Recipe Post Mortem Setup
googleworkspace/cli
Create a Google Docs post-mortem, schedule a Google Calendar review, and notify via Chat.
Generate professional reports — sprint retrospectives, financial summaries, analytics dashboards, and incident postmortems — from structured data with templates, charts, and multi-format output.
$ npx skills add seb1n/awesome-ai-agent-skills --skill report-generation -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install seb1n/awesome-ai-agent-skills report-generation --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/seb1n/awesome-ai-agent-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/communication/report-generation .claude/skills/report-generation && 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 "report-generation" agent skill from https://github.com/seb1n/awesome-ai-agent-skills/tree/main/communication/report-generation into .claude/skills/report-generation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "report-generation", 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/seb1n/awesome-ai-agent-skills/tree/main/communication/report-generationType 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 seb1n/awesome-ai-agent-skills --skill report-generation -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install seb1n/awesome-ai-agent-skills report-generation --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/seb1n/awesome-ai-agent-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/communication/report-generation .agents/skills/report-generation && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "report-generation" agent skill from https://github.com/seb1n/awesome-ai-agent-skills/tree/main/communication/report-generation into .agents/skills/report-generation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "report-generation", 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 seb1n/awesome-ai-agent-skills --skill report-generation -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install seb1n/awesome-ai-agent-skills report-generation --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/seb1n/awesome-ai-agent-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/communication/report-generation .cursor/skills/report-generation && 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 "report-generation" agent skill from https://github.com/seb1n/awesome-ai-agent-skills/tree/main/communication/report-generation into .cursor/skills/report-generation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "report-generation", 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/seb1n/awesome-ai-agent-skills.git --path communication/report-generation--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 seb1n/awesome-ai-agent-skills --skill report-generation -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install seb1n/awesome-ai-agent-skills report-generation --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/seb1n/awesome-ai-agent-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/communication/report-generation .gemini/skills/report-generation && 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 "report-generation" agent skill from https://github.com/seb1n/awesome-ai-agent-skills/tree/main/communication/report-generation into .gemini/skills/report-generation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "report-generation", 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 seb1n/awesome-ai-agent-skills report-generationInstalls 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 seb1n/awesome-ai-agent-skills --skill report-generation -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/seb1n/awesome-ai-agent-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/communication/report-generation .github/skills/report-generation && 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 "report-generation" agent skill from https://github.com/seb1n/awesome-ai-agent-skills/tree/main/communication/report-generation into .github/skills/report-generation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "report-generation", 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 seb1n/awesome-ai-agent-skills --skill report-generation -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install seb1n/awesome-ai-agent-skills report-generation --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/seb1n/awesome-ai-agent-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/communication/report-generation .opencode/skills/report-generation && 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 "report-generation" agent skill from https://github.com/seb1n/awesome-ai-agent-skills/tree/main/communication/report-generation into .opencode/skills/report-generation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "report-generation", 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.
report-generationGenerate professional reports — sprint retrospectives, financial summaries, analytics dashboards, and incident postmortems — from structured data with templates, charts, and multi-format output.
Report Generation is an agent skill from seb1n/awesome-ai-agent-skills. Generate professional reports — sprint retrospectives, financial summaries, analytics dashboards, and incident postmortems — from structured data with templates, charts, and multi-format output. Use when the user requests report generation or provides relevant inputs for this workflow.
Its SKILL.md is about 3.4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Documents & Office, covering Retrospectives, Schema markup and Runbooks and postmortems. The repository describes itself as: 103 ready-to-use AI agent skills for Claude Code, OpenAI Codex, Gemini CLI, Cursor, GitHub Copilot, Windsurf, and other Agent Skills-compatible tools. Complete SKILL.md… The licence is MIT.
6 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit 75865a5. It shows what the files ask for, not the result of running them.
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.
No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown and json).
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Report Generation loads about 3.4k tokens when it runs. Until then it costs about 76 tokens; SKILL.md has 883 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.
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.
The full file from seb1n/awesome-ai-agent-skills at commit 75865a5, republished under its MIT licence (© seb1n). 883 words, ~3,439 tokens.
.claude/skills/report-generation/SKILL.md (or your agent's skills folder).This skill enables an AI agent to produce polished, data-driven reports from structured input. The agent accepts data in JSON, CSV, or API response format, applies a report template, generates narrative insights alongside tables and chart specifications, and outputs the final report in Markdown, HTML, or PDF. It supports common report types including sprint retrospectives, financial summaries, analytics dashboards, and incident postmortems.
