Social Performance Review
stevenflanagan1/social-ai-team
Monthly social media performance review for SMBs. An agent skill from stevenflanagan1/social-ai-team.
Email campaign/sequence performance review composite. An agent skill from gooseworks-ai/goose-skills.
Install Sequence Performance
$ npx skills add gooseworks-ai/goose-skills --skill sequence-performance -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install gooseworks-ai/goose-skills sequence-performance --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/gooseworks-ai/goose-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/outreach/composites/sequence-performance .claude/skills/sequence-performance && 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 "sequence-performance" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/outreach/composites/sequence-performance into .claude/skills/sequence-performance/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sequence-performance", 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/gooseworks-ai/goose-skills/tree/main/skills/outreach/composites/sequence-performanceType 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 gooseworks-ai/goose-skills --skill sequence-performance -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install gooseworks-ai/goose-skills sequence-performance --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/outreach/composites/sequence-performance .agents/skills/sequence-performance && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "sequence-performance" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/outreach/composites/sequence-performance into .agents/skills/sequence-performance/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sequence-performance", 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 gooseworks-ai/goose-skills --skill sequence-performance -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install gooseworks-ai/goose-skills sequence-performance --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/outreach/composites/sequence-performance .cursor/skills/sequence-performance && 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 "sequence-performance" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/outreach/composites/sequence-performance into .cursor/skills/sequence-performance/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sequence-performance", 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/gooseworks-ai/goose-skills.git --path skills/outreach/composites/sequence-performance--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 gooseworks-ai/goose-skills --skill sequence-performance -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install gooseworks-ai/goose-skills sequence-performance --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/outreach/composites/sequence-performance .gemini/skills/sequence-performance && 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 "sequence-performance" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/outreach/composites/sequence-performance into .gemini/skills/sequence-performance/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sequence-performance", 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 gooseworks-ai/goose-skills sequence-performanceInstalls 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 gooseworks-ai/goose-skills --skill sequence-performance -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/outreach/composites/sequence-performance .github/skills/sequence-performance && 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 "sequence-performance" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/outreach/composites/sequence-performance into .github/skills/sequence-performance/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sequence-performance", 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 gooseworks-ai/goose-skills --skill sequence-performance -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install gooseworks-ai/goose-skills sequence-performance --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/outreach/composites/sequence-performance .opencode/skills/sequence-performance && 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 "sequence-performance" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/outreach/composites/sequence-performance into .opencode/skills/sequence-performance/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sequence-performance", 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
sequence-performance- GitHub stars
- 1.2k
- Used in
- 1 other repo
- Token cost
- ~3.3k tokens
- SKILL.md length
- 1,313 words
- Files
- 2
- Skills in repo
- 273
- Repo updated
- First seen
- Licence
- MIT
At a glance
Email campaign/sequence performance review composite. An agent skill from gooseworks-ai/goose-skills.
- Works in 7 steps: Intake → Pull Campaign Data → Quantitative Analysis → …
- Tasks that involve GraphQL
- SKILL.md covers When to Use, Phase 0: Intake, Step 1: Pull Campaign Data and Step 2: Quantitative Analysis, plus 4 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Sequence Performance is an agent skill from gooseworks-ai/goose-skills. Email campaign/sequence performance review composite. Pulls campaign data (sends, opens, replies, bounces), reads actual email copy and subject lines, analyzes reply content (objections, positive interest, questions), and produces a diagnostic report covering quantitative metrics, copy quality, lead quality, and actionable recommendations. Tool-agnostic — works with Smartlead (MCP), Instantly, Outreach, Lemlist, Apollo, or CSV data.
Its SKILL.md is about 3.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `skill.meta.json`).
It sits in Business, Finance & HR, covering GraphQL, Email marketing and Performance reviews. It works with Model Context Protocol. The repository describes itself as: Library of Growth & GTM skills + data APIs for Claude Code, Codex, Cursor to run ads, social, content, lead gen, seo and data scraping. The licence is MIT.
