Agent skill

Functional Verification

by hdl-tools in hdl-tools/digital-chip-design-agents

UVM-based functional verification — testbench architecture, test planning, directed and constrained-random stimulus, functional and code coverage closure, formal assist, and regression sign-off.

MITAuto-check: notesTesting & QA

Install Functional Verification

skills CLI
$ npx skills add hdl-tools/digital-chip-design-agents --skill functional-verification -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install hdl-tools/digital-chip-design-agents functional-verification --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/hdl-tools/digital-chip-design-agents.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/verification/skills/functional-verification .claude/skills/functional-verification && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
functional-verification
GitHub stars
211
Token cost
~4.5k tokens
SKILL.md length
2,197 words
Files
1
Skills in repo
17
Repo updated
First seen
Licence
MIT

At a glance

UVM-based functional verification — testbench architecture, test planning, directed and constrained-random stimulus, functional and code coverage closure, formal assist, and regression sign-off.

  • Works in 2 steps: memory/verification/knowledge.md — known… → memory/verification/run_state.md —…
  • Building a UVM testbench
  • SKILL.md covers Invocation, Pre-run Context, Purpose and Supported EDA Tools, plus 5 more sections
  • Calls pip

What it does

Functional Verification is an agent skill from hdl-tools/digital-chip-design-agents. UVM-based functional verification — testbench architecture, test planning, directed and constrained-random stimulus, functional and code coverage closure, formal assist, and regression sign-off. Use when building a UVM testbench, writing tests, analysing coverage, or managing a verification regression.

Its SKILL.md is about 4.5k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Testing & QA, covering Test strategy and Test coverage. The repository describes itself as: Digital HDL Design Full-stack Agents. The licence is MIT.

When your agent uses it

  • Building a UVM testbench
  • Analysing coverage
  • Managing a verification regression

Example prompts

  • “/functional-verification”

Requirements

  • Python 3
  • Pre-approved tools (allowed-tools): Read, Write, Bash

Workflow steps

2 steps, taken from the first numbered list in SKILL.md.

  1. memory/verification/knowledge.md — known failure patterns, successful tool flags, PDK/tool quirks.
  2. memory/verification/run_state.md — current run identity (run_id, design_name, tool,

What it can do on your machine

Read from SKILL.md and the folder at commit 38736b1. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Write
    • Bash

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • pip

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Functional Verification loads about 4.5k tokens when it runs. Until then it costs about 82 tokens; SKILL.md has 2,197 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~82
When it runs · the whole SKILL.md, loaded when a task matches
~4.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Read, Write, Bash

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from hdl-tools/digital-chip-design-agents at commit 38736b1, republished under its MIT licence (© hdl-tools). 2,197 words, ~4,460 tokens.

Download SKILL.mdSave it as .claude/skills/functional-verification/SKILL.md (or your agent's skills folder).
name
functional-verification
description
UVM-based functional verification — testbench architecture, test planning, directed and constrained-random stimulus, functional and code coverage closure, formal assist, and regression sign-off. Use when building a UVM testbench, writing tests, analysing coverage, or managing a verification regression.
allowed-tools
Read, Write, Bash
version
1.0.0
author
chuanseng-ng
license
MIT

Skill: Functional Verification (UVM)

Invocation

When this skill is loaded and a user presents a verification task, do not execute stages directly. Immediately spawn the digital-chip-design-agents:verification-orchestrator agent and pass the full user request and any available context to it. The orchestrator enforces the stage sequence, loop-back rules, and sign-off criteria defined below.

Use the domain rules in this file only when the orchestrator reads this skill mid-flow for stage-specific guidance, or when the user asks a targeted reference question rather than requesting a full flow execution.

Pre-run Context

Before executing or advising on any stage, read the following files if they exist:

  1. memory/verification/knowledge.md — known failure patterns, successful tool flags, PDK/tool quirks. Incorporate its guidance into every stage decision. If absent, proceed without it.
  2. memory/verification/run_state.md — current run identity (run_id, design_name, tool, last_stage). Use this to resume correctly after interruption. If absent, a new run is starting; the orchestrator will create this file before the first stage.

This pre-run read applies whether this skill is loaded by a user or called by the orchestrator mid-flow. It ensures the fix database is consulted before any diagnosis step.

Purpose

Guide the complete UVM functional verification flow from testbench architecture through coverage-closed regression sign-off. Produces a verified RTL package with documented coverage and a clean regression.


