Agent skill
verification-gate
Quellcode ansehen: tt-a1i/harnessed
Installation
npx skills add tt-a1i/harnessed --skill verification-gate1
Installationen
EU-hosted inference API
Power your AI agent skills with open-source models.
Drop-in OpenAI-compatible API. No data leaves Europe.
MiniMax
MiniMax M3
$0.40 / $1.40
per M tokens
Z.ai
GLM 5.3 Flash
$0.20 / $0.60
per M tokens
MoonshotAI
Kimi K3
$4.00 / $18.00
per M tokens
DeepSeek
DeepSeek V4.1 Flash
$0.40 / $1.40
per M tokens
Verification Gate
Before you say "done", PROVE it.
This is the final checkpoint. You must provide concrete, structured evidence for every acceptance criterion that can be verified automatically. Criteria marked MANUAL_REVIEW_NEEDED are listed for human follow-up, not skipped silently. Self-review notes may appear as background context, but they are not evidence.
<HARD-GATE> NO COMPLETION CLAIMS WITHOUT EVIDENCE FOR EVERY AUTOMATABLE CRITERION. Criteria requiring human judgment must be explicitly listed as pending review — never silently omitted. </HARD-GATE>When This Skill Activates
- After independent-qa returns SHIP or SHIP_WITH_HUMAN_REVIEW
- Before any completion signal to the user
- For micro tasks: this is the only Harnessed gate
Process
Step 0: Staleness and Governance Check
Before collecting evidence:
- Read
.harnessed/qa-state.mdif it exists - Re-run QA if
head_commitorcontract_hashno longer match - Read the QA report overview and note:
- Review Mode
- Risk Level
- Calibration Status
- Confidence
- Uncertainty
- If confidence is low on a high-risk task, or calibration is stale/missing, the best possible outcome is
VERIFIED_PENDING_HUMAN_REVIEW - If the QA path required corroboration, verify the corroborating report exists
- If disagreement required a tie-break, verify the tie-break report exists
Step 1: Locate Criteria Source
- Standalone mode: Read
.harnessed/contract.md - Complementary mode: Read
.harnessed/contract.md - Micro task: infer explicit criteria from the user's request
Step 2: Collect Evidence Per Criterion
For each criterion, provide one primary evidence type:
| Evidence Type | Format | When to Use |
|---|---|---|
| Code citation | file.ext:42 — "{code snippet}" |
Implementation exists at this location |
| Test citation | test_file.ext:15 — test name: "{name}" |
A test covers this criterion |
| Command output | $ command → {output} |
Running a command proves behavior |
| HTTP smoke test | $ curl ... → {status + body} |
Tier 1.5 behavior check |
| Static analysis | semgrep / CodeQL / bandit output |
Security-sensitive evidence |
Supplementary context (cannot be used as evidence by itself):
| Context Type | Use |
|---|---|
| QA confirmation | Supports, but never replaces, primary evidence |
| Self-review | Background only; useful for audit history, not proof |
Rules:
- evidence must be current
- if you cannot produce evidence for an automatable criterion, the task is not complete
- self-review can be cited only in a
Backgroundsection, never inEvidence
Step 3: Check for Gaps
Verify:
- every automatable criterion has primary evidence
- QA ended in SHIP or SHIP_WITH_HUMAN_REVIEW
- no unresolved ITERATE/BLOCKED findings remain
- high-risk tasks have corroborating review coverage
- tie-break reports exist when disagreement occurred
- low confidence, stale calibration, heuristic security review, or missing security tools are carried into pending human review rather than silently ignored
Step 4: Write Verification Summary
Write .harnessed/verification-summary.md:
# Verification Summary
## Task
{task description}
## Status: VERIFIED | VERIFIED_PENDING_HUMAN_REVIEW
## Review Governance
- Risk level: {standard or high-risk}
- Review mode: {mode}
- Calibration status: {current / stale / missing}
- Confidence: {High / Medium / Low}