When your agent uses it
- Tasks that involve GraphQL
- Tasks that involve Email marketing
- Tasks that involve Performance reviews
Example prompts
- “/sequence-performance”
Workflow steps
7 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit c650c6d. 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
Sequence Performance loads about 3.3k tokens when it runs. Until then it costs about 114 tokens; SKILL.md has 1,313 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 gooseworks-ai/goose-skills at commit c650c6d, republished under its MIT licence (© gooseworks-ai). 1,313 words, ~3,304 tokens.
Download SKILL.mdSave it as
.claude/skills/sequence-performance/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.- name
- sequence-performance
- description
- Email campaign/sequence performance review composite. Pulls campaign data (sends, opens, replies, bounces), reads actual email copy and subject lines, analyzes reply content (objections, positive interest, questions), and produces a diagnostic report covering quantitative metrics, copy quality, lead quality, and actionable recommendations. Tool-agnostic — works with Smartlead (MCP), Instantly, Outreach, Lemlist, Apollo, or CSV data.
- version
- 1.0.0
- tags
- research
Sequence Performance
Goes beyond vanity metrics. Most campaign reports tell you open rate and reply rate. This skill reads the actual emails you sent, reads every reply you received, classifies the responses, evaluates your copy, evaluates your lead quality, and tells you specifically what's working, what's not, and what to do about it.
Three layers of analysis:
- Quantitative: The numbers — sends, opens, replies, bounces, conversions, by touch and by variant
- Qualitative (Copy): Are the subject lines, email bodies, CTAs, and personalization actually good?
- Qualitative (Replies): What are people actually saying? What objections keep coming up?
When to Use
Use this skill when:
- User says "how's my campaign doing", "sequence performance", "campaign review", "email analytics"
- User says "analyze my outreach", "why isn't my campaign working", "review my email results"
- A campaign has been running for 7+ days and has meaningful data
Phase 0: Intake
Outreach Tool
- What outreach tool do you use? (Smartlead / Instantly / Outreach.io / Lemlist / Apollo / Other)
- How do we access campaign data? (MCP tools / API / CSV export / paste metrics)
Campaign Selection
- Which campaign? (name or ID)
- Date range? (or "all data")
Your Company Context (for copy evaluation)
- What does your company do? (one-liner)
- Who is your ICP? (titles, industries, company size)
- What problem do you solve?
- What's your CTA goal? (book meeting, get reply, drive to page)
Benchmark Context
- Is this cold outreach or warm/nurture?
- What segment are you selling to? (SMB, mid-market, enterprise)
Step 1: Pull Campaign Data
Pull three categories of data from the user's outreach tool:
A) Campaign Metrics
| Data Point | What We Need |
|---|---|
| Total emails sent | By touch (Touch 1, Touch 2, Touch 3, etc.) |
| Total unique recipients | Deduplicated count |
| Opens | By touch, unique opens vs. total opens |
| Replies | By touch, total reply count |
| Bounces | Hard bounces + soft bounces |
| Unsubscribes | Count |
| Clicks | If link tracking is on |
| Positive replies | If categorized in the tool |
| Meetings booked | If tracked |
How to pull by tool:
| Tool | Method |
|---|---|
| Smartlead (MCP) | mcp__smartlead__get_campaign_stats, mcp__smartlead__get_campaign_sequence_analytics, mcp__smartlead__get_campaign_variant_statistics |
| Instantly / Outreach / Lemlist / Apollo | Ask user for CSV export or paste metrics |
| Other | User provides CSV with columns: email, status, opened, replied, bounced |
B) Email Copy (Sequence Content)
Pull the actual templates for every touch:
| Tool | Method |
|---|---|
| Smartlead (MCP) | mcp__smartlead__get_campaign_sequences |
| Others | User pastes the copy or provides CSV export |
C) Reply Content
Pull the actual text of every reply:
| Tool | Method |
|---|---|
| Smartlead (MCP) | mcp__smartlead__get_campaign_leads_history, mcp__smartlead__fetch_master_inbox_replies |
| Others | User provides reply dump or CSV export |
Human Checkpoint
Campaign: [name]
Status: [active/paused/completed]
Sent: X emails to Y recipients
Replies: Z (full text pulled for analysis)
Touches: N touches, M variants
Data looks complete? (Y/n)Step 2: Quantitative Analysis
Benchmarks
| Metric | Cold (SMB) | Cold (Mid-Market) | Cold (Enterprise) | Warm/Nurture |
|---|---|---|---|---|
| Open rate | 40-60% | 30-50% | 25-40% | 50-70% |
| Reply rate | 3-8% | 2-5% | 1-3% | 10-20% |
| Positive reply rate | 1-3% | 0.5-2% | 0.3-1% | 5-10% |
| Bounce rate | <3% | <3% | <2% | <1% |
| Unsubscribe rate | <1% | <1% | <0.5% | <0.5% |
Calculate
Overall metrics: open rate, reply rate, positive reply rate, bounce rate, unsubscribe rate, deliverability rate. Compare each to the benchmark.
