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Vc Validate Findings

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withkynam
vc-validate-findings

Use when running VALIDATE V2-V3 fan-out. Two-layer investigation (4 dimension agents + per-section feasibility agents) synthesized into PASS/CONDITIONAL/BLOCKED net gate. Strategy-agnostic.

Overview

Publisherwithkynam
Repositoryvibecode-pro-max-kit
Skill namevc-validate-findings
Stars
1.1K
Forks
234
Bundled files
2
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.

  • 2 bundled files

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

  • Open source

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

Installation

Install the Vc Validate Findings 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.
    https://github.com/withkynam/vibecode-pro-max-kit/tree/main/.claude/skills/vc-validate-findings
  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/withkynam/vibecode-pro-max-kit.git /tmp/vibecode-pro-max-kit
mkdir -p .claude/skills
cp -r /tmp/vibecode-pro-max-kit/.claude/skills/vc-validate-findings .claude/skills/vc-validate-findings
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Vc Validate Findings 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 Vc Validate Findings 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 Vc Validate Findings 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.

vc-validate-findings

Output style: Follow process/development-protocols/communication-standards.md — answer-first, plain language, no unexplained jargon, TL;DR on long responses.

Defines the role specs, prompts, and output schemas for the two-layer VALIDATE fan-out (a parallel check across 4 dimensions + per-section feasibility probes). Produces a net gate verdict (PASS / CONDITIONAL / BLOCKED) and all the inputs needed to write the validate-contract — the written checklist that gates EXECUTE.


References

  • references/example-validate-output.md — full V4 menu + validate-contract template; calibrate output format against this file

When To Invoke

  • VALIDATE V2–V3: invoked by vc-validate-agent after V1 pre-check passes
  • Post-execute review in UPDATE PROCESS when re-validation of a plan is needed

Strategy Boundary

This skill is STRATEGY-AGNOSTIC. It defines the role specs, prompts, and output schemas for each dimension/section agent. It does NOT determine how those agents are executed (Workflow vs parallel Agent tool vs vc-team).

The execution method is determined by vc-agent-strategy-compare, which must be invoked BEFORE vc-validate-findings is called. vc-validate-findings outputs agent role definitions; the caller executes them using the recommended strategy.


Mode Selection

Choose before spawning any agents. Pass the selected mode to all Layer 1 and Layer 2 agents in their prompt context.

Simple Mode (default)

Runs the Layer 1 + Layer 2 fan-out using context available in the current conversation. Validation agents derive context from the plan file and what was passed in the prompt.

Appropriate when:

  • Plan is self-contained (all context needed is in the plan text)
  • Context is fresh in the current conversation window
  • Blast radius is clear and confined to a single domain
  • No container/infra/worker lifecycle surfaces are touched
  • Fewer than 5 blast-radius packages

Deep Mode

Trigger when any one of the following is true:

  • Plan blast radius touches container, infra, or worker lifecycle
  • Plan has 5+ blast-radius packages
  • Plan is a phase in a multi-phase program (prior phase outputs must be loaded)
  • Caller explicitly requests deep mode

How Deep Mode works — run this context-loading step BEFORE spawning any Layer 1 or Layer 2 agents:

  1. Read process/context/all-context.md routing table → identify which context groups are relevant to the blast radius
  2. Load relevant context group all-*.md files (e.g. container/all-container.md, infra/all-infra.md, tests/all-tests.md, skills/all-skills.md) — only the groups that apply
  3. If phase program: read the latest 1–2 phase reports from inside task folders (process/features/{feature}/active/{slug}_{date}/{slug}_REPORT_{date}.md) or legacy process/features/{feature}/reports/ (read-only legacy path)
  4. Read existing test files in the blast radius (gives the Layer 1 test-coverage agent real data, not guesses)
  5. Package all loaded material as a Context Bundle and pass it to every Layer 1 and Layer 2 agent

Quality difference in practice:

  • Simple: Layer 1 infra agent infers container port assignments from plan description text
  • Deep: Layer 1 infra agent reads the container context group (process/context/container/ or the repo's equivalent routing entrypoint) and knows the real port table (gateway:3000, file-server:3001, app-dev:3002, ctx-gateway:3099, browser-bridge:9377, MITM:9090/9091) — can catch real port conflicts the plan missed

Layer 1 — Four Always-On Dimension Agents

Always run all four, regardless of complexity score. These run in parallel.

