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 S3YED/appie-kit --skill ibrahim-call-profilesgit clone --depth 1 https://github.com/S3YED/appie-kitWrote 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/s3yed/appie-kit/ibrahim-call-profiles)<a href="https://agentmods.dev/skills/s3yed/appie-kit/ibrahim-call-profiles"><img src="https://agentmods.dev/badge/skills/s3yed/appie-kit/ibrahim-call-profiles/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/s3yed/appie-kit/ibrahim-call-profiles"><img src="https://agentmods.dev/badge/skills/s3yed/appie-kit/ibrahim-call-profiles.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 | $0.00040 | $0.00720 |
| Opus 5 | $0.00020 | $0.00360 |
| Sonnet 5 | $0.00008 | $0.00144 |
| Haiku 4.5 | $0.00004 | $0.00072 |
Grade A, and why
ibrahim-call-profiles 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 8d 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 — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ibrahim Call Profile Builder
When to Use
Use when a triage call recording is uploaded or Ibrahim says he finished a call and shares notes. This builds a structured profile for the second call.
Pipeline
- Get the recording or notes
- Transcribe (if audio) using OpenAI Whisper or similar
- Extract structured profile
- Store in Cognee for future recall
- Deliver profile to Ibrahim as prep for second call
Profile Structure
Extract these fields from every triage call:
5-Point Lead Extraction (Value Delivery Format):
- PAIN: #1 pain in their own words — quote it directly from transcript
- CURRENT: Where they are now (weight, routine, situation)
- FUTURE: Where they want to be (goal weight, desired state, outcome)
- WHY NOW: What makes change urgent (trigger event, deadline, health scare)
- ALREADY SENT: What Ibrahim already sent them via WhatsApp (never repeat)
Lead Info:
- Name, location, age range
- Gender (for "brother/sister" usage)
- How they found Ibrahim
Pain Points:
- Primary physical pain point (what's wrong with their body)
- Secondary pain point
- Emotional driver (why this matters to them deep down)
- Quote their own words here
Current Routine:
- Training frequency and type
- Diet/nutrition approach
- Sleep and recovery
- Biggest struggle area
Commitment Signals:
- Red flags (excuses, vague answers, not ready)
- Green flags (specific goals, past success, ready to act)
- Overall readiness score: Low/Medium/High
The Angle for Call 2:
- What specific transformation would mean most to them
- What objection to overcome
- The hook that will get them to say yes
Second Call Strategy
The second call is framed as "next steps" — never as a sales pitch.
Opening: "You did the work. I saw your [specific progress/reference]. Let's talk about where you go from here."
The Offer Frame: Present coaching as the natural next step, not a hard sell. Use their own words from the triage call about what they want.
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.
- 8d ago First seen · 84 lines · 40 tokens per session scan A 21c3bc852d8e
ibrahim-call-profiles is a skill published in the GitHub repository S3YED/appie-kit (9 stars, last pushed 16d ago), licensed MIT. It adds 40 tokens to every session and 720 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
browser-edge-cases
SOP for debugging browser automation failures on complex websites. Use when browser tools fail on specific sites like LinkedIn, Twitter/X, SPAs, or sites with Shadow DOM.
aws-patterns
Lambda best practices, S3 event patterns, SQS/SNS fanout, and DynamoDB access patterns for serverless AWS architectures.
review
Review the changes since a fixed point (commit, branch, tag, or merge-base) along two axes — Standards (does the code follow this repo's documented coding standards?) and Spec (does the code match what the originating issue/PRD asked for?). Runs both reviews in parallel sub-agents and reports them side by side. Use…
agents-md-protocol
Create or review an AGENTS.md file so coding agents get stable repo-local instructions: environment setup, testing, style, security boundaries, PR policy, and handoff rules. Use when a repo lacks durable agent guidance or when a custom harness needs a predictable context file.
investment-memo-generator
Investment memo creation combining financial analysis, document generation, and structured templates. Use when creating investment memos, pitch decks, deal summaries, or investment committee materials.
python-memory-safe-scripts
Memory-safe Python script patterns for long-running processes under systemd MemoryMax constraints. Covers allocator purge (mimalloc/glibc malloctrim), HTTP response lifecycle, DataFrame cleanup, thread-local connection reuse, and periodic GC cadence. Battle-tested through 5 OOM optimization cycles on production GPU…