skvm-general: Workflow Automation for AI Agents | OpenAgentSkill

Overview

Drive the skvm CLI on behalf of a user to profile models, AOT-compile skills, run skill-assisted tasks, run benchmarks, and manage compiled proposals. Trigger when the user asks to "profile", "aot-compile", "bench", "run a single ad-hoc task with a skill", or asks about skvm proposals. Do NOT trigger for `jit-optimize` or when the user wants to optimize/improve a skill — use the sibling `skvm-jit` skill instead.

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SkVM General Usage

You are driving skvm, a CLI that AOT-compiles and runs LLM agent skills across heterogeneous models. Use this skill when the user wants to use skvm — profile a model, AOT-compile a skill, run a task with a skill, run a benchmark, or manage optimization proposals. Do not invent flags — every example below uses the real flag set from the installed skvm binary.

Step 1: Prerequisite self-check

Split the check in two — the binary must always be present, but the API key is only required for commands that call an LLM.

Always required — skvm is on PATH:

skvm --help >/dev/null 2>&1 || { echo "skvm not installed — tell the user to run: curl -fsSL https://skillvm.ai/install.sh | sh"; exit 1; }

Required only before LLM-calling commands — profile (without --list), aot-compile, pipeline, run, bench, jit-optimize. Local filesystem commands (profile --list, proposals list|show|reject, logs, clean-jit) do not need the API key — run them even if the key is unset.

# Before running profile/aot-compile/run/bench/jit-optimize:
test -n "${OPENROUTER_API_KEY:-}" || { echo "OPENROUTER_API_KEY is not set — ask the user for their key"; exit 1; }

If a required prerequisite is missing, stop and tell the user what is missing. Do not install anything yourself.

Step 2: Profile a model

A profile (TCP — Target Capability Profile) records what an LLM can do across 26 primitive capabilities. It is the input for AOT compilation and is cached so subsequent compile calls reuse it.

skvm profile --model=<id>                              # profile one model
skvm profile --model=<id1>,<id2> --concurrency=4       # profile several in parallel
skvm profile --model=<id> --adapter=opencode           # non-default adapter
skvm profile --model=<id> --force                      # ignore cache, re-run
skvm profile --list                                    # list cached profiles

Notes:

Default adapter is bare-agent. Other valid adapters: opencode, openclaw, hermes, jiuwenclaw.
Cache lives at $SKVM_PROFILES_DIR (default .skvm/profiles/).
Profiling is expensive — confirm with the user before running on several models, and prefer --concurrency over sequential runs.

Step 3: AOT-compile a skill

AOT compilation rewrites a skill's SKILL.md (and optionally bundle files) so it fits a specific target model's capability profile. The three-pass AOT compiler runs by default.

skvm aot-compile --skill=<path> --model=<id>                       # all three passes
skvm aot-compile --skill=<path> --model=<id> --pass=1,2,3          # explicit
skvm aot-compile --skill=<path> --model=<id> --pass=1              # only pass 1 (SCR + gap analysis)
skvm aot-compile --skill=<path> --model=<id> --dry-run             # no write
skvm pipeline --skill=<path> --model=<id>                          # profile-if-needed → aot-compile

Pass semantics:

--pass=1 — SCR extraction, gap analysis, capability substitution/compensation
--pass=2 — dependency manifest + env-binding script generation
--pass=3 — workflow decomposition + DAG parallelism extraction

Compiled variants land under the proposals tree (proposals/aot-compile/...). Multiple passes can be combined in any subset: --pass=1,3 runs passes 1 and 3, skipping 2.

Step 4: Run a single task with a skill

For ad-hoc debugging of one skill on one task:

skvm run --task=<path/to/task.json> --model=<id>                    # no skill
skvm run --task=<path> --model=<id> --skill=<path/to/SKILL.md>      # with skill
skvm run --task=<path> --model=<id> --adapter=opencode --verbose    # explicit adapter + debug

Use this to reproduce a single failing task or validate a skill edit. Do not use it for benchmarking — use skvm bench instead.

