01878 |
| Sonnet 5 | $0.00028 | $0.00751 |
| Haiku 4.5 | $0.00014 | $0.00376 |
Grade A, and why
product-lifecycle-learning 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 — 203 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Lifecycle Learning
Close the loop from launch to learning. This skill compares what was intended against what actually happened, maintains an evidence-backed assumption ledger, assesses feature health, and makes disciplined continue/improve/harvest/pivot/pause/retire decisions — including full retirement lifecycles. It produces a durable retained learning record that feeds back into roadmap, analytics, adoption, experimentation, and future specifications.
Loading Guide
Load only the reference or template relevant to the task. Do not load every file at once.
| File | Load when |
|---|---|
| references/discovery-brief.md | You need to understand how lifecycle-learning concepts map across skills and where this skill's boundaries are |
| references/epistemic-discipline.md | You need the full taxonomy for classifying claims as expected, observed, uncertain, or inferred |
| references/retirement-lifecycle.md | Planning a feature or product retirement, including deprecation, migration, customer treatment, and internal cleanup |
| references/feedback-destinations.md | Routing learning outputs to the right downstream skill — roadmap, analytics, adoption, experimentation, or specification |
| templates/outcome-review.md | Conducting a structured post-launch outcome review comparing expected vs. observed |
| templates/assumption-ledger-update.md | Updating the assumption ledger with new evidence and confidence shifts |
| templates/feature-health-record.md | Assessing feature health across multiple dimensions and surfacing signals |
| templates/retirement-decision.md | Making and recording a justified retirement or continuation decision |
| templates/sunset-plan.md | Planning deprecation communication, migration paths, customer treatment, and internal cleanup |
| templates/retained-learning-record.md | Capturing durable reusable learning that survives beyond the feature |
What ships with it
12 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.
- evals/evals.json 18 KB
- README.md 4.5 KB
- references/discovery-brief.md 4.7 KB
- references/epistemic-discipline.md 5.8 KB
- references/feedback-destinations.md 5.6 KB
- references/retirement-lifecycle.md 6.2 KB
- templates/assumption-ledger-update.md 1.8 KB
- templates/feature-health-record.md 2.4 KB
- templates/outcome-review.md 2.0 KB
- templates/retained-learning-record.md 2.3 KB
- templates/retirement-decision.md 2.3 KB
- templates/sunset-plan.md 2.8 KB
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 · 203 lines · 142 tokens per session scan A 94f4773456af
product-lifecycle-learning is a skill published in the GitHub repository magnus919/agent-skills (76 stars, last pushed yesterday), licensed MIT. It adds 142 tokens to every session and 3,755 once invoked, about $0.0007 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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