Supported EDA Tools

Open-Source
  • Verilator (verilator) — fast cycle-accurate simulator; UVM support via verilator+UVM
  • Icarus Verilog (iverilog) — event-driven simulation for quick testbench checks
  • cocotb — Python-based co-simulation framework (pip install cocotb)
  • PyUVM — UVM implementation in Python for cocotb environments
  • UVVM — VHDL verification methodology library
Proprietary
  • Synopsys VCS (vcs, dialect synopsys) — industry-standard SV/UVM simulator
  • Cadence Xcelium (xrun, dialect cadence) — multi-language simulator with coverage engine
  • Siemens Questa (vsim / vlog / vcom, dialect siemens) — mixed-language simulation with UVM support

Stage: tb_architecture

Domain Rules
  1. Follow UVM 1.2 standard (IEEE 1800.2)
  2. One UVM agent per DUT interface (driver, monitor, sequencer)
  3. Active agents: drive stimulus; passive agents: monitor only
  4. Scoreboard: checks DUT output against reference model output
  5. Reference model: functional model of DUT — SystemVerilog or C++ via DPI
  6. Coverage collector: separate component from scoreboard
  7. Virtual sequencer: coordinates multi-agent stimulus scenarios
  8. All TB parameters via uvm_config_db — no hardcoded values in components
UVM Hierarchy Template
uvm_test
  └─ uvm_env
       ├─ agent_A (active)   driver + monitor + sequencer
       ├─ agent_B (passive)  monitor only
       ├─ scoreboard
       ├─ coverage_collector
       └─ virtual_sequencer
QoR Metrics to Evaluate
  • All DUT interfaces covered by an agent
  • Reference model: adequate to check all DUT outputs
  • TB compile: 0 errors
Output Required
  • TB architecture diagram
  • UVM component list and hierarchy
  • Interface-to-agent mapping table

Stage: test_planning

Domain Rules
  1. Every functional requirement → at least one directed test
  2. Every interface → protocol compliance test
  3. Error/exception cases: explicit directed tests (not left to random)
  4. Corner cases: boundary values, max/min, overflow, underflow
  5. Concurrency: multi-threaded stimulus for pipeline stress
  6. Back-pressure: tests under flow control conditions
  7. Reset: in-operation resets, reset during active transaction
  8. Define covergroups before writing tests
  9. Plan the corner cases a directed test rarely reaches, one V-plan entry each:
    • simultaneous events — load and increment, read and write when empty, flush and enable, full and empty together
    • the first cycle out of reset
    • sustained back-pressure (ready low for many cycles) and maximum-rate input with no gaps
    • an input deasserting mid-transaction
    • both extremes of the clock ratio for a multi-clock block
  10. Test at boundary parameter values, not only the default configuration: WIDTH=1, DEPTH=1 and 2, N=1, and a non-power-of-two value. A value the RTL is meant to reject gets a test that it refuses to elaborate
  11. Take the RTL hand-off as an input: each entry in design_state.rtl.unverified[] (claims the RTL flow concluded without a tool run) gets a test or is marked as assigned to formal. cdc and reset entries cannot be closed by functional simulation — record them as needing a CDC/RDC tool run rather than as covered
V-Plan Entry Template (per feature)
feature_id:   F001
description:  AXI write burst handling
tests:        [direct_single_write, burst_len_256, narrow_transfer]
assertions:   [axi_valid_stable, axi_handshake_check]
covergroups:  [burst_len_cg, burst_type_cg]
priority:     P0
QoR Metrics to Evaluate
  • Requirement coverage: 100% of spec features mapped
  • P0 tests: must pass before random testing begins
  • Estimated test count: reasonable vs schedule
Output Required
  • V-plan document
  • Covergroup definitions
  • Assertion list with expected behaviour

Stage: uvm_tb_build

Domain Rules — Sequences
  1. Base sequence: minimum valid transaction
  2. Extended sequences: specific scenarios from V-plan
  3. Sequence library: register all sequences for random selection
  4. Never hardcode values — use randomised fields with constraints
Domain Rules — Testbench Coding

Testbench code is exempt from the RTL synthesis rules — initial, #delay and blocking assignments are correct here — but it is still SystemVerilog that has to elaborate the same way in every simulator.

