maintain-formal-verification-skill
Refresh a project's formal checks after covered code changes, distinguish verification drift from product bugs, and retain per-check coverage checkpoints.
Skill by fullerzz
Chalk Labs
Need help deploying maintain-formal-verification-skill?
Chalk Labs sets up agents like this inside real teams: connected to your tools, tested on your data, with a human in the loop where it matters.
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/fullerzz-maintain-formal-verification-skill
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
Get the weekly diff
The weekly diff: one email a week with new agent tools, notable updates and grade changes, plus security alerts for tools you follow. Double opt-in, one-click unsubscribe.
About
Use cases: testing
Reviews
Write a reviewNo reviews yet. Installed it? Reviews come from verified installers only. How reviews work
Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/maintain-formal-verification-skill
What's inside
- Skill name
maintain-formal-verification-skill- Description
- Refresh a project's formal checks after covered code changes, distinguish verification drift from product bugs, and retain per-check coverage checkpoints. Use explicitly for /maintain-formal-verification-skill or auditing whether a model still describes the code.
- License
- MIT
- Instructions
- 48 lines in SKILL.md · view on GitHub
Files (2)
- SKILL.md
- openai.yaml · agents/
SKILL.md (first lines)
# Maintain a formal verification skill A passing check can describe code that no longer exists. Review correspondence before accepting that pass. This workflow maintains project-local model checks, proofs, source verifiers, and explicitly labeled generated-input testing fallbacks; runtime feature maps belong to [maintain-verification-skill](../maintain-verification-skill/SKILL.md). Follow the [native host contract](../z-mode/references/native-hosts.md). Edit only verification artifacts and their skill: models, harnesses, properties, coverage records, and evidence. Preserve invocation policy, unrelated edits, and product code. Product fixes, global installation, commits, publication, messages, and tracker writes need their own authorization. ## Locate and baseline Find checks by what they do, not a fixed skill name; follow links to canonical project files. Respect a named target and enumerate the selected checks. If none exists, direct the user to explicitly invoke [create-formal-verification-skill](../create-formal-verification-skill/SKILL.md); do not bypass its invocation policy. For each check, read its property, covered source paths and symbols, relevant callers, model correspondence, bounds, assumptions, command, tool version, evidence, and last verified commit. A checkpoint records reviewed correspondence, not absence of bugs. Keep checkpoints per check so a runnable check cannot advance a blocked sibling. If a checkpoint is absent, use the last commit changing that check's verification artifacts as a provisional comparison base and disclose the inference; it is not prior verification evidence. If history is missing or that base cannot be resolved, report the gap and review the current correspondence in full before establishing a new checkpoint. Record the current source identity, including staged, unstaged, and relevant untracked files. Run each existing check before editing and retain its output. A stale pass proves only the old description; a failure may be drift, a product bug, or a tool failure. ## Review and update Compare each check's base with the current source, including working-tree changes. Read intervening diffs and commit intent, then trace renamed, moved, or split functions and new callers or writers of guarded state outside the old file list. Classify changes against the actual verification: - **No effect:** the change does not alter covered behavior. - **Drift:** the model or harness no longer describes the code's steps, branches, ranges, or synchronization. - **Intentional promise change:** cite tests, documentation, or other change evidence before revising the property; retain its old and new wording. - **Possible bug:** the promise remains and changed behavior may violate it. Update correspondence from the code before rerunning. Match modeled atomic steps to real indivisible operations or justified synchronization boundaries. Never weaken a property, narrow a bound, or add an assumption merely to get a pass. Explain any necessary bound or assumption change and its lost coverage. ## Rerun and challenge Run the revised checks and interpret their results: - **Pass:** demonstrate property-specific detection with a deliberate real-code mutation in a disposable copy or worktree containing the evaluated source, including relevant uncommitted changes. Never mutate or reset the user's checkout. Follow the generator's [isolated detection procedure](../create-formal-verification-skill/SKILL.md#demonstrate-detection-in-isolation): for a handwritten model, show the code change triggering correspondence review, translate that semantic change into the model, and retain the failing property and manual-translation limits. A model-only failure or drift alarm is not implementation-regression detection. - **Counterexample:** reproduce each step against the real code in an isolated test or live repro, retaining source identity, command, and observed result. A plausible walkthrough or model-only mutation is not reproduction; unavailable reproduction leaves the finding unconfirmed or blocked. Correct and explain impossible model steps, then rerun. A reproduced violation is a product finding; retain the property and describe the harm. Attribute an introducing commit only with evidence, otherwise say it is unknown. Keep any reproduction isolated from product edits. - **Tool failure, timeout, inconclusive result, or missing mutation evidence:** mark the affected check blocked rather than treating process exit or partial output as verification. Retain commands, versions, outputs, correspondence evidence, mutation diffs, and counterexamples outside scratch cleanup. Remove only owned scratch state and processes; rerun the unmutated source and confirm its baseline result is restored. Existing bugs remain visible; they cannot stand in for independent mutation detection when claiming that detection. ## Refresh the record Update coverage, commands, bounds, assumptions, and evidence, then follow the revised skill once as the next agent would. Advance a check's last verified commit only after correspondence review and the required reruns and detection evidence complete. A confirmed bug may accompany a correspondence checkpoint, but record the failing result beside it. A blocked check retains its previous checkpoint, even when another check succeeds. Do not label dirty source as verified at HEAD. Retain the previous commit checkpoint and record the evaluated working-tree diff or digest separately until the exact source is committed and verified. A legacy check without a defensible base remains uncheckpointed until the current-source review and validation complete. ## Report For each check, report **in sync**, **updated**, **bug**, or **blocked**; an updated check may also expose a bug. Include the source identity and history reviewed, old/new checkpoint, verification changes and reasons, rerun and mutation results, findings, and coverage limits. Say when a stale pass became a real counterexample. Distinguish model evidence, implementation evidence, and generated-input testing; none establishes whole-system correctness. Keep verification changes separate from any separately authorized product fix.
