Post Mortem
thananon/9arm-skills
Write the canonical engineering record of a fixed bug — root cause, mechanism, fix, validation, and how it slipped through.
Compare intended product outcomes against observed results to close the launch-to-learning loop: collect post-launch evidence, distinguish expected from observed from uncertain from inferred claims…
Install Product Lifecycle Learning
$ npx skills add magnus919/agent-skills --skill product-lifecycle-learning -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install magnus919/agent-skills product-lifecycle-learning --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/magnus919/agent-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/product-lifecycle-learning .claude/skills/product-lifecycle-learning && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "product-lifecycle-learning" agent skill from https://github.com/magnus919/agent-skills/tree/main/product-lifecycle-learning into .claude/skills/product-lifecycle-learning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "product-lifecycle-learning", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/magnus919/agent-skills/tree/main/product-lifecycle-learningType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add magnus919/agent-skills --skill product-lifecycle-learning -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install magnus919/agent-skills product-lifecycle-learning --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/magnus919/agent-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/product-lifecycle-learning .agents/skills/product-lifecycle-learning && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "product-lifecycle-learning" agent skill from https://github.com/magnus919/agent-skills/tree/main/product-lifecycle-learning into .agents/skills/product-lifecycle-learning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "product-lifecycle-learning", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add magnus919/agent-skills --skill product-lifecycle-learning -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install magnus919/agent-skills product-lifecycle-learning --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/magnus919/agent-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/product-lifecycle-learning .cursor/skills/product-lifecycle-learning && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "product-lifecycle-learning" agent skill from https://github.com/magnus919/agent-skills/tree/main/product-lifecycle-learning into .cursor/skills/product-lifecycle-learning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "product-lifecycle-learning", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/magnus919/agent-skills.git --path product-lifecycle-learning--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add magnus919/agent-skills --skill product-lifecycle-learning -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install magnus919/agent-skills product-lifecycle-learning --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/magnus919/agent-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/product-lifecycle-learning .gemini/skills/product-lifecycle-learning && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "product-lifecycle-learning" agent skill from https://github.com/magnus919/agent-skills/tree/main/product-lifecycle-learning into .gemini/skills/product-lifecycle-learning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "product-lifecycle-learning", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install magnus919/agent-skills product-lifecycle-learningInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add magnus919/agent-skills --skill product-lifecycle-learning -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/magnus919/agent-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/product-lifecycle-learning .github/skills/product-lifecycle-learning && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "product-lifecycle-learning" agent skill from https://github.com/magnus919/agent-skills/tree/main/product-lifecycle-learning into .github/skills/product-lifecycle-learning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "product-lifecycle-learning", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add magnus919/agent-skills --skill product-lifecycle-learning -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install magnus919/agent-skills product-lifecycle-learning --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/magnus919/agent-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/product-lifecycle-learning .opencode/skills/product-lifecycle-learning && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "product-lifecycle-learning" agent skill from https://github.com/magnus919/agent-skills/tree/main/product-lifecycle-learning into .opencode/skills/product-lifecycle-learning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "product-lifecycle-learning", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
product-lifecycle-learning- GitHub stars
- 115
- Token cost
- ~4.7k tokens
- SKILL.md length
- 1,526 words
- Files
- 13 (incl. references)
- Skills in repo
- 131
- Repo updated
- First seen
- Licence
- MIT
At a glance
Compare intended product outcomes against observed results to close the launch-to-learning loop: collect post-launch evidence, distinguish expected from observed from uncertain from inferred claims…
- Incident postmortems
- SKILL.md covers Loading Guide, Core Methodology, When Not to Use and Routing and Feedback, plus 2 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Root-cause analysis (routes to incident-learning
What it does
Product Lifecycle Learning is an agent skill from magnus919/agent-skills. Compare intended product outcomes against observed results to close the launch-to-learning loop: collect post-launch evidence, distinguish expected from observed from uncertain from inferred claims, update assumptions, assess feature health, and choose among continue/improve/harvest/pivot/pause/retire — including retirement lifecycles with deprecation, migration, customer treatment, and retained reusable learning. Do not use for incident postmortems or root-cause analysis (routes to incident-learning or…
Its SKILL.md is about 4.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 15 other files, including reference files (for example `README.md`, `evals/evals.json` and `references/discovery-brief.md`).