  1. `default_nettype none at the top of every testbench, interface and bind file, restored with `default_nettype wire at the end so library code compiled after it is unaffected; a typo in a DUT connection must be an error, not an implicit one-bit net
  2. Instantiate the DUT with named port connections and named parameter overrides (#(.WIDTH(8))), never positional — a positional connection mis-wires silently when a port list is reordered
  3. Type every parameter and constant (int unsigned, logic [W-1:0]). An untyped parameter and a size-cast bare literal (WIDTH'(1)) are signed; in a scoreboard comparison that yields a false pass or a false mismatch on values with the top bit set
  4. Compare expected and actual at the same width and signedness, and compare with !== so an X or Z on the DUT output is a mismatch, not a silent pass
  5. Comment the why: each assumption about the DUT, each waiver, each deliberately illegal stimulus
Domain Rules — Drivers
  1. Drive signals cycle-accurate to protocol specification
  2. Handle back-pressure per the ready/valid contract. As a source: assert valid without waiting for ready, then hold valid and the payload stable until the beat is accepted. As a sink: ready may depend on valid. A driver that waits for ready before asserting valid deadlocks against a DUT that waits for valid, and hides DUT bugs that only appear under back-pressure
  3. Protocol assertion in driver to catch illegal stimulus early
Domain Rules — Scoreboard
  1. Predict expected output from reference model before DUT output arrives
  2. Report mismatches with full context (stimulus, expected, actual)
  3. Track: total checks, pass, fail, untriggered
Domain Rules — SVA Assertions
  1. Protocol assertions: in interface bind, not DUT
  2. Functional assertions: in checker or bind module
  3. All assertions: clearly named with descriptive failure message
  4. Every ready/valid interface carries the contract as assertions: transfer only on valid && ready; valid not retracted before the beat is accepted; payload stable while valid && !ready; valid and ready deasserted (not X) in reset
QoR Metrics to Evaluate
  • TB compile: 0 errors, 0 warnings
  • Sanity test: passes with known-good RTL
  • All components active in simulation log
Output Required
  • UVM component source files
  • SVA assertion files (bind-based)
  • Compile script

Stage: directed_tests

Domain Rules
  1. Implement one directed test per V-plan entry — tests must be deterministic

  2. Each test: verify the exact functional requirement it targets (no catch-all tests)

  3. Error/exception paths: explicit stimulus to trigger each one

  4. Corner cases: boundary values, max/min, overflow, underflow — one test each

  5. Reset during active transaction: at least one test per interface

  6. P0 tests must all pass before constrained-random phase begins

  7. DUT bug found during directed test: write a fix_request entry to design_state.fix_requests[] per the schema in the verification-orchestrator Design State section; terminate with decision=escalate. The pipeline-orchestrator (chip-design-meta) handles RTL re-invocation — do not loop locally or wait for user confirmation.

  8. Before filing, classify the failure as DUT bug or testbench bug, and name the mechanism, not just the location — the reported symptom is not necessarily the bug. Set suspected_rtl.basis to traced only when you followed the mismatch back to that signal in the waveform; otherwise hypothesis. Start from the symptom:

    SymptomLook first at
    Intermittent, rate scales with clock ratioClock-domain crossing
    Fails at bring-up, fine once runningReset release and reset values
    Hangs until resetFSM state with no exit, protocol wait-for cycle
    Wrong only for large valuesWidth truncation, arithmetic overflow
    Wrong only for negative valuesSigned/unsigned mix
    Fails only under sustained loadArbitration fairness, retracted valid, unstable payload
    Passes in RTL sim, fails in gate simBlocking assignment in clocked block, incomplete sensitivity list, X-optimism
    Degrades slowly over a long runLeaked credit or token
  9. Never make a failing test pass by weakening the check: do not loosen the scoreboard comparison, disable or waive an assertion, or constrain stimulus away from the failing case unless that stimulus was illegal per the spec. A fix to the testbench must leave every other test's checking at least as strict as before

Show full SKILL.md (796 more words)Show less
QoR Metrics to Evaluate
  • All V-plan features covered by at least one directed test
  • P0 directed tests: 100% pass before proceeding
  • 0 UVM FATAL or ERROR during directed test phase
Output Required
  • Directed test source files (one UVM sequence per feature)
  • Directed test pass/fail report
  • Bug report (if any DUT bugs found)