Step 5: Bench a skill

Benchmarking runs a skill across many tasks and condition variants. It can get expensive fast — always confirm with the user before running across many models or tasks, and use --concurrency for parallelism.

skvm bench --model=<id>                                              # all conditions, all tasks
skvm bench --model=<id> --conditions=original,aot-compiled           # baseline + compiled
skvm bench --model=<id> --conditions=jit-optimized                   # use latest jit-optimize best round
skvm bench --model=<id> --conditions=jit-boost --jit-runs=5          # 5 warmup runs for solidification
skvm bench --model=<id1>,<id2> --concurrency=4                       # multi-model in parallel
skvm bench --model=<id> --tasks=task_01,task_02 --runs-per-task=3    # specific tasks, 3 reps each
skvm bench --model=<id> --async-judge                                # defer LLM-judge to post-run batch
skvm bench --resume=latest                                           # resume an interrupted session
skvm bench --list-sessions                                           # list past sessions

Valid --conditions strings:

no-skill — run the task with no skill injected (baseline floor)
original — the skill as-written (baseline ceiling)
aot-compiled — full 3-pass AOT compiled variant
aot-compiled-p1, -p2, -p3, -p12, -p13, -p23 — single or partial AOT passes
jit-optimized — the latest best-round variant from skvm jit-optimize proposals
jit-boost — code-solidification runtime optimization

Bench logs land at .skvm/log/bench/<sessionId>/.

Step 6: Manage jit-optimize proposals

Proposals are the artifact produced by skvm jit-optimize (and by the sibling skvm-jit skill). Each proposal contains the original skill in round-0/, one or more improved rounds in round-N/, and metadata recording which round the engine considered best.

skvm proposals list                                           # all proposals
skvm proposals list --status=pending                          # only pending (not yet accepted/rejected)
skvm proposals list --skill=<name> --target-model=<id>        # filter by skill + target model
skvm proposals show <id>                                      # print metadata and per-round summary
skvm proposals accept <id>                                    # deploy the engine-recommended best round
skvm proposals accept <id> --round=2                          # override: deploy round 2 instead
skvm proposals accept <id> --target=<dir>                     # deploy to a non-default skill dir
skvm proposals reject <id>                                    # mark as rejected (no deploy)
skvm proposals cancel <id>                                    # stop a detached run still in phase=running

Proposal id format: <harness>/<safe-target-model>/<skill-name>/<timestamp>, where <safe-target-model> is the slugified target model id (forward slashes in the CLI id become --). When the user gives you an id like bare-agent/openrouter--anthropic--claude-sonnet-4.6/calendar/20260401T120000Z, pass it verbatim — do not reformat it.

Detached runs (skvm jit-optimize --detach) write an extra run-status.json inside the proposal directory that tracks execution phase (running / done / failed), separate from meta.json.status. skvm proposals show renders this header and tails the last 20 lines of run.log for running / failed detached runs. If the user wants to stop a detached optimization mid-run, use cancel; sync runs (no --detach) do not need cancel, they block until complete.

Critical rule: only run skvm proposals accept when the user has explicitly asked to deploy. If the user just says "check the proposals", run list and show and stop there. Accepting without confirmation overwrites the skill files in place.

Step 7: Environment variables

Variable	Required	Purpose
`OPENROUTER_API_KEY`	yes	OpenRouter key used by bare-agent, profiler, compiler (when routed through OpenRouter), and jit-optimize optimizer
`ANTHROPIC_API_KEY`	optional	Enables the Anthropic SDK backend for the compiler and judge
`SKVM_DATA_DIR`	optional	Override the input dataset root (default: `./skvm-data` submodule)
`SKVM_CACHE`	optional	Override the runtime cache root (default: `~/.skvm`)
`SKVM_PROPOSALS_DIR`	optional	Override the proposals storage root (default: `~/.skvm/proposals/`)

OPENROUTER_API_KEY is only required for commands that actually call an LLM. Local-only commands (proposals list/show/reject, profile --list, logs, clean-jit) run without it.