It sits in DevOps & Cloud, covering Root cause analysis, Site reliability engineering and Product analytics. The repository describes itself as: Curated collection of AI agent skills for Hermes and other agent frameworks. The licence is MIT.
When your agent uses it
- Incident postmortems
- Root-cause analysis (routes to incident-learning
- Site-reliability-engineering)
- Do not use for analytics instrumentation
Example prompts
- “/product-lifecycle-learning”
What it can do on your machine
Read from SKILL.md and the folder at commit 22b4723. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Product Lifecycle Learning loads about 4.7k tokens when it runs, and up to ~10k if it reads all its reference files. Until then it costs about 198 tokens; SKILL.md has 1,526 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from magnus919/agent-skills at commit 22b4723, republished under its MIT licence (© magnus919). 1,526 words, ~4,680 tokens.
Download SKILL.mdSave it as
.claude/skills/product-lifecycle-learning/SKILL.md (or your agent's skills folder). This skill also uses 12 other files; get the full folder from GitHub.- name
- product-lifecycle-learning
- description
- Compare intended product outcomes against observed results to close the launch-to-learning loop: collect post-launch evidence, distinguish expected from observed from uncertain from inferred claims, update assumptions, assess feature health, and choose among continue/improve/harvest/pivot/pause/retire — including retirement lifecycles with deprecation, migration, customer treatment, and retained reusable learning. Do not use for incident postmortems or root-cause analysis (routes to incident-learning or site-reliability-engineering); do not use for analytics instrumentation or metric dashboard design (routes to product-analytics-and-measurement); do not use arbitrary thresholds as universal retirement rules — decisions require human judgment and context.
- license
- MIT
- metadata.tags
- product-lifecycle-learning, post-launch-review, outcome-review, feature-health, assumption-update, retirement-decisions, deprecation, sunset-planning…
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 |
Core Methodology
The Launch-to-Learning Loop
LAUNCH → [OBSERVE] → [COMPARE] → [IDENTIFY GAPS] → [UPDATE ASSUMPTIONS] → [ASSESS HEALTH] → [DECIDE] → [CAPTURE LEARNING] → (feed back)
| | | | | | |
Collect Expected vs. Gap analysis Assumption Feature health Continue / Retained
outcome observed with confidence ledger update dimensions Improve / learning
data outcomes intervals Harvest / record
Pivot /
Pause /
RetireThe loop starts after launch (the feature or capability is live and generating data) and ends with a durable learning artifact that feeds the next cycle of roadmap, analytics, adoption, experimentation, and specification work.
Stage-by-Stage
| Stage | Input | Activity | Output |
|---|---|---|---|
| Observe | Analytics data, adoption metrics, user feedback, support tickets, operational metrics | Collect outcome evidence from observed behavior and system data. Distinguish signal from noise. Flag missing or low-confidence data. | Collected outcome data with confidence labels |
| Compare | Expected outcomes (from spec/roadmap), observed outcomes, confidence intervals | Compare the two; identify alignment, deviation, and surprise. Do not conflate expectation with observation. | Gap analysis: what matched, what diverged, what was ambiguous |
| Identify gaps | Gap analysis, assumption ledger | Identify which assumptions held and which broke. Distinguish between measurement gaps (could not observe) and outcome gaps (observed deviation). | Assumption gap register with confidence |
| Update assumptions | Assumption gap register, prior assumption ledger | Revise assumptions: strengthen confirmed ones, weaken contradicted ones, add new ones surfaced by the data. Record confidence shifts. | Updated assumption ledger. Use templates/assumption-ledger-update.md. |
| Assess health | Updated assumptions, adoption data, operational metrics, user feedback | Evaluate feature health across adoption, technical, operational, and strategic dimensions. Do not reduce to a single score. | Feature health assessment. Use templates/feature-health-record.md. |
| Decide | Feature health assessment, business context, portfolio priorities | Choose one of six lifecycle decisions. The decision requires human judgment; no automated threshold. | Decision record with accountable owner. Use templates/retirement-decision.md. |
| Capture learning | Decision record, gap analysis, updated assumptions, context | Produce a durable retained learning record: what was learned, why, and how it should inform future work. Not a transient meeting summary. | Retained learning record. Use templates/retained-learning-record.md. |
| Feed back | Retained learning record | Route learning to downstream skills: roadmap, analytics, adoption, experimentation, specifications. See references/feedback-destinations.md. | Routed learning outputs |
Epistemic Discipline
Every claim in lifecycle-learning output is classified into exactly one of four categories. These are not conflated; a comparison is not an observation, and an inference is not a fact.
