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 flaqai/ios-application-development-skills --skill ios-memgraph-leaksgit clone --depth 1 https://github.com/flaqai/ios-application-development-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/flaqai/ios-application-development-skills/ios-memgraph-leaks)<a href="https://agentmods.dev/skills/flaqai/ios-application-development-skills/ios-memgraph-leaks"><img src="https://agentmods.dev/badge/skills/flaqai/ios-application-development-skills/ios-memgraph-leaks/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/flaqai/ios-application-development-skills/ios-memgraph-leaks"><img src="https://agentmods.dev/badge/skills/flaqai/ios-application-development-skills/ios-memgraph-leaks.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 | $0.00036 | $0.00899 |
| Opus 5 | $0.00018 | $0.00449 |
| Sonnet 5 | $0.00007 | $0.00180 |
| Haiku 4.5 | $0.00004 | $0.00090 |
Grade A, and why
ios-memgraph-leaks 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 10d 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.
This is a copy
100% identical to ios-memgraph-leaks — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
iOS Memgraph Leaks
Use this skill to prove iOS leaks from a live simulator process or an existing .memgraph. Pair it with ../ios-debugger-agent/SKILL.md when the task also needs simulator build, install, launch, UI driving, logs, or screenshots.
Core Workflow
- Build, launch, and drive the exact flow that should release objects.
- Capture a memgraph from the running simulator process with
scripts/capture_sim_memgraph.sh. - Summarize leaks with
scripts/summarize_memgraph_leaks.py. - For each app-owned leaked type, inspect ownership with
leaks --traceTree=<address> <file.memgraph>and grouped leak evidence. - Make the smallest root-cause patch, then recapture the same flow on the same simulator when possible.
- Report proof: before/after leak counts, disappeared root types, remaining leaks, memgraph paths, and test/build results.
Do not claim a leak fix from a smaller memgraph alone. A credible fix explains the ownership path that kept the object alive and shows that the same path or type disappears after the patch.
Capture
Prefer capturing from the simulator already used for the reproduction. Resolve the simulator UDID and app bundle identifier, then capture the running app:
SKILL_DIR="<absolute path to this loaded skill folder>"
SIM="<simulator-udid>"
BUNDLE_ID="<app.bundle.identifier>"
MEMGRAPH_DIR="$(mktemp -d "${TMPDIR:-/tmp}/codex-ios-memgraph.XXXXXX")"
"$SKILL_DIR/scripts/capture_sim_memgraph.sh" \
--udid "$SIM" \
--bundle-id "$BUNDLE_ID" \
--out-dir "$MEMGRAPH_DIR"
Do not derive SKILL_DIR from the target app repo's pwd; installed plugins usually live outside the app being debugged. Store captures in a run-specific temp or user-chosen folder, not under SKILL_DIR.
If the process cannot be found, confirm the bundle identifier and use xcrun simctl spawn "$SIM" launchctl list to inspect running labels.
Summarize
Summarize an existing memgraph:
"$SKILL_DIR/scripts/summarize_memgraph_leaks.py" \
/path/to/app.memgraph \
--trace-limit 5 \
--out /path/to/leak-summary.md
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 10d ago First seen · 77 lines · 36 tokens per session scan A f09a1e78c00b
ios-memgraph-leaks is a skill published in the GitHub repository flaqai/ios-application-development-skills (4 stars, last pushed 27d ago), licensed MIT. It adds 36 tokens to every session and 899 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to ios-memgraph-leaks, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
mobile-debugging
Remote JavaScript console and debugging on mobile. Use for phone/tablet console errors, responsive testing, Eruda, and vConsole.
dredge
When something goes wrong during a build — including when the report can't be reproduced at all — diagnose what type of failure it is before deciding how to respond. Targeted fix, hard reset, full rethink, or a reproduction gap — the right response depends on the right diagnosis.
compose-ui-builder
Author and edit a Compose UI design over MCP against a compose-preview serve deployment — create a screen or a Wear widget, insert and edit nodes, and export the Kotlin, PNG or SVG. Use when asked to build, change, or review a UI Builder design (a URL like /ui-builder/ / ), to turn a design into Compose code, or to…
prove-checks
Prove a passing check was capable of failing before recording it as evidence. Use when a test, CI job, build-and-diff, smoke test or rehearsal comes back green and that green is about to be treated as proof - especially when the check depends on a setup mutation (a sed/awk rewrite, an env var, a secret, a fixture…
python-performance-optimization
Profile and optimize Python code using cProfile, memory profilers, and performance best practices.
logging-patterns
Logging conventions -- level usage, formatting style, structured output.