ida-domain-api
Analyze binaries using the Domain API for IDA Pro. Use when examining program structure, functions, disassembly, cross-references, or strings.
What this skill does
# Domain API for IDA Pro
Use this skill to efficiently use the Domain API for IDA Pro, which is easier and more concise than the legacy IDA Python SDK.
**Always prefer the IDA Domain API** over the legacy low-level IDA Python SDK. The Domain API provides a clean, Pythonic interface that is easier to use and understand.
### Documentation Resources
| Resource | URL |
|----------|-----|
| **LLM-optimized overview** | https://ida-domain.docs.hex-rays.com/llms.txt |
| **Getting Started** | https://ida-domain.docs.hex-rays.com/getting_started/index.md |
| **Examples** | https://ida-domain.docs.hex-rays.com/examples/index.md |
| **API Reference** | https://ida-domain.docs.hex-rays.com/ref/{module}/index.md |
Available API modules: `bytes`, `comments`, `database`, `entries`, `flowchart`, `functions`, `heads`, `hooks`, `instructions`, `names`, `operands`, `segments`, `signature_files`, `strings`, `types`, `xrefs`
**To fetch specific API documentation**, use URLs like:
- `https://ida-domain.docs.hex-rays.com/ref/functions/index.md` - Function analysis API
- `https://ida-domain.docs.hex-rays.com/ref/xrefs/index.md` - Cross-reference API
- `https://ida-domain.docs.hex-rays.com/ref/strings/index.md` - String analysis API
### Opening a Database
```python
from ida_domain import Database
from ida_domain.database import IdaCommandOptions
# Open an existing .i64/.idb
ida_options = IdaCommandOptions(auto_analysis=False, new_database=False)
with Database.open("path/to/sample.i64", ida_options, save_on_close=False) as db:
pass
# Analyze a raw input file and create a new database
ida_options = IdaCommandOptions(
auto_analysis=True,
new_database=True,
output_database="path/to/cache/sample.i64",
load_resources=True,
)
with Database.open("path/to/input.exe", ida_options, save_on_close=True) as db:
pass
```
### Commonly Used IdaCommandOptions
`IdaCommandOptions` maps directly to IDA command-line switches. Two options are especially common for headless analysis tools:
**`load_resources=True`** (maps to `-R`): loads MS Windows exe resources. Important for PE analysis that needs resource data.
**`plugin_options`** (maps to `-O`): pass options to IDA plugins. The most common use is **disabling Lumina** to prevent it from injecting bad or non-deterministic names:
```python
ida_options = IdaCommandOptions(
auto_analysis=True,
new_database=True,
output_database=str(cache_path),
load_resources=True,
plugin_options="lumina:host=0.0.0.0 -Osecondary_lumina:host=0.0.0.0",
)
```
This sets both primary and secondary Lumina server addresses to `0.0.0.0`, effectively disabling Lumina lookups. The `plugin_options` field is a raw string; `build_args()` emits `-O{value}` verbatim, so embedding `-O` inside the value for additional plugin options works correctly — the above produces `-Olumina:host=0.0.0.0 -Osecondary_lumina:host=0.0.0.0`.
Other useful fields: `processor` (`-p`), `log_file` (`-L`), `script_file` (`-S`), `db_compression` (`-P`). See the `IdaCommandOptions` docstring for the full list.