Stage: constrained_random

Domain Rules
  1. Constraint blocks: randomise all stimulus fields within protocol-legal ranges
  2. Bias constraints: weight toward uncovered coverage bins identified in prior runs
  3. Seeds: use at least 10 distinct seeds before evaluating coverage
  4. Scoreboards active throughout: every transaction checked against reference model
  5. Any UVM FATAL: stop immediately — do not accumulate errors across seeds
  6. Any scoreboard mismatch: classify as DUT bug or testbench bug before continuing, using the symptom table in directed_tests rule 8
  7. Run until coverage targets are met or max seed budget exhausted
QoR Metrics to Evaluate
  • Functional coverage: trending toward 100% across seeds
  • No persistent scoreboard mismatches (classify and fix before more seeds)
  • Regression pass rate: 100% (no failing seeds)
Output Required
  • Coverage report (merged across all seeds run so far)
  • Uncovered bin list for directed test closure
  • Seed log (seed number, pass/fail, coverage achieved)

Stage: coverage_analysis

Coverage Targets
TypeTargetPriority
Functional (V-plan)design_state.constraints.coverage.functional_pct% (default: 100%)P0
Code Line≥ design_state.constraints.coverage.line_pct% (default: 95%)P1
Code Branch≥ design_state.constraints.coverage.branch_pct% (default: 90%)P1
Code Toggle≥ design_state.constraints.coverage.toggle_pct% (default: 85%)P2
FSM Statedesign_state.constraints.coverage.fsm_state_pct% (default: 100%)P0
FSM Transition≥ design_state.constraints.coverage.fsm_transition_pct% (default: 95%)P0
Assertion triggereddesign_state.constraints.coverage.assertion_pct% (default: 100%)P1
Closure Strategy
  1. Identify uncovered bins after N random seeds
  2. Write targeted directed tests for hard-to-hit bins
  3. Adjust constraints to bias toward uncovered areas
  4. Waive unreachable bins with justification (dead code)
QoR Metrics to Evaluate
  • Functional coverage: 100% (no unwaived misses)
  • Code coverage: per targets above
  • Waiver file: all entries approved by verification lead
Output Required
  • Coverage report (merged across all seeds)
  • Uncovered bin list with closure plan
  • Waiver file

Stage: formal_assist

Use Cases for Formal
  1. Protocol compliance: prove handshake never violates
  2. Deadlock freedom: prove no state where valid=1 and ready never comes
  3. Liveness: every request eventually gets a response
  4. One-hot FSM: state encoding never has 0 or >1 bits set
  5. Coverage closure: hit bins unreachable by simulation
Domain Rules
  1. Write properties in concurrent SVA
  2. Group properties by feature in separate .sva files
  3. Constrain environment with assumptions that match valid stimulus
  4. Run vacuity check: assumption disabled → property should NOT hold
  5. Bound liveness properties (##[1:BOUND])
QoR Metrics to Evaluate
  • All properties: PROVEN or clearly UNREACHABLE
  • No vacuous proofs
  • Additional coverage bins closed vs simulation baseline
Output Required
  • SVA property file
  • Formal run report (proven/failed/vacuous per property)
  • CEX waveform descriptions for any failures

Stage: regression_signoff

Regression Tiers
TierTriggerDurationContents
SmokeEvery RTL commit< 30 minP0 directed tests
NightlyEvery night< 8 hrAll directed + 100 random seeds
WeeklyWeekly gate< 48 hrFull suite, 1000 seeds
Sign-offTape-out gateUnlimitedFull suite, 10,000 seeds
Pass Criteria
  • 0 simulation failures (excluding waived known bugs)
  • 0 UVM FATAL or UVM ERROR messages
  • All coverage targets met (see coverage_analysis targets; driven by design_state.constraints.coverage.*)
  • Formal: all P0 properties proven
  • All P0/P1 bugs: closed
Output Required
  • Regression pass/fail report
  • Final merged coverage report
  • Open bug list
  • Sign-off checklist

Constraint Validation

See plugins/meta/skills/pipeline-orchestration/SKILL.md §Constraints Schema for the authoritative schema and stage-entry validation rule.

No required keys for functional verification — all constraints in this domain are optional with schema defaults.

Optional (schema defaults apply when absent):

  • constraints.coverage.functional_pct (default: 100) — functional/V-plan coverage target %
  • constraints.coverage.line_pct (default: 95) — code line coverage target %
  • constraints.coverage.branch_pct (default: 90) — code branch coverage target %
  • constraints.coverage.toggle_pct (default: 85) — toggle coverage target %
  • constraints.coverage.fsm_state_pct (default: 100) — FSM state coverage target %
  • constraints.coverage.fsm_transition_pct (default: 95) — FSM transition coverage target %
  • constraints.coverage.assertion_pct (default: 100) — assertion trigger coverage target %

Tag constraint_ref in history entries when evaluating QoR against these values (e.g. "coverage.functional_pct").