Ingest and validate the source data. Accept the input data file or payload in JSON, CSV, YAML, or raw API response format. Validate the schema — check for required fields, correct data types, missing values, and outliers. If the data is incomplete (e.g., a sprint retro JSON missing the velocity field), flag the gap and either infer a default, request the missing value, or note the omission in the report. Normalize date formats, currency symbols, and units for consistency.
Select or customize the report template. Match the data to a report template based on the report type specified by the user. Built-in templates include: sprint_retrospective, financial_summary, analytics_dashboard, incident_postmortem, and weekly_status. Each template defines the section order, required data mappings, chart types, and tone (analytical for financial reports, constructive for retros, urgent for postmortems). Users can customize templates by overriding sections, adding fields, or changing the visual theme.
Compute metrics and generate insights. Derive computed metrics from the raw data — percentage changes, averages, rankings, trend directions, and anomaly flags. For a sprint retro, calculate velocity variance and commitment accuracy. For an analytics report, compute conversion rates and segment-level breakdowns. Then generate narrative insights: not just "conversion rate was 3.2%" but "conversion rate dropped 0.8pp from last period, driven primarily by a 15% decline in mobile traffic."
Populate the report structure. Fill the template sections with the computed metrics, narrative insights, and formatted data tables. Generate chart specifications (bar, line, pie, or table) for each visualization slot in the template. Use Markdown table syntax for inline tables and a chart spec format (e.g., Mermaid, Vega-Lite, or Chart.js JSON) for visual charts. Add section headers, executive summary, and any appendices.
Format and render the output. Produce the final report in the requested format. For Markdown, output a clean .md file with tables and chart code blocks. For HTML, wrap the Markdown output in a styled template with CSS for print-friendly rendering. For PDF, convert the HTML version using a headless browser or a tool like Puppeteer or WeasyPrint. Ensure charts render correctly in all formats and that tables don't break across pages in PDF.
Review and finalize. Run a quality check: verify all data points in the report trace back to the source data, ensure no template variables remain unresolved (e.g., {{quarter}}), confirm that insights are supported by the numbers, and check that the report length matches the expected range for its type. Deliver the report with a brief summary of what was generated.
Provide the agent with the report type, source data, any customization preferences, and the desired output format. The agent returns a complete, formatted report.
Prompt format:
Generate a [report type] report.
Data source: [path to JSON/CSV file, or inline data]
Time period: [date range or sprint number]
Template: [built-in template name or "custom"]
Output format: [markdown / html / pdf]Input data (JSON):
{
"sprint": {
"number": 14,
"start_date": "2026-01-27",
"end_date": "2026-02-07",
"goal": "Ship checkout flow redesign and analytics instrumentation"
},
"velocity": {
"committed": 34,
"completed": 29,
"previous_sprints": [31, 28, 33, 30]
},
"stories": {
"total": 12,
"completed": 10,
"carried_over": 2,
"carried_over_items": [
"CHECKOUT-142: Edge case handling for international addresses",
"ANALYTICS-89: Cross-domain tracking for subdomain events"
]
},
"what_went_well": [
"Checkout redesign shipped 2 days early",
"Zero P1 bugs in production release",
"Effective pairing sessions between frontend and backend teams"
],
"what_could_improve": [
"Sprint scope increased mid-sprint with 3 unplanned tickets",
"QA bottleneck on Thursday caused idle time for 2 developers",
"Standup meetings ran over 15 minutes on 3 of 10 days"
],
"action_items": [
{ "owner": "Priya", "action": "Add a mid-sprint scope freeze policy" },
{ "owner": "James", "action": "Schedule a dedicated QA window on Wednesdays" },
{ "owner": "Dana", "action": "Timekeeper rotation for standups" }
]
}Output (Markdown report):
# Sprint 14 Retrospective
**Sprint dates:** January 27 – February 7, 2026
**Sprint goal:** Ship checkout flow redesign and analytics instrumentation
---
## Executive Summary
Sprint 14 delivered 85% of committed story points (29 of 34), slightly below
the team's rolling average velocity of 30.5. The primary sprint goal was
partially met: the checkout redesign shipped ahead of schedule, but two
analytics stories carried over due to mid-sprint scope changes and a QA
bottleneck. Zero P1 bugs were introduced, reflecting strong code quality.
## Velocity
| Metric | Sprint 14 | 4-Sprint Average |
|----------------------|-----------|------------------|
| Committed | 34 pts | 31.0 pts |
| Completed | 29 pts | 30.5 pts |
| Commitment accuracy | 85% | 98% |
The team over-committed by 3 points relative to the historical average.
The shortfall is attributable to 3 unplanned tickets added mid-sprint,
which consumed approximately 5 story points of capacity.