### Transparent Database Cache for Headless Tools
For repeatable CLI tools, do not re-analyze raw binaries every run. Prefer this pattern:
- if the input is already an `.i64` or `.idb`, use it directly
- otherwise hash the raw input and use the SHA-256 as the cache key
- create `<cache>/<sha256>.i64` on demand
- serialize access with a lock file and an extra `.nam` check, because another IDA process may have the database unpacked
- create the cached database with `save_on_close=True`
- reopen cached databases read-only with `save_on_close=False`
- after requesting auto-analysis, call `ida_auto.auto_wait()` before querying
```python
import contextlib
import fcntl
import hashlib
import os
import time
from pathlib import Path
import ida_auto
from ida_domain import Database
from ida_domain.database import IdaCommandOptions
DATABASE_POLL_INTERVAL = 0.25
DATABASE_ACCESS_TIMEOUT = 5.0
DATABASE_ANALYSIS_TIMEOUT = 120.0
def get_cache_dir() -> Path:
root = Path(os.environ.get("XDG_CACHE_HOME", Path.home() / ".cache"))
return root / "your-tool"
def compute_sha256(path: Path) -> str:
digest = hashlib.sha256()
with path.open("rb") as f:
for chunk in iter(lambda: f.read(65536), b""):
digest.update(chunk)
return digest.hexdigest()
def wait_for_repack(db_path: Path, timeout: float) -> None:
nam_path = db_path.with_suffix(".nam")
deadline = time.monotonic() + timeout
while nam_path.exists():
if time.monotonic() >= deadline:
raise RuntimeError(f"database appears busy: {db_path}")
time.sleep(DATABASE_POLL_INTERVAL)
@contextlib.contextmanager
def database_access_guard(db_path: Path, timeout: float):
wait_for_repack(db_path, timeout)
lock_path = Path(str(db_path) + ".lock")
lock_fd = lock_path.open("w")
deadline = time.monotonic() + timeout
try:
while True:
try:
fcntl.flock(lock_fd, fcntl.LOCK_EX | fcntl.LOCK_NB)
break
except OSError:
if time.monotonic() >= deadline:
raise RuntimeError(f"timed out waiting for lock: {db_path}")
time.sleep(DATABASE_POLL_INTERVAL)
wait_for_repack(db_path, max(0.0, deadline - time.monotonic()))
yield
finally:
fcntl.flock(lock_fd, fcntl.LOCK_UN)
lock_fd.close()
def resolve_database(file_path: Path) -> Path:
if file_path.suffix.lower() in {".i64", ".idb"}:
return file_path
cache_dir = get_cache_dir()
cache_dir.mkdir(parents=True, exist_ok=True)
cache_path = cache_dir / f"{compute_sha256(file_path)}.i64"
if cache_path.exists():
return cache_path
with database_access_guard(cache_path, DATABASE_ANALYSIS_TIMEOUT):
if cache_path.exists():
return cache_path
ida_options = IdaCommandOptions(
auto_analysis=True,
new_database=True,
output_database=str(cache_path),
load_resources=True,
)
with Database.open(str(file_path), ida_options, save_on_close=True):
ida_auto.auto_wait()
if not cache_path.exists():
raise RuntimeError(f"analysis did not create {cache_path}")
return cache_path
@contextlib.contextmanager
def open_database_session(db_path: Path, auto_analysis: bool = False):
with database_access_guard(db_path, DATABASE_ACCESS_TIMEOUT):
ida_options = IdaCommandOptions(auto_analysis=auto_analysis, new_database=False)
with Database.open(str(db_path), ida_options, save_on_close=False) as db:
if auto_analysis:
ida_auto.auto_wait()
yield db
```
Notes:
- keep cached database creation quiet in normal mode; only log cache paths in verbose/debug mode
- if you need a different cache policy, keep the same three-phase guard: wait for `.nam`, acquire `flock`, re-check `.nam`
- if you must fall back to `idapro.open_database(...)` for options not exposed by `ida-domain`, import your local project modules before `import idapro`, because `idapro` can mutate `sys.path`
- disable Lumina via `plugin_options="lumina:host=0.0.0.0 -Osecondary_lumina:host=0.0.0.0"` in `IdaCommandOptions` (see "Commonly Used IdaCommandOptions" above); no need to fall back to `idapro.open_database()` for this
### Key Database Properties
```python
with Database.open(path, ida_options) as db:
db.minimum_ea # Start address
db.maximum_ea # End address
db.metadata # Database metadata
db.architecture # Target architecture
db.functions # All functions (iterable)
db.strings # All strings (iterable)
db.segments # Memory segments
db.names # Symbols and labels
db.entries # Entry points
db.types # Type definitions
db.comments # All comments
db.xrefs # Cross-referenRelated in Backend & APIs
jfrog
IncludedInteract with the JFrog Platform via the JFrog CLI and REST/GraphQL APIs. Use this skill when the user wants to manage Artifactory repositories, upload or download artifacts, manage builds, configure permissions, manage users and groups, work with access tokens, configure JFrog CLI servers, search artifacts, manage properties, set up replication, manage JFrog Projects, run security audits or scans, look up CVE details, query exposures scan results from JFrog Advanced Security, manage release bundles and lifecycle operations, aggregate or export platform data, or perform any JFrog Platform administration task. Also use when the user mentions jf, jfrog, artifactory, xray, distribution, evidence, apptrust, onemodel, graphql, workers, mission control, curation, advanced security, exposures, or any JFrog product name.
