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- Aditya232-rtx/Ouroboros
- 최근 소스 활동
- 2026년 8월 22일 22:16
- 감지된 SKILL.md 언어
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설치 방법
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
소스 파일 검토
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
메뉴
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/Aditya232-rtx/Ouroboros --skill actual-setup명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
로컬 사본을 원하시나요? SkillsMP에서 현재 제공할 수 있는 파일을 다운로드하세요.
Add security scanning to CI/CD with Fang — GitHub Actions, GitLab CI, or any pipeline — so every pull request gets a diff-scoped AI pentest that blocks vulnerable code before it merges, with results as PR comments and SARIF uploaded to code scanning. Covers both the self-hosted open-source CLI (runs in your runner) and the managed app.fang.ai platform (GitHub/GitLab app or API, no runner infra). Use when the user asks to add security scanning, SAST/DAST, pentesting, vulnerability checks, or automated security review to their CI pipeline, pre-merge gate, or PR workflow.
Fix security vulnerabilities found by a Fang pentest (open-source CLI or app.fang.ai cloud) — triage by severity, patch the root cause rather than the symptom, and re-run Fang to prove each fix actually closes the exploit. Handles injection, XSS, SSRF, broken access control, IDOR, and other validated findings. Use after a Fang scan reports findings, or when the user asks to remediate, patch, or fix security issues from a fang_runs report, vulnerabilities.json, findings.sarif, or a cloud scan.
Run a managed pentest of a web app or API through the app.fang.ai REST API — no local Docker, LLM key, or install needed. Create an API token, register domain/repository assets, launch and poll scans, triage vulnerabilities, export SARIF, download PDF/DOCX pentest reports for SOC 2 and other compliance evidence (Enterprise plan), start PR reviews, and set up schedules and webhooks. Use when the user wants continuous or scheduled pentesting-as-a-service, an auditor-ready pentest report, scans tracked in a team dashboard, or security testing from a sandboxed agent/CI environment with no infrastructure.
SKILL.md 표시 중
| name | actual-setup |
| description | Set up Actual Computer (actual.inc) inference in Ouro. |
| version | 2.0.0 |
| author | shl0ms + Ouro |
| license | MIT |
| platforms | ["linux","macos","windows"] |
| metadata | {"hermes":{"tags":["actual","actual-inc","provider","local-inference","relay","gguf","setup"],"category":"devops"}} |
Sets up actual.inc (Actual Computer) as a Ouro inference
provider. Actual turns the user's own hardware into a private inference cluster
and exposes an OpenAI-compatible API two ways: a hosted end-to-end-encrypted
relay at https://api.actual.inc (authenticated with an ac_ key), and a local
on-device daemon at http://127.0.0.1:8080 (no auth on loopback). This skill
does not install the Actual daemon for the user — device authorization requires
a human in a browser.
ac_ key and wants Ouro routed through their Actual cluster.actual provider support (provider id actual,
aliases actual-computer, actualcomputer, aci). Do NOT configure Actual
as a custom_providers / providers.actual.* entry on current Ouro — the
built-in provider owns the name and handles base-url normalization, the
Responses transport, and local no-auth automatically.ac_ inference key from
https://actual.inc/user/keys.curl -fsSL "https://actual.inc/install" | bash) and completed device
authorization by running actual once and opening the printed
https://actual.inc/device?code=... URL in a browser. Relay that URL to the
user and WAIT — never invent an email or authorize on their behalf. Codes
expire in 5 minutes; re-run actual for a fresh one..env (secrets only — never config.yaml):
append ACTUAL_API_KEY=ac_... to ~/.ouro/.env.terminal:
curl -s https://api.actual.inc/v1/models -H "Authorization: Bearer $ACTUAL_API_KEY"
ouro config set model.provider actual
ouro config set model.default "MODEL_ID_FROM_DISCOVERY"
ouro chat -Q -q "Reply with exactly: ACTUAL_OK" --provider actual -m MODEL_ID
actual models search "qwen2.5 0.5b instruct gguf" --limit 8 --no-prompt
# Downloads REQUIRE an explicit quantization (409 ambiguous_model_download otherwise):
actual models download "Qwen/Qwen2.5-0.5B-Instruct-GGUF/Q4_K_M"
actual models list # note the INSTALLED name (differs from download id)
actual models load "qwen2.5-0.5b-instruct-q4_k_m" # load by installed name
ACTUAL_BASE_URL with a loopback host flips the
built-in provider into local no-auth mode automatically — no key needed:
append ACTUAL_BASE_URL=http://127.0.0.1:8080 to ~/.ouro/.env, then:
ouro config set model.provider actual
ouro config set model.default "INSTALLED_MODEL_NAME"
ouro chat -Q -q "Reply with exactly: LOCAL_OK" --provider actual -m INSTALLED_NAME -t file,web
| Thing | Value |
|---|---|
| Hosted relay | https://api.actual.inc/v1 (normalized from bare host automatically) |
| Local daemon | http://127.0.0.1:8080/v1 (no auth on loopback) |
| Key env var | ACTUAL_API_KEY (ac_...) |
| Base URL env var | ACTUAL_BASE_URL (loopback host ⇒ local no-auth mode) |
| Provider id / aliases | actual / actual-computer, actualcomputer, aci |
| Transport | Responses API (codex_responses) — built-in, do not override |
| Cluster pinning | X-Cluster-ID header via providers.actual.extra_headers in config.yaml |
| Model size guide | 0.5B Q4_K_M ~470MB (toy), 7-8B Q4_K_M ~4.5GB (daily driver), 32B ~20GB |
none/low/medium/high/max;
xhigh/ultra used to fail with a cryptic
Expecting value: line 1 column 1 (char 0) (a wrapped HTTP 400). The
built-in provider clamps xhigh→high and ultra→max on the wire. If a
request still 400s this way on an old Ouro, set a per-model cap:
agent.reasoning_overrides.<model>: high in config.yaml.data: [DONE] — Ouro reports
Provider returned an empty stream with no finish_reason. This is NOT an
SSE bug. Fixes: restrict tools (-t file,web), load the model with a
larger n_ctx, or pick a >=64k-context model for the full toolset.
Upstream tracking: #51448 (do not file new issues; add evidence there).
Related but distinct: #65631 (HTTP-200 SSE carrying a 400), #56516
(reasoning-only streams).actual models download takes
repo/QUANT and 409s without an explicit quantization;
actual models load takes the INSTALLED name from actual models list.reasoning field and can burn a small
max_tokens entirely on reasoning. Give generous max_tokens before
assuming failure.actual. Older setup guides
(pre first-class support) wrote providers.actual.* config blocks. On
current Ouro the built-in provider wins the name; stale custom blocks
are ignored or conflict. Remove them and use the env vars + model.provider
flow above.# Relay:
ouro chat -Q -q "Reply with exactly: ACTUAL_OK" --provider actual -m MODEL
# Local (small model — reduced toolset):
ouro chat -Q -q "Reply with exactly: LOCAL_OK" --provider actual -m MODEL -t file,web
# Provider status (local no-auth shows key_source=local-offline):
ouro status
For other OpenAI-compatible clients (e.g. OpenCode), see
references/opencode.md.