Per-touch breakdown:
- Touch-level open/reply rates
- Marginal reply rate (replies from THIS touch / people who received this touch but hadn't replied yet)
- Touch contribution (what % of total replies came from each touch)
Variant analysis (if A/B testing):
- Open rate and reply rate per variant
- Statistical confidence: <50 sends = "insufficient data", 50-100 = "directional", 100-250 = "likely winner", 250+ = "statistically significant"
- Winner recommendation: scale, keep testing, or kill
Step 3: Reply Analysis
Read every reply, classify it, and extract patterns.
Reply Categories
| Category | Definition |
|---|---|
| Positive interest | Wants to learn more, open to a conversation |
| Meeting request | Explicitly asks to meet or provides availability |
| Warm / Curious | Interested but non-committal, asks questions |
| Objection — Timing | Not now, but potentially later |
| Objection — Budget | Can't afford or not a priority |
| Objection — Competitor | Already using a competing solution |
| Objection — Relevance | Doesn't see the fit |
| Objection — Authority | Not the right person |
| Not interested | Flat no |
| Auto-reply / OOO | Automated response |
| Referral | Redirects to someone else |
| Question | Asks about product/offering |
Objection Patterns
- Which objection appears most? (reveals systemic issues)
- Do objections cluster at Touch 1 (bad targeting) vs. Touch 3 (fatigue)?
- Which are handleable (timing, authority) vs. terminal (relevance)?
- What exact language do people use?
Positive Signal Patterns
- Which touch/variant generated positive replies?
- What do positive responders have in common? (title, industry, company size)
- What questions do warm leads ask? (reveals what's missing from the email)
Reply Quality Score
| Score | Criteria |
|---|---|
| Strong | >50% positive/warm. Objections are handleable. |
| Mixed | 30-50% positive. Mix of handleable and terminal. |
| Weak | <30% positive. Dominated by "not interested" and "not relevant." |
| Toxic | High unsubscribe + angry replies. Something is fundamentally wrong. |
Step 4: Copy Quality Assessment
Evaluate the actual email copy against best practices and reply data.
Subject Lines
| Criterion | Red Flags |
|---|---|
| Length | >60 chars gets truncated on mobile |
| Specificity | Generic "Quick question" or "Checking in" |
| Spam triggers | "Free", "Limited time", ALL CAPS |
| Open rate correlation | Low open rate = subject line problem |
Show full SKILL.md (523 more words)Show less
Email Body
| Criterion | Red Flags |
|---|---|
| Hook (first line) | "I'm reaching out because..." or "We are a company that..." |
| Length | Over 150 words |
| Value prop clarity | Jargon, vague language, buzzwords |
| Proof points | No proof = no credibility |
| Personalization | Only {first_name} merge field |
| CTA | Multiple CTAs, high-friction asks, or no CTA |
| Filler language | "Hope this finds you well", "just checking in" |
| Sequence progression | Touch 2 is just a "bump" of Touch 1 |
Grades
Grade each touch A through F on: hook quality, value prop clarity, proof usage, personalization level, CTA quality.
Step 5: Lead Quality Assessment
Evaluate whether we're sending to the right people.
Targeting Check
- Do lead titles match ICP buyer/champion/user personas?
- Are leads in target industries?
- Right seniority level for the ask?
- Company size in target range?