cupynumeric-migration-readiness
IncludedPre-migration readiness assessor for porting NumPy to cuPyNumeric. Use BEFORE substantial porting work begins when the user asks whether code will scale on GPU, whether they should migrate to cuPyNumeric, which NumPy patterns transfer cleanly, what must be refactored before porting, or mentions pre-port assessment, scaling analysis, or refactor planning. Inspect the user's source code, look up NumPy usage, cross-reference the cuPyNumeric API support manifest, and distinguish distributed-scaling-friendly patterns from blockers such as unsupported APIs, scalar synchronization, host round-trips, Python/object-heavy control flow, shape/data-dependent branching, and in-place mutation hazards. Produce a verdict of READY, LIGHT REFACTOR, SIGNIFICANT REFACTOR, or NOT RECOMMENDED, with concrete refactor pointers.
alibabacloud-data-agent-skill
IncludedInvoke Alibaba Cloud Apsara Data Agent for Analytics via CLI to perform natural language-driven data analysis on enterprise databases. Data Agent for Analytics is an intelligent data analysis agent developed by Alibaba Cloud Database team for enterprise users. It automatically completes requirement analysis, data understanding, analysis insights, and report generation based on natural language descriptions. This tool supports: discovering data resources (instances/databases/tables) managed in DMS, initiating query or deep analysis sessions, real-time progress tracking, and retrieving analysis conclusions and generated reports. Use this Skill when users need to query databases, analyze data trends, generate data reports, ask questions in natural language, or mention "Data Agent", "data analysis", "database query", "SQL analysis", "data insights".
token-optimizer
IncludedReduce OpenClaw token usage and API costs through smart model routing, heartbeat optimization, budget tracking, and native 2026.2.15 features (session pruning, bootstrap size limits, cache TTL alignment). Use when token costs are high, API rate limits are being hit, or hosting multiple agents at scale. The 4 executable scripts (context_optimizer, model_router, heartbeat_optimizer, token_tracker) are local-only — no network requests, no subprocess calls, no system modifications. Reference files (PROVIDERS.md, config-patches.json) document optional multi-provider strategies that require external API keys and network access if you choose to use them. See SECURITY.md for full breakdown.
resend-cli
IncludedUse this skill when the task is specifically about operating Resend from an AI agent, terminal session, or CI job via the official resend CLI: installing/authenticating the CLI, sending/listing/updating/cancelling emails, batch sends, domains and DNS, webhooks and local listeners, inbound receiving, contacts, topics, segments, broadcasts, templates, API keys, profiles, or debugging Resend CLI/API failures. Trigger on mentions of Resend CLI, `resend`, `resend doctor`, `resend emails send`, `resend domains`, `resend webhooks listen`, `resend emails receiving`, or agent-friendly terminal automation.
alibabacloud-odps-maxframe-coding
IncludedUse this skill for MaxFrame SDK development and documentation navigation on Alibaba Cloud MaxCompute (ODPS). Helps answer MaxFrame API, concept, official example, and supported pandas API questions; create data processing programs; read/write MaxCompute tables; debug jobs (remote or local); and build custom DPE runtime images. Trigger when users mention MaxFrame, MaxCompute with MaxFrame, ODPS table processing, DPE runtime, MaxFrame docs/examples, DataFrame/Tensor operations, or GPU runtime setup. Works for both English and Chinese queries about Alibaba Cloud data processing with MaxFrame.