Rules

Never run bench or profile across many models without explicit user confirmation — they can cost tens of dollars per run. Always quote an expected model count back to the user before starting.
Never run skvm proposals accept unless the user explicitly asked to deploy.
Prefer --concurrency=<n> over sequential loops for multi-model work.
Do not invent flags. If the user asks for something you don't see in this skill, run skvm <command> --help to check before guessing.
Do not install anything. If skvm is missing, tell the user to run the installer; if OPENROUTER_API_KEY is missing, ask them for it.
Surface skvm's stderr progress lines (e.g. Installing bundled opencode…, Downloading profile…) as normal output — they are not errors.

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File metadata

name: skvm-general
description: Drive the skvm CLI on behalf of a user to profile models, AOT-compile skills, run skill-assisted tasks, run benchmarks, and manage compiled proposals. Trigger when the user asks to "profile", "aot-compile", "bench", "run a single ad-hoc task with a skill", or asks about skvm proposals. Do NOT trigger for `jit-optimize` or when the user wants to optimize/improve a skill — use the sibling `skvm-jit` skill instead.

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---
name: skvm-general
description: Drive the skvm CLI on behalf of a user to profile models, AOT-compile skills, run skill-assisted tasks, run benchmarks, and manage compiled proposals. Trigger when the user asks to "profile", "aot-compile", "bench", "run a single ad-hoc task with a skill", or asks about skvm proposals. Do NOT trigger for `jit-optimize` or when the user wants to optimize/improve a skill — use the sibling `skvm-jit` skill instead.
---

# SkVM General Usage

You are driving `skvm`, a CLI that AOT-compiles and runs LLM agent skills across heterogeneous models. Use this skill when the user wants to *use* skvm — profile a model, AOT-compile a skill, run a task with a skill, run a benchmark, or manage optimization proposals. Do **not** invent flags — every example below uses the real flag set from the installed `skvm` binary.

## Step 1: Prerequisite self-check

Split the check in two — the binary must always be present, but the API key is only required for commands that call an LLM.

**Always required** — skvm is on PATH:

```bash
skvm --help >/dev/null 2>&1 || { echo "skvm not installed — tell the user to run: curl -fsSL https://skillvm.ai/install.sh | sh"; exit 1; }
```

**Required only before LLM-calling commands** — `profile` (without `--list`), `aot-compile`, `pipeline`, `run`, `bench`, `jit-optimize`. Local filesystem commands (`profile --list`, `proposals list|show|reject`, `logs`, `clean-jit`) do **not** need the API key — run them even if the key is unset.

```bash
# Before running profile/aot-compile/run/bench/jit-optimize:
test -n "${OPENROUTER_API_KEY:-}" || { echo "OPENROUTER_API_KEY is not set — ask the user for their key"; exit 1; }
```

If a required prerequisite is missing, **stop** and tell the user what is missing. Do not install anything yourself.

## Step 2: Profile a model

A profile (TCP — Target Capability Profile) records what an LLM can do across 26 primitive capabilities. It is the input for AOT compilation and is cached so subsequent compile calls reuse it.

```bash
skvm profile --model=<id>                              # profile one model
skvm profile --model=<id1>,<id2> --concurrency=4       # profile several in parallel
skvm profile --model=<id> --adapter=opencode           # non-default adapter
skvm profile --model=<id> --force                      # ignore cache, re-run
skvm profile --list                                    # list cached profiles
```

Notes:
- Default adapter is `bare-agent`. Other valid adapters: `opencode`, `openclaw`, `hermes`, `jiuwenclaw`.
- Cache lives at `$SKVM_PROFILES_DIR` (default `.skvm/profiles/`).
- Profiling is expensive — confirm with the user before running on several models, and prefer `--concurrency` over sequential runs.