Signal Quality (from replies)
| Pattern | What It Tells You |
|---|---|
| High "not relevant" replies | Sending to people who don't have the problem |
| High "wrong person" replies | Right companies, wrong roles |
| High "already have a solution" | Right problem, late to the party |
| High "timing" objections | Right people, right problem, wrong moment — not a targeting issue |
| Low reply + high open rate | People open but don't find it relevant — copy/targeting mismatch |
| High bounce rate | List quality issue — bad emails, old data |
Step 6: Generate Report
Report Structure
# Sequence Performance Review: [Campaign Name]
**Period:** [date range] | **Status:** [active/paused/completed]
---
## Executive Summary
**Overall verdict:** [One sentence]
| Dimension | Grade | Assessment |
|-----------|-------|-----------|
| Metrics | [A-F] | [one-liner] |
| Copy Quality | [A-F] | [one-liner] |
| Lead Quality | [A-F] | [one-liner] |
| Reply Quality | [Strong/Mixed/Weak/Toxic] | [one-liner] |
### What's Working (Double Down)
- [Specific thing with data]
### What's Not Working (Fix or Kill)
- [Specific thing with data]
### Top 3 Actions
1. [Highest-impact action]
2. [Second]
3. [Third]
---
## Detailed Metrics
### Overall Performance
| Metric | Actual | Benchmark | Status |
|--------|--------|-----------|--------|
| Open rate | X% | Y% | [above/below] |
| Reply rate | X% | Y% | [above/below] |
| Bounce rate | X% | <3% | [flag] |
| ... | ... | ... | ... |
### Performance by Touch
| Touch | Sent | Open Rate | Reply Rate | Marginal Reply Rate | % of Total Replies |
|-------|------|-----------|------------|--------------------|--------------------|
| 1 | X | Y% | Z% | Z% | W% |
### Variant Performance (if A/B testing)
| Touch | Variant | Subject | Sent | Open Rate | Reply Rate | Confidence | Action |
|-------|---------|---------|------|-----------|------------|------------|--------|
---
## Reply Deep Dive
### Reply Classification
| Category | Count | % of Replies |
|----------|-------|-------------|
### Top Objections
| Objection | Count | Handleable? | Suggested Response |
|-----------|-------|------------|-------------------|
### Notable Replies
[5-10 most instructive replies with quotes]
---
## Copy Assessment
[Subject line verdicts, body grades, sequence architecture assessment]
---
## Lead Quality
[Targeting assessment, actual vs intended ICP]
---
## Recommendations (Prioritized)
### High Priority (Do This Week)
1. **[Action]** — [data point] → [expected impact]
### Medium Priority (Do This Month)
2. **[Action]** — [data point] → [expected impact]
### Kill List
- [Anything that should be stopped]Recommendation Logic
| Finding | Recommendation |
|---|---|
| Open rate below benchmark | Subject line rewrite — suggest 3 alternatives |
| Reply rate below + open rate fine | Body copy issue — focus on hook, proof, CTA |
| Both below benchmark | Full sequence rewrite |
| High "not relevant" objections | Targeting issue — tighten ICP filters |
| High "wrong person" referrals | Title targeting issue — shift to referred titles |
| High "already have solution" | Add competitive differentiation to copy |
| High "timing" objections | Not a problem — set up 90-day re-engagement |
| One variant clearly winning | Scale winner, test new idea in losing slot |
| Touch 2/3 near-zero marginal replies | Cut sequence short or rewrite with new angles |
| High bounce rate | List hygiene — verify emails, check data source |
| Deliverability <95% | Infrastructure — check SPF/DKIM/DMARC, reduce volume |
Human Checkpoint
Present the executive summary, then ask:
Full detailed report available. Want to see the full breakdown, or act on a specific recommendation?Adapting to Data Availability
| Missing Data | What Gets Skipped | Still Useful? |
|---|---|---|
| Reply text | Reply classification + objection patterns | Partially — metrics + copy still run |
| Variant data | Variant analysis | Yes — single-variant analysis still runs |
| Lead demographics | Targeting assessment | Yes — infers from reply patterns |
| Open tracking | Open rate analysis | Partially — reply rate + copy still run |
Minimum viable data: Emails sent + reply count + email copy text.
Cost
Free. Pure reasoning + data from user's outreach tool.
Tips
- Run at Day 7 and Day 14. Day 7 catches deliverability and subject line problems. Day 14 gives enough replies for objection analysis.