Memory

Write on stage completion

After each stage completes (regardless of whether an orchestrator session is active), write or overwrite one JSON record in memory/verification/experiences.jsonl keyed by run_id. This ensures data is persisted even if the flow is interrupted or called without full orchestrator context.

Use run_id = verification_<YYYYMMDD>_<HHMMSS> (set once at flow start; reuse on each stage update). Set signoff_achieved: false until the final sign-off stage completes.

Run state (write before first stage, update after each stage)

Write memory/verification/run_state.md as the first action before launching any tool:

markdown
run_id:      verification_<YYYYMMDD>_<HHMMSS>
design_name: <design>
tool:        <primary tool>
start_time:  <ISO-8601>
last_stage:  <first stage name>

Update last_stage after each stage completes. This file lets wakeup-loop prompts and resumed sessions identify the correct run without relying on in-memory state. Create the file and parent directories if they do not exist.

Optional: claude-mem index

If mcp__plugin_ecc_memory__add_observations is available in this session, emit each applied fix as an observation to entity chip-design-verification-fixes after writing to experiences.jsonl. Skip silently if the tool is absent — JSONL is the canonical record.

© hdl-tools, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in plugins/verification/skills/functional-verification of hdl-tools/digital-chip-design-agents.

Open the folder on GitHubat commit 38736b1

Compare with similar skills

Functional Verification next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Functional Verification compared with similar skills
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Automated Test Planningtestdouble/han279—~6.7kAutomated safety check: PassMIT
Test Coveragethedaviddias/Front-End-Checklist74k—~407Automated safety check: PassMIT
QA LeadIbrahim-3d/orchestrator-supaconductor380—~2.5kAutomated safety check: PassAGPL-3.0

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Categories

Questions about Functional Verification

What does Functional Verification do?

UVM-based functional verification — testbench architecture, test planning, directed and constrained-random stimulus, functional and code coverage closure, formal assist, and regression sign-off. Functional Verification is an agent skill from hdl-tools/digital-chip-design-agents. UVM-based functional verification — testbench architecture, test planning, directed and constrained-random stimulus, functional and code coverage closure, formal assist, and regression sign-off.

When should I use Functional Verification?

Functional Verification fits situations like: building a UVM testbench; analysing coverage; managing a verification regression.

How do I install Functional Verification in Claude Code?

Run `npx skills add hdl-tools/digital-chip-design-agents --skill functional-verification -a claude-code`. Or copy the skill folder (plugins/verification/skills/functional-verification in hdl-tools/digital-chip-design-agents) into .claude/skills/functional-verification in your project. Claude Code loads it when a task matches its description.

How do I install Functional Verification in Codex?

Run `npx skills add hdl-tools/digital-chip-design-agents --skill functional-verification -a codex`. Or copy the skill folder (plugins/verification/skills/functional-verification in hdl-tools/digital-chip-design-agents) into .agents/skills/functional-verification in your project. Codex loads it when a task matches its description.

Can I use Functional Verification in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add hdl-tools/digital-chip-design-agents --skill functional-verification -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/functional-verification, .gemini/skills/functional-verification, .github/skills/functional-verification and .opencode/skills/functional-verification in your project.

What does Functional Verification need to run?

Going by SKILL.md and its folder, Functional Verification needs the command-line tools its instructions call (pip). Our summary lists: Python 3. Its frontmatter pre-approves these tools: Read, Write, Bash.

Does Functional Verification access the network?

SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Functional Verification safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Functional Verification use?

Functional Verification is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Functional Verification use?

About 4.5k tokens (SKILL.md is roughly 18k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Functional Verification?

Skills that share tags, products or a category with Functional Verification: Designing Tests (CloudAI-X/opencode-workflow, 275 stars), Go Testing (Gentleman-Programming/gentle-ai, 7.6k stars), Automated Test Planning (testdouble/han, 279 stars) and Test Coverage (thedaviddias/Front-End-Checklist, 74k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Functional Verification?

hdl-tools (a GitHub organization) maintains it in hdl-tools/digital-chip-design-agents, which has 211 GitHub stars. The repository holds 17 skills in this directory. The repository was last updated on October 3, 2026.

Source: hdl-tools/digital-chip-design-agents on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.