DimensionFocusContext to attach
Infra/setup fitDoes this work with container/worker/proxy architecture? Are target file paths, port numbers, and runtime surfaces correct?process/context/all-context.md routing → container and infra groups (follow routing table for local paths)
Test coverageIs the verification strategy realistic given the test infra? Which tiers apply (fully-automated / hybrid / agent-probe)?process/context/tests/all-tests.md
Breaking changesIdentify API contracts, schemas, auth flows, or public contract changes. Are downstream consumers listed and safe?Plan's Public Contracts and Blast Radius sections
Security surfaceQuick STRIDE/OWASP scan. Does the plan touch auth, billing, data, secrets, or trust boundaries?vc-security skill context

Note: the security surface dimension INVOKES the vc-security skill — do not absorb vc-security's logic here.

Per-Agent Output Format

Dimension: [name]
Status: PASS | CONCERN | FAIL
Findings:
- [finding 1]
- [finding 2]
Confidence: HIGH | MEDIUM | LOW
Notes: [optional context]

Layer 2 — Per-Section Feasibility Agents

One agent per plan section or phase. These run in parallel with each other (and may overlap with Layer 1 in time, but Layer 2 results are presented after Layer 1 results are collected).

Each Layer 2 agent must answer four questions — not just "are edit targets findable?":

  1. Mechanical feasibility — Are the edit target strings present and uniquely matchable in the named files? Can the described create/write steps be executed without collision?
  2. Plan gaps — What is this section missing that it should include? Are there adjacent files or behaviors that should be updated but are not listed?
  3. Conflicts — Does anything in this section contradict current file state, other plan sections, or repo conventions?
  4. Risk — What is the single highest-risk edit in this section and how should the execute-agent sequence or mitigate it?

Per-Agent Output Format

Section: [section name or phase number]
Status: PASS | CONCERN | FAIL
Mechanical feasibility: [verdict + evidence]
Gaps found: [list or "none"]
Conflicts found: [list or "none"]
Highest-risk edit + mitigation: [description]

Warning: A Layer 2 agent that only confirms edit targets are findable without assessing gaps and conflicts is incomplete and must be re-run with the full four-question prompt.

[V2-PROBE] Feasibility Probe Emission Rule

When answering the 4 questions above, if a plan section depends on an untested runtime/system behavior (network/protocol/runtime/third-party response shape) that cannot be verified by reading source files, the Layer 2 agent MUST:

  1. Emit VC-FEASIBILITY-PROBE-NEEDED: [hypothesis] — cost-class: [class] and halt.
  2. NOT produce the standard per-agent output format.
  3. NOT attempt to reason about the untested behavior as if it were mechanical.

Mechanical checks (NO probe): edit targets findable by Grep, file exists, schema field present, export names matchable, port in the container table, config key present in env.ts.

Examples of mechanical checks (NO probe): "does src/env.ts export a <SERVICE>_JWT_SECRET field?" → read the file; "does the container table list port 3000 for the gateway service?" → read the table.

Probe candidates (emit + halt): any behavior that requires a running system, live network call, or in-container exec to verify.

Examples of probe candidates: "does the gateway forward the X-Custom-Routing header to the upstream provider at runtime?", "does the container proxy honor the allow-list config field when injecting platform keys?", "does the OpenRouter API return pricing as a string or a number?".

Note: For each CONCERN found, INVOKE vc-scenario. For high-risk flagged concerns, INVOKE vc-predict.


V3 Synthesis Rules

The vc-validate-agent (or orchestrator) synthesizes all Layer 1 and Layer 2 outputs:

  1. Collect all dimension and section verdicts.
  2. List all FAILs prominently — any FAIL from any agent must be surfaced.
  3. List all CONCERNs grouped by dimension.
  4. Note contradictions between agents explicitly; do not resolve them silently.
  5. Compute the net gate status:
    • Any FAIL → gate is BLOCKED (unless user explicitly converts to CONDITIONAL)
    • One or more CONCERNs, no FAILs → gate is CONDITIONAL (if user accepts) or re-runs
    • No FAILs, no CONCERNs → gate is PASS
  6. Select test gates per the Test Tier Waterfall section.
  7. Confirm or update the parallel strategy recommendation based on synthesized findings.

Note: Invoke vc-sequential-thinking at this synthesis step for contradiction ranking.