## Step 3: AOT-compile a skill

AOT compilation rewrites a skill's `SKILL.md` (and optionally bundle files) so it fits a specific target model's capability profile. The three-pass AOT compiler runs by default.

```bash
skvm aot-compile --skill=<path> --model=<id>                       # all three passes
skvm aot-compile --skill=<path> --model=<id> --pass=1,2,3          # explicit
skvm aot-compile --skill=<path> --model=<id> --pass=1              # only pass 1 (SCR + gap analysis)
skvm aot-compile --skill=<path> --model=<id> --dry-run             # no write
skvm pipeline --skill=<path> --model=<id>                          # profile-if-needed → aot-compile
```

Pass semantics:
- `--pass=1` — SCR extraction, gap analysis, capability substitution/compensation
- `--pass=2` — dependency manifest + env-binding script generation
- `--pass=3` — workflow decomposition + DAG parallelism extraction

Compiled variants land under the proposals tree (`proposals/aot-compile/...`). Multiple passes can be combined in any subset: `--pass=1,3` runs passes 1 and 3, skipping 2.

## Step 4: Run a single task with a skill

For ad-hoc debugging of one skill on one task:

```bash
skvm run --task=<path/to/task.json> --model=<id>                    # no skill
skvm run --task=<path> --model=<id> --skill=<path/to/SKILL.md>      # with skill
skvm run --task=<path> --model=<id> --adapter=opencode --verbose    # explicit adapter + debug
```

Use this to reproduce a single failing task or validate a skill edit. Do **not** use it for benchmarking — use `skvm bench` instead.

## Step 5: Bench a skill

Benchmarking runs a skill across many tasks and condition variants. It can get expensive fast — always confirm with the user before running across many models or tasks, and use `--concurrency` for parallelism.

```bash
skvm bench --model=<id>                                              # all conditions, all tasks
skvm bench --model=<id> --conditions=original,aot-compiled           # baseline + compiled
skvm bench --model=<id> --conditions=jit-optimized                   # use latest jit-optimize best round
skvm bench --model=<id> --conditions=jit-boost --jit-runs=5          # 5 warmup runs for solidification
skvm bench --model=<id1>,<id2> --concurrency=4                       # multi-model in parallel
skvm bench --model=<id> --tasks=task_01,task_02 --runs-per-task=3    # specific tasks, 3 reps each
skvm bench --model=<id> --async-judge                                # defer LLM-judge to post-run batch
skvm bench --resume=latest                                           # resume an interrupted session
skvm bench --list-sessions                                           # list past sessions
```

Valid `--conditions` strings:
- `no-skill` — run the task with no skill injected (baseline floor)
- `original` — the skill as-written (baseline ceiling)
- `aot-compiled` — full 3-pass AOT compiled variant
- `aot-compiled-p1`, `-p2`, `-p3`, `-p12`, `-p13`, `-p23` — single or partial AOT passes
- `jit-optimized` — the latest best-round variant from `skvm jit-optimize` proposals
- `jit-boost` — code-solidification runtime optimization

Bench logs land at `.skvm/log/bench/<sessionId>/`.

## Step 6: Manage jit-optimize proposals

Proposals are the artifact produced by `skvm jit-optimize` (and by the sibling `skvm-jit` skill). Each proposal contains the original skill in `round-0/`, one or more improved rounds in `round-N/`, and metadata recording which round the engine considered best.

```bash
skvm proposals list                                           # all proposals
skvm proposals list --status=pending                          # only pending (not yet accepted/rejected)
skvm proposals list --skill=<name> --target-model=<id>        # filter by skill + target model
skvm proposals show <id>                                      # print metadata and per-round summary
skvm proposals accept <id>                                    # deploy the engine-recommended best round
skvm proposals accept <id> --round=2                          # override: deploy round 2 instead
skvm proposals accept <id> --target=<dir>                     # deploy to a non-default skill dir
skvm proposals reject <id>                                    # mark as rejected (no deploy)
skvm proposals cancel <id>                                    # stop a detached run still in phase=running
```

Proposal id format: `<harness>/<safe-target-model>/<skill-name>/<timestamp>`, where `<safe-target-model>` is the slugified target model id (forward slashes in the CLI id become `--`). When the user gives you an id like `bare-agent/openrouter--anthropic--claude-sonnet-4.6/calendar/20260401T120000Z`, pass it verbatim — do not reformat it.