| Category | Definition | Example | Source |
|---|---|---|---|
| Expected | What was intended or predicted before launch | "We expected activation to reach 60% within 30 days" | Spec, roadmap, launch brief |
| Observed | What actually happened, measured from data | "Activation reached 43% at 30 days (95% CI: 39-47%)" | Analytics, adoption data, operational metrics |
| Uncertain | What is ambiguous, noisy, or contested | "Attribution is confounded by a simultaneous pricing change; cannot isolate feature effect" | Confidence intervals, conflicting signals, data-quality issues |
| Inferred | What is concluded from evidence, with reasoning | "The gap between expected 60% and observed 43% suggests the onboarding redesign did not reduce time-to-value as hypothesized; the pricing change confound means we cannot rule out an external cause" | Reasoned implication from evidence |
Full taxonomy and field guide in references/epistemic-discipline.md.
Lifecycle Decisions
Six outcomes are available after assessment. The choice requires human judgment informed by evidence; no numeric threshold or automated rule replaces context and accountability.
| Decision | Meaning | Typical evidence profile | Follow-up |
|---|---|---|---|
| Continue | Keep as-is; feature is healthy | Outcomes match or exceed expectations; stable, low-risk | Schedule next review |
| Improve | Invest in enhancement | Adoption gap exists but fixable; underlying need confirmed | Feed roadmap and experimentation |
| Harvest | Reduce investment, maintain for existing users | Declining growth but stable base; not worth expanding | Monitor for retirement signals |
| Pivot | Change direction significantly | Need confirmed but current approach failed | Feed roadmap, discovery, experimentation |
| Pause | Temporarily halt investment | Ambiguous results, external confounds, or resource constraint | Schedule re-assessment with new evidence |
| Retire | Deprecate and remove | Sustained non-adoption, replacement exists, or strategic misalignment | Execute retirement lifecycle |
Retirement Lifecycle
When the decision is Retire, a structured retirement lifecycle covers the full path from deprecation announcement through internal cleanup. Full detail in references/retirement-lifecycle.md.
| Phase | Activity | Template |
|---|---|---|
| Deprecation communication | Announce retirement: timeline, rationale, alternatives. Target affected users with segmentation. | templates/sunset-plan.md |
| Migration path | Provide migration tooling, documentation, and support for existing users. Define the recommended path. | templates/sunset-plan.md |
| Customer treatment | Support commitments during sunset: data export, grace periods, extended support windows, SLA preservation, refund/credit policies where applicable. Coordinate with customer-success. | templates/sunset-plan.md; route communication plans to conditional-customer-success |
| Internal cleanup | Remove feature flags, archive code, update documentation, retire monitoring and alerting, reclaim infrastructure. | templates/sunset-plan.md |
| Learning closure | Capture what the feature's lifecycle taught — not a postmortem, but a closure record that completes the learning loop. | templates/retained-learning-record.md |
Show full SKILL.md (634 more words)Show less
Retained Learning Record
Every lifecycle-learning cycle produces a durable retained learning record — not a transient meeting summary. The record captures:
- What the feature or capability was intended to achieve (expected outcomes)
- What actually happened (observed outcomes, with confidence)
- What was uncertain and why
- What assumptions were updated and how
- What decision was made (continue/improve/harvest/pivot/pause/retire) and who made it
- Why that decision was reached, with evidence
- What should inform future decisions — reusable patterns, anti-patterns, assumptions to test next time
- Where the learning was routed (roadmap, analytics, adoption, experimentation, specifications)
This record is the durable learning artifact. It is the evidence that the launch-to-learning loop actually closed.
When Not to Use
This skill does not own:
- Incident postmortems, root-cause analysis, or operational incident review — these belong to
incident-learning(not yet landed) and ../site-reliability-engineering/SKILL.md. Lifecycle-learning consumes incident signals as input but does not produce postmortems. - Analytics instrumentation, metric dashboard design, tracking-plan creation, or event taxonomy — these belong to ../product-analytics-and-measurement/SKILL.md. Lifecycle-learning consumes analytics data as input but does not own measurement infrastructure.
- Customer-success account management, renewal decisions, or health scoring — these belong to
conditional-customer-success(not yet landed). Lifecycle-learning routes retirement communication plans and customer-treatment strategies there. - Roadmap prioritization or portfolio allocation — these belong to ../product-roadmapping-and-portfolio/SKILL.md. Lifecycle-learning feeds evidence into roadmap decisions but does not make them.