- Reply analysis is where the gold is. Metrics tell you WHAT. Replies tell you WHY.
- High open + low reply = copy problem. The subject gets them to open but the email doesn't deliver.
- Low open + decent reply rate = subject line problem. The email works, people just aren't seeing it.
- "Not relevant" is the most important objection. If >20% say "this isn't for me," it's targeting, not copy.
- Don't kill a variant too early. Need 100+ sends per variant for directional data.
- Touch 2/3 should contribute 30-40% of replies. If Touch 1 is 90%+, your follow-ups aren't adding value.
© gooseworks-ai, 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 1 other file in skills/outreach/composites/sequence-performance of gooseworks-ai/goose-skills.
- SKILL.md
- skill.meta.json
Open the folder on GitHubat commit c650c6d
Used in 1 other repository
We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in gooseworks-ai/goose-skills, which our catalogue first saw on October 9, 2026.
Compare with similar skills
Sequence Performance 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 |
|---|---|---|---|---|---|---|
| Sequence Performance this skillgooseworks-ai/goose-skills | 1.2k | 1 repos | ~3.3k | Automated safety check: Pass | MIT | |
| Social Performance Reviewstevenflanagan1/social-ai-team | 242 | — | ~3.7k | Automated safety check: Pass | None | |
| Model Calibration Curveaipoch/medical-research-skills | 2k | — | ~2.7k | Automated safety check: Pass | MIT | |
| Data Importindranilbanerjee/digital-marketing-pro | 859 | 1 repos | ~2.3k | Automated safety check: Pass | MIT | |
| Sealeap Feilian Amazon Buyer Segment Price Calibrationxjli360/sealeap-amazon-skills | 247 | — | ~816 | Automated safety check: Pass | MIT | |
| Sealeap Taotie Amazon Third Party Tool Data Calibrationxjli360/sealeap-amazon-skills | 247 | — | ~804 | Automated safety check: Pass | MIT |
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Questions about Sequence Performance
What does Sequence Performance do?
Email campaign/sequence performance review composite. An agent skill from gooseworks-ai/goose-skills. Sequence Performance is an agent skill from gooseworks-ai/goose-skills. Email campaign/sequence performance review composite.
When should I use Sequence Performance?
Sequence Performance fits situations like: tasks that involve GraphQL; tasks that involve Email marketing; tasks that involve Performance reviews.
How do I install Sequence Performance in Claude Code?
Run `npx skills add gooseworks-ai/goose-skills --skill sequence-performance -a claude-code`. Or copy the skill folder (skills/outreach/composites/sequence-performance in gooseworks-ai/goose-skills) into .claude/skills/sequence-performance in your project. Claude Code loads it when a task matches its description.
How do I install Sequence Performance in Codex?
Run `npx skills add gooseworks-ai/goose-skills --skill sequence-performance -a codex`. Or copy the skill folder (skills/outreach/composites/sequence-performance in gooseworks-ai/goose-skills) into .agents/skills/sequence-performance in your project. Codex loads it when a task matches its description.
Can I use Sequence Performance 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 gooseworks-ai/goose-skills --skill sequence-performance -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/sequence-performance, .gemini/skills/sequence-performance, .github/skills/sequence-performance and .opencode/skills/sequence-performance in your project.
What does Sequence Performance need to run?
SKILL.md names no scripts, command-line tools or credentials: Sequence Performance is instructions for the agent only.
Does Sequence Performance 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 Sequence Performance 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 Sequence Performance use?
Sequence Performance is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Sequence Performance use?
About 3.3k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Sequence Performance?
Skills that share tags, products or a category with Sequence Performance: Social Performance Review (stevenflanagan1/social-ai-team, 242 stars), Model Calibration Curve (aipoch/medical-research-skills, 2k stars), Data Import (indranilbanerjee/digital-marketing-pro, 859 stars) and Sealeap Feilian Amazon Buyer Segment Price Calibration (xjli360/sealeap-amazon-skills, 247 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Sequence Performance?
gooseworks-ai (a GitHub organization) maintains it in gooseworks-ai/goose-skills, which has 1,239 GitHub stars. The repository holds 273 skills in this directory. The repository was last updated on October 8, 2026.
Source: gooseworks-ai/goose-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.