Net Gate Derivation Output Format

Present this exact table format after synthesis:

Layer 1 dimensions

Layer 1 dimensionsStatus
Infra fitPASS / CONCERN / FAIL
Test coveragePASS / CONCERN / FAIL
Breaking changesPASS / CONCERN / FAIL
Security surfacePASS / CONCERN / FAIL

Layer 2 sections

Layer 2 sectionsStatus
Section A — [name]PASS / CONCERN / FAIL
Section B — [name]PASS / CONCERN / FAIL
Section N — [name]PASS / CONCERN / FAIL

Totals: [N] FAILs / [N] CONCERNs / [N] PASSes

→ Net Gate: [PASS / CONDITIONAL / BLOCKED]

Decision rules:

  • PASS: 0 FAILs, 0 CONCERNs. All plan fixes applied. Proceed to EXECUTE.
  • CONDITIONAL: 0 FAILs, [N] CONCERNs. [N] fixed in plan, [N] as execute-agent instructions, [N] as known-gaps. Proceed to EXECUTE with gaps on record.
  • BLOCKED: [N] unresolved FAILs. [List each.] Return to PLAN — do not route to EXECUTE until each FAIL is resolved or explicitly converted to CONDITIONAL by user.

Findings Output Format

Use this table format for each dimension's findings (Section I of the V4 menu):

FindingSeverityProposed fix
[Finding description]CONCERN / FAIL[Proposed fix — apply to plan, execute-agent instruction, or backlog artifact]
[Finding description]✅ PASS—

Show PASS findings as ✅ PASS with — in the proposed fix column.


Section IV Output Format

Proposed Plan Updates

Show the summary below to the user before they approve EXECUTE (before gate V5). These changes are applied to the plan file when the user accepts.

#What changesWhere in planWhy
P1[e.g. Add route registration step to Section A checklist][Section A — Implementation Checklist][Gap found: route not reachable without this step]
P2[e.g. Correct blast radius: add downstream consumers][Blast Radius section][Breaking-changes agent found unlisted consumers]
PN[...][...][...]

Execute-Agent Instructions

Concerns that cannot be fixed in plan text — written to the validate-contract for execute-agent to follow:

#InstructionTrigger condition
E1[e.g. Confirm exact file path before writing Section A. If path differs: update edit target, do NOT skip. Document corrected path in phase report.]Section A entry
E2[e.g. Container change requires image rebuild. Use docker:build + container recreate via API lifecycle. Never docker cp.]Section D entry
EN[...][...]

Backlog Artifacts

ArtifactLocationWhat it tracks
[e.g. test-envelope-regression_NOTE_03-06-26.md][process/features/development-process/backlog/][Envelope regression test against downstream consumers]
[...][...][...]

/goal Block Output

After the validate-contract is written to the plan file (V6), the skill stores the /goal block.

Single plan: derive the /goal block from plan content (SESSION GOAL, charter, autonomy rules, hard stops, next phase, contract summary, execute start command). Write it to the plan file under a new ## Autonomous Goal Block section.

Multi-phase program: the /goal already exists in the umbrella ## Stable Program Goal. Do NOT rewrite it — only verify it is current and points to the umbrella path.

The /goal block must include:

  • SESSION GOAL
  • Autonomy rules
  • Hard stops
  • Next phase
  • Contract summary
  • Execute start command

Multi-phase program rule: If operating within a phase program (umbrella plan exists), emit the /goal block update automatically without asking — do not prompt the user.

Single-plan rule: Store the /goal block during V6. Whether it is printed for copy-paste is decided by the validate-agent's single V5 gate option (Accept vs Accept + print /goal). This skill does not open an extra prompt or separate user round-trip.

/goal must be fully copy-pastable (plain text block, no special formatting, under 4000 chars).

V5 during /goal autonomous execution: agent self-decides (CONDITIONAL → proceed, BLOCKED → backlog + proceed to next non-blocked phase). Skip any print ask — user is not present.

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 Vc Validate Findings AI skill do?

Use when running VALIDATE V2-V3 fan-out. Two-layer investigation (4 dimension agents + per-section feasibility agents) synthesized into PASS/CONDITIONAL/BLOCKED net gate. Strategy-agnostic.

Why use Vc Validate Findings on TypingMind?

Because you install it once and use it with any model. Vc Validate Findings 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 Vc Validate Findings in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/withkynam/vibecode-pro-max-kit/tree/main/.claude/skills/vc-validate-findings. 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 Vc Validate Findings?

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 Vc Validate Findings?

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

Is the Vc Validate Findings AI skill free?

Yes. It is published on GitHub by withkynam 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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