From fullerzz/agent-skills, MIT.
More from fullerzz/agent-skills
- 0.603score
herdr-workflow
fullerzz
Use Herdr as the execution layer alongside existing zstack workflows when the user enables it; also handle explicitly requested Herdr agent and pane…
SkillAFreshness: A★ 2Claude Code +9 - 0.600score
ketchup
fullerzz
Catch up on this chat since your last message in plain language, with current PR, CI, and agent status where available and enough context to decide what…
SkillAFreshness: A★ 2Claude Code +9 - 0.595score
create-formal-verification-skill
fullerzz
Generate a reusable project-local skill for model checking or proving code-linked properties.
SkillAFreshness: A★ 2Claude Code +9 - 0.595score
correct
fullerzz
Find repeated agent mistakes in this repository and prevent recurrence through architecture, types, lint or CI, then behavioral tests, with docs last.
SkillAFreshness: A★ 2Claude Code +9 - 0.595score
how
fullerzz
Read-only explanations of how a named code subsystem works: entry points, callers, runtime flow, package ownership, and layering.
SkillAFreshness: A★ 2Claude Code +9 - 0.592score
interrogate
fullerzz
Adversarial review of a requested diff or design using independent reviewers and a synthesized verdict.
SkillAFreshness: A★ 2Claude Code +9 - 0.587score
automate-me
fullerzz
Draft or update a personal mode skill from stated conventions and explicitly scoped history.
SkillAFreshness: A★ 2Claude Code +9 - 0.587score
no-comments
fullerzz
Review unnecessary comments and workarounds with a scoped comment-sicko report.
SkillAFreshness: A★ 2Claude Code +9 - 0.575score
architect
fullerzz
Sketch types, signatures, and module structure before code, then stay in the loop while implementation fills in.
SkillAFreshness: A★ 2Claude Code +9 - 0.575score
create-verification-skill
fullerzz
Generate a project-local verification skill that drives your app the way a user does — any language, framework, or platform.
SkillAFreshness: A★ 2Claude Code +9 - 0.573score
benchmark-checklist
fullerzz
Vet a perf measurement (limiter, tuning, limits, errors, repeatability, relevance, and whether the work happened) before you report or act on it.
SkillAFreshness: A★ 2Claude Code +9 - 0.573score
figure-it-out
fullerzz
Design an auditable playbook when no narrower one fits: a large migration, an ambitious multi-part change, or work a human reviews after stepping away.
SkillAFreshness: A★ 2Claude Code +9
Signals
- Stars
- 2
- Forks
- 0
- Open issues
- 0
- Last commit (this folder)
- Oct 22, 2026 (8 days ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/fullerzz/agent-skills
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
More from fullerzz
- 0.603score
herdr-workflow
fullerzz
Use Herdr as the execution layer alongside existing zstack workflows when the user enables it; also handle explicitly requested Herdr agent and pane…
SkillAFreshness: A★ 2Claude Code +9 - 0.600score
ketchup
fullerzz
Catch up on this chat since your last message in plain language, with current PR, CI, and agent status where available and enough context to decide what…
SkillAFreshness: A★ 2Claude Code +9 - 0.595score
how
fullerzz
Read-only explanations of how a named code subsystem works: entry points, callers, runtime flow, package ownership, and layering.
SkillAFreshness: A★ 2Claude Code +9 - 0.595score
create-formal-verification-skill
fullerzz
Generate a reusable project-local skill for model checking or proving code-linked properties.
SkillAFreshness: A★ 2Claude Code +9 - 0.595score
correct
fullerzz
Find repeated agent mistakes in this repository and prevent recurrence through architecture, types, lint or CI, then behavioral tests, with docs last.
SkillAFreshness: A★ 2Claude Code +9 - 0.592score
interrogate
fullerzz
Adversarial review of a requested diff or design using independent reviewers and a synthesized verdict.
SkillAFreshness: A★ 2Claude Code +9
Done for you
Rather not set it up yourself?
Chalk Labs scopes, builds and runs agents for your team: the task audit, the eval harness, the integrations and the weekly ops report. Fixed scope, priced before we start.
See how we build AI agents →Read the case studies
Chalk Labs is a two-founder AI and growth studio. We build AI agents, CRMs and MVPs for Web3 and AI companies, and have shipped 50+ product and token launches.
- launches shipped
- 50+
- launches shipped
- media placements
- 500+
- media placements
- founders on every account
- 2
- founders on every account
Press we've placed: Forbes · CoinDesk · Decrypt · Cointelegraph · The Block
Rahil Jain, Co-founder, Chalk Labs. Ex-Goldman Sachs engineer. You talk to the person who builds it.
Get maintain-formal-verification-skill running in your business
Tell us what you want to automate. A founder replies within one working day with a plan, not a pitch deck.
Cite this page
maintain-formal-verification-skill. Agent Mart. https://agentmart.dev/skills/fullerzz-maintain-formal-verification-skill (last updated 2026-10-24)