## Story Completion
- **Completed:** 10 of 12 stories (83%)
- **Carried over:** 2 stories
- CHECKOUT-142: Edge case handling for international addresses
- ANALYTICS-89: Cross-domain tracking for subdomain events
Both carried-over stories are estimated at 2-3 points each and will be
prioritized at the top of Sprint 15.
## What Went Well
- Checkout redesign shipped 2 days ahead of the sprint end date, allowing
extra time for integration testing.
- Zero P1 bugs in the production release — the team's pairing sessions
caught issues early in development.
- Cross-functional pairing sessions were cited by 3 team members as the
highlight of the sprint.
## What Could Improve
- **Mid-sprint scope creep:** 3 unplanned tickets were added after sprint
planning, consuming capacity and contributing to the 2 carried-over stories.
- **QA bottleneck:** All completed stories landed in QA on Thursday, creating
a pileup that left 2 developers idle on Friday morning.
- **Standup duration:** Standups exceeded 15 minutes on 3 of 10 days.
## Action Items
| Owner | Action | Target Date |
|-------|--------------------------------------------------|-------------|
| Priya | Add a mid-sprint scope freeze policy | Feb 10 |
| James | Schedule a dedicated QA window on Wednesdays | Feb 10 |
| Dana | Implement timekeeper rotation for standups | Feb 10 |
---
*Report generated from sprint data. Source: sprint-14-retro.json*Input:
Generate an analytics report for the marketing website.
Data source: /data/website-analytics-jan-2026.json
Time period: January 2026
Output format: markdownOutput:
# Website Analytics Report — January 2026
**Reporting period:** January 1–31, 2026
**Compared to:** December 2025
---
## Executive Summary
Website traffic increased 12% month-over-month to 284,000 sessions, driven
primarily by organic search growth (+18%). However, overall conversion rate
declined from 4.0% to 3.2%, with the sharpest drop on mobile devices. The
pricing page continues to be the highest-converting entry point, while the
blog showed strong traffic growth but low conversion intent.
## Traffic Overview
| Metric | January 2026 | December 2025 | Change |
|--------------------|-------------|---------------|---------|
| Total sessions | 284,000 | 253,500 | +12.0% |
| Unique visitors | 198,600 | 179,400 | +10.7% |
| Pages per session | 3.2 | 3.4 | -5.9% |
| Avg session dur. | 2m 48s | 3m 05s | -9.2% |
| Bounce rate | 42% | 38% | +4pp |
## Traffic by Channel
| Channel | Sessions | Share | MoM Change |
|----------------|----------|-------|------------|
| Organic Search | 142,000 | 50% | +18.3% |
| Direct | 62,500 | 22% | +5.1% |
| Paid Search | 39,800 | 14% | +8.7% |
| Social | 25,600 | 9% | +2.3% |
| Referral | 14,100 | 5% | -3.8% |
**Insight:** Organic search growth (+18.3%) was the primary traffic driver,
likely attributable to the 6 new blog posts published in January. However,
the drop in pages-per-session and increased bounce rate suggest this new
organic traffic has lower engagement intent than existing visitors.
## Conversion Funnel
| Stage | Visitors | Rate | MoM Change |
|------------------------|----------|--------|------------|
| Homepage to Pricing | 84,200 | 29.6% | -1.2pp |
| Pricing to Signup | 15,400 | 18.3% | -2.1pp |
| Signup to Activation | 9,100 | 59.1% | +0.8pp |
| **Overall conversion** | **9,100**| **3.2%** | **-0.8pp** |
**Insight:** The conversion drop is concentrated in the Pricing to Signup
step (-2.1pp). A/B test data shows the new pricing page variant (launched
Jan 15) underperforms the control by 1.8pp. Recommendation: revert to the
control pricing page and redesign the variant.
## Recommendations
1. **Revert the pricing page A/B test.** The new variant is underperforming
by 1.8pp on the key Pricing to Signup conversion step.
2. **Add conversion-intent CTAs to blog posts.** The 18% organic traffic
growth is not converting. Embed contextual product CTAs within blog
content to capture high-intent readers.
3. **Investigate mobile conversion drop.** Mobile sessions grew 15% but
mobile conversion fell 1.2pp. Audit the mobile signup flow for UX
friction, particularly form field usability and page load speed.
4. **Double down on organic content.** The SEO investment is paying off
in traffic. Publish 8 posts in February targeting mid-funnel keywords
with stronger commercial intent.
---
*Report generated from website-analytics-jan-2026.json. Data: Google Analytics.*© seb1n, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Just SKILL.md in communication/report-generation of seb1n/awesome-ai-agent-skills.