- Uncertainty: {summary or None}
## Evidence
### Criterion: "{criterion text}"
- **Evidence:** {type}: {citation}
- **Verified:** Yes
## Pending Human Review
{manual-review criteria, low-confidence boundaries, security-sensitive follow-ups}
## Background
- Self-review notes: {optional, informational only}
- QA confirmation: {optional, informational only}
## QA History
- Rounds: {number of QA iterations}
- Final grade: {SHIP or SHIP_WITH_HUMAN_REVIEW}
- Issues fixed during QA: {brief list or None}
## Files Changed
{list of modified files}Step 5: Present to User
For fully verified work:
- say the task is complete
- mention criteria count and QA rounds
- point to
.harnessed/verification-summary.md
For pending human review:
- say the task is code-complete or verified pending human review
- state exactly what still needs human review
- never imply the remaining checks were automated
Micro Task Verification
For micro tasks:
- state what changed and why
- cite the file:line
- confirm no obvious regression
- present a one-line summary
Anti-Rationalization
| Your Thought | Why It's Wrong | What To Do |
|---|---|---|
| "The QA already passed, this gate is redundant" | QA checks correctness; the gate checks evidence completeness and human-review carry-through. | Complete the gate. |
| "My self-review proves the behavior" | Self-review is biased background, not proof. | Use self-review as context only. |
| "The reviewer confidence was low, but I'll still call it fully done" | Low confidence is a governance signal. Ignoring it defeats the point of the gate. | Downgrade to pending human review. |
| "No security tool complained, so we're safe" | Tool silence is not proof of safety. | Record the limitation and keep human review where needed. |
What Verification Gate Catches That QA Doesn't
QA asks: "Does the implementation appear correct?"
Verification Gate asks: "Can we prove every automatable requirement, and did we preserve uncertainty honestly?"
The combination of independent QA + verification gate creates a double-lock:
- QA lowers self-evaluation bias and increases issue-finding rate
- Gate ensures every requirement and every uncertainty is surfaced explicitly
Verwandte Skills
Mehr aus dieser Quelle: tt-a1i/harnessed
So verwenden Sie diesen Skill
- 1
Install verification-gate by running
npx skills add tt-a1i/harnessed --skill verification-gatein your project directory. Führen Sie den obigen Installationsbefehl in Ihrem Projektverzeichnis aus. Die Skill-Datei wird von GitHub heruntergeladen und in Ihrem Projekt platziert. - 2
Keine Konfiguration erforderlich. Ihr KI-Agent (Claude Code, Cursor, Windsurf usw.) erkennt installierte Skills automatisch und nutzt sie als Kontext bei der Code-Generierung.
- 3
Der Skill verbessert das Verständnis Ihres Agenten für verification-gate, und hilft ihm, etablierte Muster zu befolgen, häufige Fehler zu vermeiden und produktionsreifen Code zu erzeugen.
Was Sie erhalten
Skills sind Klartext-Anweisungsdateien — kein ausführbarer Code. Sie kodieren Expertenwissen über Frameworks, Sprachen oder Tools, das Ihr KI-Agent liest, um seine Ausgabe zu verbessern. Das bedeutet null Laufzeit-Overhead, keine Abhängigkeitskonflikte und volle Transparenz: Sie können jede Anweisung vor der Installation lesen und prüfen.
Kompatibilität
Dieser Skill funktioniert mit jedem KI-Coding-Agenten, der das skills.sh-Format unterstützt, einschließlich Claude Code (Anthropic), Cursor, Windsurf, Cline, Aider und anderen Tools, die projektbezogene Kontextdateien lesen. Skills sind auf Transportebene framework-agnostisch — der Inhalt bestimmt, für welche Sprache oder welches Framework er gilt.
Data sourced from the skills.sh registry and GitHub. Install counts and security audits are updated regularly.