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 Ootto-AI/claude-content-skills --skill launch-retrospectivegit clone --depth 1 https://github.com/Ootto-AI/claude-content-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/ootto-ai/claude-content-skills/launch-retrospective)<a href="https://agentmods.dev/skills/ootto-ai/claude-content-skills/launch-retrospective"><img src="https://agentmods.dev/badge/skills/ootto-ai/claude-content-skills/launch-retrospective/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/ootto-ai/claude-content-skills/launch-retrospective"><img src="https://agentmods.dev/badge/skills/ootto-ai/claude-content-skills/launch-retrospective.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00114 | $0.00649 |
| Opus 5 | $0.00057 | $0.00324 |
| Sonnet 5 | $0.00023 | $0.00130 |
| Haiku 4.5 | $0.00011 | $0.00065 |
Grade A, and why
launch-retrospective 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 12d 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 — 46 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Launch Retrospective
Turn a completed campaign into durable operational learning: what happened, why it may have happened, and the next smallest decision.
1. Reconstruct the intended plan
Bring together the original objective, audience, approved message, channel roles, budget or partner scope where supplied, timing, measurement plan, and decisions made during execution. Compare what actually shipped with what was planned. Separate a deliberate change from an accidental deviation.
2. Gather the evidence with limits attached
Use available performance data, qualitative feedback, partner notes, approvals, and execution records. State dates, metric definitions, missing data, and material confounders. Keep a timeline so the team does not attribute an outcome to a change that happened after it.
3. Distinguish facts from explanations
List observations first: what was published, what audiences did, and what tracking shows. Then list hypotheses about message, format, distribution, timing, destination, or process. Give each hypothesis a confidence level and the evidence that would strengthen or weaken it.
4. Commit to operational changes
Return a short set of keep, change, test, and stop decisions with owners and due dates. Update reusable inputs such as the campaign brief template, proof library, UTM vocabulary, or creator brief. Leave a clear record of unresolved questions so they are not rewritten as facts next time.
Hard rules
- Do not use a retrospective to blame individuals, creators, or customers for an outcome.
- Never claim causation where the evidence only shows correlation.
- Include negative and inconclusive results; they are part of the learning.
- Do not compare campaigns with different objectives or measurement conditions as if they were identical.
- Separate process failure from message failure before prescribing a fix.
Failure modes
| Failure | Do this instead |
|---|---|
| The retro is a victory lap or blame document | Use a factual timeline, uncertainty, and future-oriented decisions. |
| A single metric explains the whole launch | Combine the original job, data boundary, qualitative evidence, and process record. |
| Lessons stay as vague observations | Turn each learning into a named change, test, or stop decision. |
| The same tracking issue returns next launch | Update the UTM or measurement workflow with a clear owner. |
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.
- 12d ago First seen · 46 lines · 114 tokens per session scan A 95e618a5d476
launch-retrospective is a skill published in the GitHub repository Ootto-AI/claude-content-skills (28 stars, last pushed 19d ago), licensed MIT. It adds 114 tokens to every session and 649 once invoked, about $0.0006 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 2026-08-30.
Other skills, from other repositories
scrapecreators-api
Scrape and extract public data from 27+ social media platforms using the ScrapeCreators REST API. Covers TikTok, Instagram, YouTube, LinkedIn, Facebook, Twitter/X, Reddit, Threads, Bluesky, Pinterest, Snapchat, Twitch, Kick, Truth Social, TikTok Shop, Google, and link-in-bio services (Linktree, Komi, Pillar, Linkbio…
outlier-post-finder
Use when the user wants to find posts, videos, reels, shorts, tweets, or social content that overperformed versus a creator, brand, or competitor baseline. Finds outliers, explains why they worked, extracts hooks and formats, and produces a practical swipe file.
competitor-social-research
Use when the user wants to research competitors' social media strategy, compare brands or creators, find what content is working in a niche, identify content gaps, or produce a practical social strategy brief from public social data.
ad-library-teardown
Use when the user wants to analyze active ads from Meta/Facebook, Google, or LinkedIn ad libraries; tear down a competitor's messaging; extract hooks, offers, CTAs, video transcripts, landing page claims, and test ideas from public ads.
comment-mining
Use when the user wants to mine comments and replies for audience reactions, customer language, questions, objections, complaints, product ideas, buying intent, sentiment, or voice-of-customer insights from public social posts and videos.
transcript-intelligence
Use when the user wants to summarize, analyze, or repurpose transcripts from TikTok, Instagram, YouTube, Facebook, X/Twitter, LinkedIn, Rumble, or Reddit video posts. Extracts hooks, claims, quotes, content atoms, themes, and reusable scripts.