- Arbitrary or automated retirement thresholds — this skill never applies rules like "retire if DAU < 100" or "kill if NPS < 30" without context about the product, market, user base, and alternatives. Retirement decisions require human judgment and named accountability.
Routing and Feedback
Inputs (consumed by lifecycle-learning)
| Input | Source |
|---|---|
| Expected outcomes, acceptance criteria | ../spec-driven-development/SKILL.md, roadmap briefs |
| Observed outcomes, metric data, funnels, cohorts | ../product-analytics-and-measurement/SKILL.md |
| Adoption evidence, activation rates, retention signals | ../product-adoption/SKILL.md |
| Experiment results, readout learning entries | ../product-experimentation/SKILL.md |
| Incident signals, reliability data | ../site-reliability-engineering/SKILL.md, incident-learning |
| Customer feedback, support trends, health signals | conditional-customer-success |
Outputs (produced by lifecycle-learning, routed to)
| Output | Destination | Purpose |
|---|---|---|
| Revised assumptions, decision evidence | ../product-roadmapping-and-portfolio/SKILL.md | Roadmap updates, bet re-evaluation |
| Metric refinement needs, measurement gaps | ../product-analytics-and-measurement/SKILL.md | Improve instrumentation, close measurement gaps |
| Adoption pattern changes, behavior insights | ../product-adoption/SKILL.md | Adoption strategy adjustments |
| New hypotheses, experiment ideas | ../product-experimentation/SKILL.md | Feed experimentation pipeline |
| Spec improvements, acceptance-criteria refinements | ../spec-driven-development/SKILL.md | Future specification quality |
| Retirement communication plans, migration coordination, customer treatment during sunset | conditional-customer-success | Customer-facing retirement execution; prose reference (skill not yet landed) |
| Incident-driven learning signals | incident-learning | Incident-driven learning loop; prose reference (skill not yet landed) |
At least five feedback destinations must be updated per cycle: roadmap, analytics, adoption, experimentation, and specifications. Additional routing to customer-success and incident-learning is conditional on the decision.
File Map
| File | Purpose | Load when |
|---|---|---|
| references/discovery-brief.md | Maps existing lifecycle, learning, and retirement material; ownership boundaries | Understanding the skill's place in the catalog |
| references/epistemic-discipline.md | Full taxonomy: expected / observed / uncertain / inferred with field guide | Classifying claims in any lifecycle-learning output |
| references/retirement-lifecycle.md | Complete retirement lifecycle: deprecation, migration, customer treatment, internal cleanup | Retirement decision or sunset planning |
| references/feedback-destinations.md | Detailed routing guide for each feedback destination | Routing learning outputs to downstream skills |
| templates/outcome-review.md | Structured post-launch outcome review | Conducting an outcome review |
| templates/assumption-ledger-update.md | Assumption ledger update with confidence shifts | Updating assumptions after new evidence |
| templates/feature-health-record.md | Multi-dimensional feature health assessment | Assessing feature health |
| templates/retirement-decision.md | Justified retirement or continuation decision record | Making a lifecycle decision |
| templates/sunset-plan.md | Deprecation communication, migration, customer treatment, internal cleanup plan | Planning a retirement execution |
| templates/retained-learning-record.md | Durable reusable learning artifact | Capturing learning that survives the feature |
Related Skills
- ../product-analytics-and-measurement/SKILL.md — Owns instrumentation and metric definition. Lifecycle-learning consumes analytics outputs.
- ../product-adoption/SKILL.md — Owns adoption diagnostics and strategy. Lifecycle-learning consumes adoption evidence.
- ../product-experimentation/SKILL.md — Owns experiment design and readout. Lifecycle-learning consumes experiment results.
- ../product-roadmapping-and-portfolio/SKILL.md — Owns roadmap and portfolio decisions. Lifecycle-learning feeds evidence.
- ../spec-driven-development/SKILL.md — Owns specifications and acceptance criteria. Lifecycle-learning feeds spec improvements.
- ../site-reliability-engineering/SKILL.md — Owns operational reliability. Lifecycle-learning consumes incident signals.
conditional-customer-success— Consumer for retirement communication plans, customer treatment during sunset, migration support coordination. Prose reference; skill not yet landed.incident-learning— Destination for incident-driven learning signals. Prose reference; skill not yet landed.