Detached runs (`skvm jit-optimize --detach`) write an extra `run-status.json` inside the proposal directory that tracks execution phase (`running` / `done` / `failed`), separate from `meta.json.status`. `skvm proposals show` renders this header and tails the last 20 lines of `run.log` for running / failed detached runs. If the user wants to stop a detached optimization mid-run, use `cancel`; sync runs (no `--detach`) do not need `cancel`, they block until complete.

**Critical rule**: only run `skvm proposals accept` when the user has **explicitly** asked to deploy. If the user just says "check the proposals", run `list` and `show` and stop there. Accepting without confirmation overwrites the skill files in place.

## Step 7: Environment variables

| Variable | Required | Purpose |
|----------|----------|---------|
| `OPENROUTER_API_KEY` | yes | OpenRouter key used by bare-agent, profiler, compiler (when routed through OpenRouter), and jit-optimize optimizer |
| `ANTHROPIC_API_KEY` | optional | Enables the Anthropic SDK backend for the compiler and judge |
| `SKVM_DATA_DIR` | optional | Override the input dataset root (default: `./skvm-data` submodule) |
| `SKVM_CACHE` | optional | Override the runtime cache root (default: `~/.skvm`) |
| `SKVM_PROPOSALS_DIR` | optional | Override the proposals storage root (default: `~/.skvm/proposals/`) |

`OPENROUTER_API_KEY` is only required for commands that actually call an LLM. Local-only commands (`proposals list/show/reject`, `profile --list`, `logs`, `clean-jit`) run without it.

## Rules

- **Never run `bench` or `profile` across many models without explicit user confirmation** — they can cost tens of dollars per run. Always quote an expected model count back to the user before starting.
- **Never run `skvm proposals accept` unless the user explicitly asked to deploy.**
- **Prefer `--concurrency=<n>` over sequential loops** for multi-model work.
- **Do not invent flags.** If the user asks for something you don't see in this skill, run `skvm <command> --help` to check before guessing.
- **Do not install anything.** If `skvm` is missing, tell the user to run the installer; if `OPENROUTER_API_KEY` is missing, ask them for it.
- **Surface skvm's stderr progress lines** (e.g. `Installing bundled opencode…`, `Downloading profile…`) as normal output — they are not errors.

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The description says 'Do NOT trigger for jit-optimize' but the skill includes jit-optimize in the prerequisite check and mentions it in bench conditions, which could cause confusion.
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Indexed

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Source repository: SJTU-IPADS/SkVM
License: MIT
Version: 1.0.0
Last GitHub push: Aug 28, 2026
Registry updated: Sep 5, 2026
Instruction path: skills/skvm-general/SKILL.md @ f33b48ef8243

Version reported in registry metadata; check source releases before relying on it.

