greydgl--pentestgpt
ab5fbb4d90
* first refactor
* feat: dockerized tool with persistent Claude+Codex login + multi-model benchmark
Run the autonomous CTF/pentest tool in Docker with a one-time, persistent login for
BOTH Claude Code and Codex, and add a multi-model benchmark harness.
Backend (multi-model):
- Add `--backend {claude,codex}` to the CTF pipeline. CodexBackend (pentestgpt/core/
backend.py) wraps unified_agent's Codex backend and translates its events into
AgentMessages, so the same pipeline runs on Claude (opus/sonnet) or Codex
(gpt-5.5/gpt-5.4-mini). Wired through config.backend, pipeline stage construction,
and the CLI (+ PENTESTGPT_CODEX_EFFORT; greppable [CODEX_USAGE] under PENTESTGPT_BENCH=1).
Docker tool (tool-only image; the benchmark stays OUTSIDE the image):
- Extend Dockerfile: Codex CLI (@openai/codex) + openai_codex SDK + unified_agent/
pentestgpt_agent/pentestgpt_legacy packages + gobuster/dirb + socat. Add .dockerignore
(keeps creds/benchmark/workspace out of the build context).
- Persistent dual login (the hard part) — asymmetric by token model:
* Claude: `setup-token` -> token stored in the pentestgpt-claude volume; entrypoint
exports CLAUDE_CODE_OAUTH_TOKEN (setup-token does not write .credentials.json; macOS
host creds live in the Keychain and can't be copied).
* Codex: the container does its OWN `codex login` (NOT seeding -- ChatGPT refresh tokens
are single-use, so a shared/copied login 401s on first refresh). The 127.0.0.1:1455
OAuth callback is forwarded into the container via a socat hop (-p 1455:8455).
* scripts/docker-login.sh is idempotent: checks logins live, logs in only the missing one(s).
- docker-compose codex-config volume (+ pinned names); entrypoint token-export + non-blocking
preflight; scripts/docker-auth-status.sh; Make targets (docker-build/login/auth-status/
run/shell/down/nuke).
- Verified end-to-end: one `make docker-login` -> a fresh container reports claude+codex
logged in with live round-trips; the CTF pipeline (Codex) captured a flag against an
isolated fixture and the pentest pipeline ran cleanly; persists across recreation, no re-login.
Benchmark (multi-model, host-side):
- benchmark/pilot/ harness (run_pilot.py + report.py): builds each xbow challenge, discovers
the loopback port, runs the pipeline across the 4 model combos, judges by the baked
FLAG{sha256(UPPER-dir)}, and renders REPORT.md (infra failures excluded from solve rates).
Includes the partial pilot's results (results.jsonl + REPORT.md).
Docs: docs/docker-dev-plan.md (full plan + implementation status); CLAUDE.md and README
docker quickstart; benchmark/pilot/README.md; design-doc roadmap (docs/redesign).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* fix: fail controller on backend error messages
* fix: allow listing sessions without target
* docs: add docker xbow benchmark report
* fix: infer concrete backend constructor type
* docs: refresh docker benchmark documentation
* feat(benchmark): add pure single-agent baseline + pipeline comparison
Add a "pure single agent" benchmark variant -- one bare `claude -p` /
`codex exec` call per target (no pipeline) -- to quantify what the 3-stage
PentestGPT pipeline buys over an un-orchestrated agent on the xbow targets.
- pentestgpt/prompts/stages.py: ctf_single_agent_{system,task}_prompt -- the
pipeline's shared fragments collapsed into ONE turn, so prompt content is
held constant and the only variable is the multi-stage decomposition.
- benchmark/pilot/run_docker_bench.py: docker-network runner
(--variant single|pipeline). Brings the target up, discovers the container's
internal IP+network (skips DB side-cars/ports), docker-runs the tool image on
that network, and scores the ground-truth flag against the agent's *assistant
text* only (parity with the pipeline's raw streaming). Reads stdout in chunks
to handle >64KB JSON lines. Resumable; --dry-run supported.
- benchmark/pilot/report_comparison.py -> DOCKER_COMPARISON.md: head-to-head
pipeline-vs-single per model on the common non-infra set.
- tests/unit/test_single_agent_prompt.py: prompt-builder coverage.
- docs: README, CLAUDE.md, benchmark README, DOCKER_REPORT updated.
Recorded result (10 medium/hard targets x 4 models, container-to-container,
same baseline image digest 0c4c0f3e..., commit dca0019 image):
Model Pipeline Single
Claude Opus 5/10 7/10 (single +2)
Claude Sonnet 6/10 4/10 (pipeline +2)
Codex gpt-5.5 7/10 7/10 (tie)
Codex gpt-5.4-mini 3/10 4/10 (single +1)
TOTAL 21/40 22/40
Single agent matches the pipeline on solve rate (55% vs 52%) while using
~40% fewer Codex tokens (13.0M vs 21.8M) and solving faster. The pipeline
only clearly helps Claude Sonnet (which times out solo); Opus is better solo.