© magnus919, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 12 other files (references) in product-lifecycle-learning of magnus919/agent-skills.
- SKILL.md
- README.md
- evals/evals.json
- references/discovery-brief.md
- references/epistemic-discipline.md
- references/feedback-destinations.md
- references/retirement-lifecycle.md
- templates/assumption-ledger-update.md
- templates/feature-health-record.md
- templates/outcome-review.md
- templates/retained-learning-record.md
- templates/retirement-decision.md
- templates/sunset-plan.md
Open the folder on GitHubat commit 22b4723
Compare with similar skills
Product Lifecycle Learning next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Product Lifecycle Learning this skillmagnus919/agent-skills | 115 | — | ~4.7k | Automated safety check: Pass | MIT | |
| Post Mortemthananon/9arm-skills | 3.2k | — | ~3.4k | Automated safety check: Pass | None | |
| Broken API InterviewerPrepLabsAI/InterviewMentor | 112 | — | ~2.6k | Automated safety check: Pass | MIT | |
| Post-Incident DebriefVeryGoodOpenSource/vgv-wingspan | 109 | — | ~1.9k | Automated safety check: Pass | MIT | |
| Conducting Post Incident Lessons Learnedmukul975/Anthropic-Cybersecurity-Skills | 34k | — | ~1.7k | Automated safety check: Pass | Apache-2.0 | |
| Post Mortemhanamizuki/solopreneur | 152 | — | ~1.7k | Automated safety check: Pass | MIT |
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Related
Questions about Product Lifecycle Learning
What does Product Lifecycle Learning do?
Compare intended product outcomes against observed results to close the launch-to-learning loop: collect post-launch evidence, distinguish expected from observed from uncertain from inferred claims…. Product Lifecycle Learning is an agent skill from magnus919/agent-skills. Compare intended product outcomes against observed results to close the launch-to-learning loop: collect post-launch evidence, distinguish expected from observed from uncertain from inferred claims, update assumptions, assess feature health, and choose among continue/improve/harvest/pivot/pause/retire — including retirement lifecycles with deprecation, migration, customer treatment, and retained reusable learning.
When should I use Product Lifecycle Learning?
Product Lifecycle Learning fits situations like: incident postmortems; root-cause analysis (routes to incident-learning; site-reliability-engineering); do not use for analytics instrumentation.
How do I install Product Lifecycle Learning in Claude Code?
Run `npx skills add magnus919/agent-skills --skill product-lifecycle-learning -a claude-code`. Or copy the skill folder (product-lifecycle-learning in magnus919/agent-skills) into .claude/skills/product-lifecycle-learning in your project. Claude Code loads it when a task matches its description.
How do I install Product Lifecycle Learning in Codex?
Run `npx skills add magnus919/agent-skills --skill product-lifecycle-learning -a codex`. Or copy the skill folder (product-lifecycle-learning in magnus919/agent-skills) into .agents/skills/product-lifecycle-learning in your project. Codex loads it when a task matches its description.
Can I use Product Lifecycle Learning in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add magnus919/agent-skills --skill product-lifecycle-learning -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/product-lifecycle-learning, .gemini/skills/product-lifecycle-learning, .github/skills/product-lifecycle-learning and .opencode/skills/product-lifecycle-learning in your project.
What does Product Lifecycle Learning need to run?
SKILL.md names no scripts, command-line tools or credentials: Product Lifecycle Learning is instructions for the agent only.
Does Product Lifecycle Learning access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Product Lifecycle Learning safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Product Lifecycle Learning use?
Product Lifecycle Learning is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Product Lifecycle Learning use?
About 4.7k tokens (SKILL.md is roughly 19k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 5.7k tokens, read only when the agent opens those files.
What are the alternatives to Product Lifecycle Learning?
Skills that share tags, products or a category with Product Lifecycle Learning: Post Mortem (thananon/9arm-skills, 3.2k stars), Broken API Interviewer (PrepLabsAI/InterviewMentor, 112 stars), Post-Incident Debrief (VeryGoodOpenSource/vgv-wingspan, 109 stars) and Conducting Post Incident Lessons Learned (mukul975/Anthropic-Cybersecurity-Skills, 34k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Product Lifecycle Learning?
magnus919 (a GitHub user) maintains it in magnus919/agent-skills, which has 115 GitHub stars. The repository holds 131 skills in this directory. The repository was last updated on October 10, 2026.
Source: magnus919/agent-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.