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    "install_policy": "block",
    "evidence": {
      "stars": "552 GitHub stars",
      "repoActivity": "552 stars, 54 forks",
      "lastPushed": "20d since push",
      "license": "MIT",
      "repository": "https://github.com/SJTU-IPADS/SkVM/tree/main/skills/skvm-general",
      "install": "npx skills add SJTU-IPADS/SkVM --skill skvm-general",
      "installSafety": "standard package or runtime install path",
      "permissionSurface": "secrets or environment access, shell or command execution",
      "documentation": "Strong README/SKILL.md context",
      "agentOutcomes": "No agent outcome data yet"
    },
    "outcome_evidence": {
      "total": 0,
      "successes": 0,
      "failures": 0,
      "not_relevant": 0,
      "success_rate": null,
      "recent_success_rate": null,
      "recent_failure_rate": null,
      "install_attempts": 0,
      "install_success_rate": null,
      "risk_blocked": 0,
      "setup_required": 0,
      "avg_output_quality": null,
      "production_outcomes": 0,
      "last_outcome_at": null,
      "label": "No agent outcome data yet"
    },
    "auto_install": {
      "allowed": false,
      "sandbox_required": true,
      "reason": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
    },
    "best_for": [
      "automation",
      "agent-skill"
    ],
    "known_risks": [
      "The SKILL.md excerpt is truncated; the full file may contain additional details not reviewed.",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "Dependency/runtime risk: command execution surface, credential or environment access",
      "Permission surface: secrets or environment access, shell or command execution"
    ]
  },
  "agent_proven": {
    "version": "agent-proven-v1",
    "score": 0,
    "tier": "unproven",
    "label": "Needs first agent run",
    "summary": "No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.",
    "metrics": {
      "totalOutcomes": 0,
      "successfulOutcomes": 0,
      "failedOutcomes": 0,
      "installAttempts": 0,
      "installSuccessRate": null,
      "successRate": null,
      "recentSuccessRate": null,
      "recentFailureRate": null,
      "riskBlocked": 0,
      "setupRequired": 0,
      "notRelevant": 0,
      "avgOutputQuality": null,
      "avgTimeToUsefulMs": null,
      "productionOutcomes": 0,
      "humanReviewRequired": 0,
      "uniqueAgents": 0,
      "lastOutcomeAt": null
    },
    "signals": [],
    "penalties": [
      "No real agent outcome evidence yet"
    ]
  },
  "audit": {
    "score": 77,
    "risk_level": "needs_review",
    "risk_label": "Needs review",
    "warnings": [
      "Dependency or permission surface needs review",
      "Permission surface may require sandboxing",
      "The SKILL.md excerpt is truncated; the full file may contain additional details not reviewed.",
      "The description says 'Do NOT trigger for jit-optimize' but the skill includes jit-optimize in the prerequisite check and mentions it in bench conditions, which could cause confusion.",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "Dependency/runtime risk: command execution surface, credential or environment access",
      "Permission surface: secrets or environment access, shell or command execution"
    ]
  },
  "safety_gate": {
    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 74,
    "label": "Strong"
  },
  "supply": {
    "track": "Data, BI, and analytics",
    "scenario": "Workflow automation",
    "maintenance": "20d since push",
    "risk": "Needs review"
  },
  "alternative_skills": [],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "production agents without a repository review",
    "The SKILL.md excerpt is truncated; the full file may contain additional details not reviewed.",
    "No OpenAgentSkill engagement data yet",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "The description says 'Do NOT trigger for jit-optimize' but the skill includes jit-optimize in the prerequisite check and mentions it in bench conditions, which could cause confusion."
  ],
  "agent_contract": {
    "task_input": "Use skvm-general in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 68/100 Manual review",
      "Audit: 77/100 Needs review",
      "Safety: 37/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "sjtu-ipads-skvm-general (skvm-general)",
      "install_command": "npx skills add SJTU-IPADS/SkVM --skill skvm-general",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "sjtu-ipads-skvm-general",
      "task": "Use skvm-general in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/sjtu-ipads-skvm-general",
    "api": "https://www.openagentskill.com/api/agent/skills/sjtu-ipads-skvm-general",
    "audit": "https://www.openagentskill.com/skills/sjtu-ipads-skvm-general/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=sjtu-ipads-skvm-general&task=Use%20skvm-general%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20skvm-general%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20skvm-general%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/sjtu-ipads-skvm-general/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/sjtu-ipads-skvm-general"
  }
}

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Creator: SJTU-IPADS
Source: SJTU-IPADS/SkVM
Indexed by: OpenAgentSkill community index

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