Full per-challenge grid in DOCKER_COMPARISON.md; raw records in
docker_single_results.jsonl.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* feat(benchmark): add pentestgpt_agent docker harness
* bench: refresh pentestgpt_agent smoke result
* fix(benchmark): make repeat rows variant-aware
* fix(agent): fall back for semantic executor labels
* fix(agent): tolerate executor prose evidence
* fix(benchmark): score accepted framework findings
* bench: append partial framework repeat results
* bench: complete framework repeat sweep
* bench: expose framework executor concurrency
* bench: add extended parallel framework sweep
* checkpoint: preserve working agent and benchmark state
* feat: harden durable agent loop and xbow qualification
* fix: reserve an exploit result turn
* docs: record clean xbow qualification
* build: consume unified-agent from the git wrapper repo
Repoint pentestgpt_agent_new's unified-agent dependency from the local
editable path (../../UnifiedAgentPoC, now renamed and gone) to the pinned
git source PentestGPT-Project/UnifedAgentWrapper@d05d21f. Regenerate uv.lock
and update test_dependency.py to assert the external package is installed
from that VCS URL (not the repo-root vendored copy) at version 0.2.0.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* refactor: make pentestgpt_agent_new the sole framework
Remove the retired ledger-based pentestgpt_agent package (instructor/executor/
judge) and its orphaned unit + smoke tests. The nested pentestgpt_agent_new
project (Supervisor/Executor over a durable SQLite loop, consuming unified-agent
from the git wrapper) is now the single maintained framework.
Repoint the top-level tooling to it:
- pyproject: drop the pentestgpt-agent console script and pentestgpt_agent from
the wheel packages.
- Makefile: lint/format target parent code only; typecheck/check/ci now run the
nested framework's own gate (ruff, format, mypy, pytest) via test-agent-new /
check-agent-new, so `make check` finally covers it; `make run` delegates to the
pentestgpt-agent-new CLI.
- Dockerfile: stop copying the removed package (kept the build working); note the
framework is not baked into the image yet.
- docker container-health test: import the substrate packages that actually ship.
- CLAUDE.md / AGENT.md: describe the new framework, the git-sourced wrapper, and
the deprioritized benchmark/Docker rewire.
The XBOW `--variant framework` path and docker-bench Makefile targets still point
at the old in-image framework and are left as a pending rewire (benchmarks
deprioritized); the naive `--variant single` path is unaffected.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* refactor: rename pentestgpt_agent_new -> pentestgpt_agent
The framework reclaims the clean name now that the old ledger-based package is
gone. Rename the nested project folder, its src package, the distribution
(pentestgpt-agent-new -> pentestgpt-agent) and CLI, and every import/reference in
the package, the umbrella Makefile, the Dockerfile, the docker health test, and
CLAUDE.md / AGENT.md. Regenerate uv.lock. The audit CLI stays pentestgpt-agent-audit;
the git-sourced unified-agent dependency is unchanged. `make check` is green
(108 nested tests). The two historical *_REPORT.md files keep the old name as
dated records.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* chore: extract benchmark harness to sibling xbow-benchmark repo
Move PentestGPT/benchmark/ out to ../xbow-benchmark (its own repo) to keep this
project clean. The harness was decoupled from the framework code (it scores
container output, never imports pentestgpt_agent/unified_agent), so only
operational ties remain and they now live in the sibling repo.
- Remove benchmark/ and the 4 harness unit tests (relocated + repointed there).
- Strip the docker-bench-*/bench-* targets and their config vars from the
Makefile; keep the tool-image lifecycle (docker-build/login/run/...) and add a
help pointer to `make -C ../xbow-benchmark help`.
The sibling repo mounts this checkout read-only (--source-root ../PentestGPT) and
runs the pentestgpt:latest image built here.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* feat: harden autonomous framework and runtime integration
---------
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
166 行
5.7 KiB
Python
166 行
5.7 KiB
Python
"""Agent Skills (agentskills.io): one canonical skill dir, both agents discover it.
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Claude Code reads project skills from ``<ws>/.claude/skills/``; Codex reads the
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cross-agent location ``<ws>/.agents/skills/``. ``install_skills`` validates each
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skill against the open spec and links it into both, so a single SKILL.md serves
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both agents. Validation enforces the spec's shared baseline; ``lint_skill``
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flags Claude-only syntax that would degrade on Codex.
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"""
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from __future__ import annotations
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import re
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import shutil
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from dataclasses import dataclass, field
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from pathlib import Path
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import yaml
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from .types import SkillError
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NAME_RE = re.compile(r"^[a-z0-9]+(-[a-z0-9]+)*$")
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MAX_NAME_LEN = 64
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MAX_DESCRIPTION_LEN = 1024
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# Discovery roots inside a workspace, per host.
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CLAUDE_SKILLS_DIR = Path(".claude") / "skills"
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CODEX_SKILLS_DIR = Path(".agents") / "skills"
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# Claude-only constructs that Codex (and other agentskills hosts) ignore.
