Install
$ agentstack add skill-lakesoftai-deepdone-agent-skills-deepdone-verify ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
DeepDone Verify
Purpose
Make verification deterministic and scoped.
Verification Order
Start narrow. Widen only when impact demands it.
Typical order:
- focused unit or smoke check nearest change
- package or app lint
- package or app typecheck
- affected test suite
- broader integration or full gate if shared code changed
If repo exposes shared wrappers, prefer just . If not, use repo's real package-level commands.
Command Discovery
Before guessing commands, inspect repo affordances in this order:
justfileor.justfile: prefer named project wrappers.Makefile: use focused targets liketest,lint,check, or package-specific targets.package.json: readscripts; prefer focused package scripts before broadtest.pyproject.toml: inspect tool config and project layout; preferuv run,pytest,ruff,mypy, or repo wrappers already documented.Cargo.toml: prefercargo test,cargo check, or package-specific variants.go.mod: prefergo test ./...only when package-level test is not obvious.- Existing CI config or README commands if no local wrapper is found.
Record discovery when it affects command choice:
- command: discovery
result: pass
notes: found `justfile`; using `just test-auth` before broader checks
If discovery finds no runnable command, log a blocked entry with exact reason.
Mandatory Notes
Record exact command and result. If something could not run, state exact blocker. Do not claim pass based on intent.
Ledger Writeback
Append lines under ## Verification Log such as:
- command: `just test api-auth`
result: pass
notes: focused auth test passed
- command: `uv run pytest tests/test_auth.py`
result: fail
notes: login fixture broken after schema change
- command: `just lint`
result: blocked
notes: no `justfile` in repo
result: is required. Use only pass, fail, or blocked.
Risk Triggers
Broaden verification when change touches:
- shared libs
- auth or permissions
- schema or migrations
- external APIs
- config or deployment-sensitive paths
- user-visible flows
Workflow
- Discover command wrappers and package-level checks.
- Identify smallest convincing check.
- Run it.
- Decide whether broader checks are needed.
- Run broader checks only where justified.
- Log every command and outcome with
result:. - State residual risk if coverage still incomplete.
Output
Return:
- checks run
- pass, fail, or blocked status
- residual risk
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: lakesoftai
- Source: lakesoftai/deepdone-agent-skills
- License: Apache-2.0
- Homepage: https://deepdone.ai
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.