Open the folder on GitHubat commit 75865a5
Report Generation 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 |
|---|---|---|---|---|---|---|
| Report Generation this skillseb1n/awesome-ai-agent-skills | 206 | — | ~3.4k | Automated safety check: Pass | MIT | |
| Recipe Post Mortem Setupgoogleworkspace/cli | 31k | — | ~256 | Automated safety check: Pass | Apache-2.0 | |
| Campaign Postmortemzapier/gtm-cheat-codes | 342 | — | ~438 | Automated safety check: Pass | MIT | |
| Post Mortemalirezarezvani/claude-skills | 28k | — | ~968 | Automated safety check: Pass | MIT | |
| Spreadsheet Handovermohitagw15856/pm-claude-skills | 1.4k | — | ~1.4k | Automated safety check: Pass | MIT | |
| After Action Reportrampstackco/claude-skills | 940 | 1 repos | ~2.5k | Automated safety check: Pass | MIT |
googleworkspace/cli
Create a Google Docs post-mortem, schedule a Google Calendar review, and notify via Chat.
zapier/gtm-cheat-codes
Build sourced campaign retrospectives from goals, launch context, performance data, team feedback, and follow-up actions.
alirezarezvani/claude-skills
/cs:post-mortem <decision — Honest retrospective on an executed decision, scored against original assumptions and dissent.
mohitagw15856/pm-claude-skills
Hand over a spreadsheet so it survives its author leaving — the README tab that decodes the sheet's logic, the update runbook with sources and cadence, the fragility warnings, and the walkthrough…
rampstackco/claude-skills
Run a structured after-action review (postmortem, retrospective) on a launch, incident, or completed project to capture timeline, root cause analysis, contributing factors, and actionable lessons.
briiirussell/cybersecurity-skills
Learn from public breach disclosures — extract the audit question each one implies and check your own stack.
seb1n/awesome-ai-agent-skills
Plan, execute, document, and retest authorized security assessments of AI agents and multi-agent workflows using safe adversarial cases, synthetic identities, canaries, and evidence-based findings.
seb1n/awesome-ai-agent-skills
Build a preliminary, evidence-based EU AI Act readiness assessment across AI-system inventory, territorial scope, operator roles, prohibited-practice screening, risk classification, transparency…
seb1n/awesome-ai-agent-skills
Design and verify auditable human oversight, approval gates, escalation paths, and safe state transitions for AI agent workflows.
seb1n/awesome-ai-agent-skills
Design, implement, harden, and verify Model Context Protocol (MCP) servers with precise tool contracts, least-privilege authorization, safe transports, structured errors, and interoperability tests.
seb1n/awesome-ai-agent-skills
Audit agent skills, plugins, prompts, manifests, scripts, dependencies, and bundled assets for provenance, prompt-injection, permission, execution, exfiltration, persistence, and update risk.
seb1n/awesome-ai-agent-skills
Inspect, profile, clean, reconcile, analyze, visualize, and verify spreadsheet data while preserving formulas, formatting, types, and source files.
Categories
Generate professional reports — sprint retrospectives, financial summaries, analytics dashboards, and incident postmortems — from structured data with templates, charts, and multi-format output. Report Generation is an agent skill from seb1n/awesome-ai-agent-skills. Generate professional reports — sprint retrospectives, financial summaries, analytics dashboards, and incident postmortems — from structured data with templates, charts, and multi-format output.
Report Generation fits situations like: the user requests report generation; provides relevant inputs for this workflow.
Run `npx skills add seb1n/awesome-ai-agent-skills --skill report-generation -a claude-code`. Or copy the skill folder (communication/report-generation in seb1n/awesome-ai-agent-skills) into .claude/skills/report-generation in your project. Claude Code loads it when a task matches its description.
Run `npx skills add seb1n/awesome-ai-agent-skills --skill report-generation -a codex`. Or copy the skill folder (communication/report-generation in seb1n/awesome-ai-agent-skills) into .agents/skills/report-generation in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add seb1n/awesome-ai-agent-skills --skill report-generation -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/report-generation, .gemini/skills/report-generation, .github/skills/report-generation and .opencode/skills/report-generation in your project.
SKILL.md names no scripts, command-line tools or credentials: Report Generation is instructions for the agent only.
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.
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.
Report Generation is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
About 3.4k tokens (SKILL.md is roughly 14k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Report Generation: Recipe Post Mortem Setup (googleworkspace/cli, 31k stars), Campaign Postmortem (zapier/gtm-cheat-codes, 342 stars), Post Mortem (alirezarezvani/claude-skills, 28k stars) and Spreadsheet Handover (mohitagw15856/pm-claude-skills, 1.4k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
seb1n (a GitHub user) maintains it in seb1n/awesome-ai-agent-skills, which has 206 GitHub stars. The repository holds 91 skills in this directory. The repository was last updated on August 9, 2026.
Source: seb1n/awesome-ai-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.