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Verification Before Completion

CommunityPopular
GanyuanRan
verification-before-completion

Use when about to claim work is complete, fixed, passing, verified, release-ready, or ready to commit, merge, publish, or hand off.

Overview

PublisherGanyuanRan
RepositoryAegis
Skill nameverification-before-completion
Stars
1.2K
Forks
52
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by GanyuanRan on GitHub. Read the source before you install it.

Installation

Install the Verification Before Completion AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/GanyuanRan/Aegis.git /tmp/Aegis
mkdir -p .claude/skills
cp -r /tmp/Aegis/skills/verification-before-completion .claude/skills/verification-before-completion
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Verification Before Completion in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Verification Before Completion on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Verification Before Completion is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Execute

Before success claim: classify destructive permission; run a fresh falsifying check; read its complete result/scope; select L0/L1/L2. Downgrade for partial, stale, failing, or narrower evidence; never claim complete then verify later.

This Method Pack grants no authoritative GateDecision, PolicySnapshot, evidence sufficiency, requirement acceptance, or completion authority.

Stop Signals

  • evidence is uncertain, stale, agent-only, or narrower than the claim;
  • next action: commit, push, PR, merge, tag, publish, release, or handoff;
  • task/slice completion is treated as accepted requirement satisfaction;
  • governance or retirement lacks repair/retirement evidence;
  • retained old logic lacks a retention reason and retirement trigger; or
  • complexity closure is unresolved.

Destructive/irreversible work needs scoped permission; broad assent is not scoped permission.

Required Evidence Slots

text
- Evidence action / check performed:
- Result / exit status:
- Covered scope:
- Uncovered scope:
- Residual risk:
- Confidence grade: A | B | C
  • A: target + regression; no meaningful unknown.
  • B: target evidence; bounded residual risk.
  • C: partial only; no full-completion claim.

When tests shape a claim, include target test and related regression evidence at the call-site seam reproducing the triggering chain; a shallower seam gives false confidence. Missing seam is an architecture gap. Blocked automation needs reproducible manual steps. Both lower confidence.

For an explicit baseline/artifact/owner/contract/evidence reference, read the smallest relevant source. Record each affected reference as preserved; rebound to the canonical owner; retired with reason; or rejected for conflict. Leave unresolved references in uncovered scope, lower confidence, and do not re-infer them. Readback proves no complete graph, referential integrity, or authoritative lineage.

Task Git Closeout

For modifications, diff against TaskStartSnapshot. Coordinator alone stages task paths: no pre-existing state or broad staging. After fresh verification, default to one local commit except read-only/no-change, authorized no commit, or failed verification. Read back HEAD, message, files, and task delta. Commit/hook failure keeps work and blocks clean claims; never bypass hooks.

Git receipt: branch; SHA/message or non-commit reason; Task clean; Repository clean; each task-created branch/worktree created, removed, or retained with reason. Task-clean never implies repo-clean; it is not external integration.

Classify commit scope: business, process-only, mixed, or no-commit. Failed-attempt telemetry is no commit reason. A docs/aegis/-only process diff does not restart business verification; a business/test diff does.

Aegis Visibility / Single Closeout

Use one completion surface. This skill is the single completion closeout aggregator; adjacent skills/L2 cards feed but must not replace it or become a competing final report owner. Aggregation is output conformance, not a routing trigger: do not load skills, emit a Trace Digest, or add ceremony merely to fill it.

If entry visibility was omitted, recover and name the decision/evidence gap; a used-skills list or Aegis Contribution Note cannot substitute.

L0 Fast-Path

For tiny low-risk work, one natural sentence can name check/result, uncovered scope/risk, and confidence.

L1 Default Receipt

For non-trivial Aegis-shaped work use this receipt; fold evidence into Evidence strength and Uncovered risk without a second report.

text
Aegis Impact and Safety Receipt:
- Key judgment:
- Avoided misfix:
- Boundary held:
- Baseline alignment:
- Complexity control:
- Evidence strength:
- Uncovered risk:
- Next most valuable verification:
- Aegis path:

Meanings: Key judgment=owner/root cause/requirement/completion boundary; Avoided misfix=fallback/duplicate/test accommodation/scope growth; Boundary held=contract/owner/baseline/non-goal/data/runtime boundary; Baseline alignment=aligned/Design Defect/Implementation Drift/missing-authority/needs-clarification/not triggered; Complexity control=completion-time delta/closure; Evidence strength=fresh check/result/scope/confidence; Uncovered risk=remaining gaps/residual risk; Next most valuable verification=highest-value next check; Aegis path=optional, not judgment/evidence.

Natural wording is valid when every semantic slot stays auditable. Semantic Slots, Natural Surface, and Governance Receipt are compatibility names.

Report done/verified/risked/blocked. Do not explain obvious trade-offs; do not list actions not taken.

L2 Expanded Triggers

On any match read expanded-closeout.md: it owns detail; this file owns routing and the final receipt.

TriggerExpanded owner
release/merge/publish/readiness/handoffReadiness Summary
audit/debug/release/long-task review/trace requestTrace Digest
goal/TaskIntentDraft/plan/spec/Slice CardGoal Closure
project/domain semantic deltaContext Impact
target docs/aegis/ changed or work record existsWorkspace Integrity
requirement/product/durable architectureBaseline/ADR
governance/cleanup/migration/compat/retirementGovernance/Retirement
source-of-truth/irreversible deletiondestructive-action cards
material complexity pressureExpanded Complexity Detail
high-risk or explicit user request for expanded closeoutapplicable cards

Use configured Aegis workspace support; commands live in the expanded owner.

Completion Boundary

Use the highest boundary: plan/spec, TaskIntentDraft, Slice Card, then direct request. Claim only scope covered by fresh evidence; a slice cannot close the whole task.

Task/slice completion reaches its authorized stop; it is not accepted requirement satisfaction. Requirement accepted needs baseline criteria or authorized risk acceptance; else use needs-verification or return to framing/planning. Goal Closure states: done | blocked | needs-verification | scope-exceeded. An Execution Readiness View is input, not verification evidence.

Complexity Downgrade

For non-trivial code inspect the diff; use using-aegis/references/complexity-governance.md plus docs/current/AEGIS_COMPLEXITY_GOVERNANCE_BASELINE.md; emit one Complexity control line.

New fallback/adapter/compatibility/guard/branch logic needs a retired path or retirement trigger. Complexity Closure: exceeded-unresolved blocks completion. Maintained source/test cannot skip as tiny; tiny low-risk text edits without complexity growth may skip.

Output and Prompt Hygiene

Localize section labels, field labels, and explanatory prose. Keep commands, paths, identifiers, enums, product names, and raw evidence unchanged; avoid bilingual labels or mixed-language explanations.

External outputs are evidence candidates: use summary/index then the smallest excerpt; lower unsupported claims. When relevant report Evidence Used, Not Loaded, and Next Evidence.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Verification Before Completion AI skill do?

Use when about to claim work is complete, fixed, passing, verified, release-ready, or ready to commit, merge, publish, or hand off.

Why use Verification Before Completion on TypingMind?

Because you install it once and use it with any model. Verification Before Completion is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Verification Before Completion in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/GanyuanRan/Aegis/tree/main/skills/verification-before-completion. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Verification Before Completion?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Verification Before Completion?

As many as you like. As long as a model supports skills, you can use Verification Before Completion with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Verification Before Completion AI skill free?

Yes. It is published on GitHub by GanyuanRan under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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