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_PORTABILITY_PATTERNS = [
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("$ARGUMENTS", "argument substitution ($ARGUMENTS/$1...) is Claude-only"),
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(
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"!`",
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"dynamic shell injection (!`cmd`) is Claude-only and runs before the model sees content",
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),
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("${CLAUDE_", "${CLAUDE_*} variables are Claude-only"),
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]
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@dataclass
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class Skill:
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name: str
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description: str
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path: Path
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body: str
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frontmatter: dict = field(default_factory=dict)
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def _parse_frontmatter(text: str, where: Path) -> tuple[dict, str]:
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if not text.startswith("---"):
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raise SkillError(f"{where}: SKILL.md must start with YAML frontmatter (--- ... ---)")
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end = text.find("\n---", 3)
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if end == -1:
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raise SkillError(f"{where}: unterminated YAML frontmatter")
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raw = text[3:end]
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body = text[end + 4 :].lstrip("\n")
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try:
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data = yaml.safe_load(raw) or {}
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except yaml.YAMLError as e:
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raise SkillError(f"{where}: invalid YAML frontmatter: {e}") from e
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if not isinstance(data, dict):
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raise SkillError(f"{where}: frontmatter must be a YAML mapping")
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return data, body
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def load_skill(skill_dir: Path) -> Skill:
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"""Load and validate one ``<skill-dir>/SKILL.md`` per the agentskills spec."""
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skill_dir = Path(skill_dir)
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skill_md = skill_dir / "SKILL.md"
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if not skill_md.is_file():
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raise SkillError(f"{skill_dir}: no SKILL.md")
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front, body = _parse_frontmatter(skill_md.read_text(encoding="utf-8"), skill_md)
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name = str(front.get("name") or "")
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description = str(front.get("description") or "")
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if not NAME_RE.match(name) or len(name) > MAX_NAME_LEN:
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raise SkillError(
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f"{skill_md}: invalid skill name {name!r} "
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f"(lowercase/digits/single-hyphens, <= {MAX_NAME_LEN} chars)"
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)
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if name != skill_dir.name:
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raise SkillError(
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f"{skill_md}: skill name {name!r} must match its directory name {skill_dir.name!r}"
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)
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if not description.strip():
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raise SkillError(f"{skill_md}: description is required")
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if len(description) > MAX_DESCRIPTION_LEN:
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raise SkillError(f"{skill_md}: description exceeds {MAX_DESCRIPTION_LEN} chars")
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return Skill(name=name, description=description, path=skill_dir, body=body, frontmatter=front)
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def lint_skill(skill: Skill) -> list[str]:
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"""Warnings for constructs that work in Claude Code but not in Codex."""
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warnings = []
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for needle, why in _PORTABILITY_PATTERNS:
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if needle in skill.body:
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warnings.append(f"{skill.name}: contains {needle!r} — {why}")
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return warnings
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def discover_skills(source_dir: Path) -> list[Skill]:
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source_dir = Path(source_dir)
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if not source_dir.is_dir():
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raise SkillError(f"skills source {source_dir} is not a directory")
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skills = []
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for child in sorted(source_dir.iterdir()):
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if child.is_dir() and (child / "SKILL.md").is_file():
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skills.append(load_skill(child))
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return skills
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def make_skill(parent: Path, name: str, description: str, body: str) -> Path:
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"""Write a minimal spec-valid skill (used by examples and tests)."""
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d = Path(parent) / name
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d.mkdir(parents=True, exist_ok=True)
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(d / "SKILL.md").write_text(
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f"---\nname: {name}\ndescription: {description}\n---\n\n{body}\n",
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encoding="utf-8",
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)
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return d
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def _install_one(skill: Skill, target_root: Path, mode: str, force: bool) -> None:
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target_root.mkdir(parents=True, exist_ok=True)
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target = target_root / skill.name
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source = skill.path.resolve()
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if target.is_symlink():
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if mode == "symlink" and target.resolve() == source:
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return # already correct
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target.unlink()
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elif target.exists():
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if not force:
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raise SkillError(
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f"{target} exists and is not a managed symlink; pass force=True to replace it"
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)
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shutil.rmtree(target)
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if mode == "symlink":
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target.symlink_to(source, target_is_directory=True)
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elif mode == "copy":
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shutil.copytree(source, target)
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else:
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raise SkillError(f"unknown install mode {mode!r} (use 'symlink' or 'copy')")
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def install_skills(
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workspace: Path,
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source_dir: Path,
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mode: str = "symlink",
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force: bool = False,
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) -> list[Skill]:
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"""Install every skill under ``source_dir`` into BOTH hosts' discovery dirs.
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Copy mode replaces previously-copied skills on reinstall (the target dirs
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under ``.claude/skills`` / ``.agents/skills`` are treated as managed).
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"""
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workspace = Path(workspace)
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skills = discover_skills(source_dir)
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if not skills:
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raise SkillError(f"no skills found under {source_dir}")
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for skill in skills:
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for root in (workspace / CLAUDE_SKILLS_DIR, workspace / CODEX_SKILLS_DIR):
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_install_one(skill, root, mode=mode, force=force or mode == "copy")
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return skills
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