Agent skill

Qv Quality Reporting

by tetherto in tetherto/qvac

Run the deterministic code-quality audit, turn related findings into contextual remediation groups, prepare approval-gated Asana proposals, reconcile recurring runs, or configure twice-monthly…

Apache-2.0Auto-check passedDevelopment

Install Qv Quality Reporting

skills CLI
$ npx skills add tetherto/qvac --skill qv-quality-reporting -a claude-code

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

GitHub CLI
$ gh skill install tetherto/qvac qv-quality-reporting --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/tetherto/qvac.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/qv-quality-reporting .claude/skills/qv-quality-reporting && 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
qv-quality-reporting
GitHub stars
683
Token cost
~1.6k tokens
SKILL.md length
760 words
Files
7 (incl. scripts, references)
Skills in repo
50
Repo updated
First seen
Licence
Apache-2.0

At a glance

Run the deterministic code-quality audit, turn related findings into contextual remediation groups, prepare approval-gated Asana proposals, reconcile recurring runs, or configure twice-monthly…

  • Works in 9 steps: Run pnpm quality:reporting from the… → Stop if the command fails, any detector… → Read .quality/triage.json, → …
  • Tasks that involve Proposals and quotes
  • SKILL.md covers Default Workflow, Review Shape, Priority Judgment and Recurring Runs, plus 1 more section
  • Runs JavaScript scripts from its folder; calls node and pnpm

What it does

Qv Quality Reporting is an agent skill from tetherto/qvac. Run the deterministic code-quality audit, turn related findings into contextual remediation groups, prepare approval-gated Asana proposals, reconcile recurring runs, or configure twice-monthly quality reporting.

Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 9 other files, including scripts and reference files (for example `agents/openai.yaml`, `references/scheduling.md` and `references/ticket-template.md`).

It sits in Development, covering Proposals and quotes and Code quality. It works with Asana. The repository describes itself as: Open-source local AI SDK - run AI on-device with no cloud, no API keys. Supports GGUF, RAG, image, music, and video generation, speech-to-text, P2P inference, and more… The licence is Apache-2.0.

When your agent uses it

  • Tasks that involve Proposals and quotes
  • Tasks that involve Code quality

Example prompts

  • “/qv-quality-reporting”

Requirements

  • Node.js

Workflow steps

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

  1. Run pnpm quality:reporting from the repository root.
  2. Stop if the command fails, any detector reports diagnostics, or resolutions
  3. Read .quality/triage.json,
  4. Form cohesive remediation groups. Start with deterministic candidates, then
  5. Assign ownership and P1-P4 priority using the config plus evidence in the
  6. Build the review batch using references/ticket-template.md
  7. Reconcile each provisional page read-only with
  8. End the initial run by asking the user which proposal IDs to approve. Do not
  9. After the user explicitly approves IDs, rerun the helper for only that subset

What it can do on your machine

Read from SKILL.md and the folder at commit f874b3a. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 2 files in scripts/ (JavaScript), which the agent can run.

    Shell commands in SKILL.md call:

    • node
    • pnpm

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

  • Network

    No URLs in SKILL.md. Its commands use pnpm, 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

Qv Quality Reporting loads about 1.6k tokens when it runs, and up to ~3.2k if it reads all its reference files. Until then it costs about 58 tokens; SKILL.md has 760 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~58
When it runs · the whole SKILL.md, loaded when a task matches
~1.6k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3.2k

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 passed

The automated check found no risky patterns in SKILL.md.

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); the scripts in this folder are not scanned.

SKILL.md

The full file from tetherto/qvac at commit f874b3a, republished under its Apache-2.0 licence (© tetherto). 760 words, ~1,592 tokens.

Download SKILL.mdSave it as .claude/skills/qv-quality-reporting/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.
name
qv-quality-reporting
description
Run the deterministic code-quality audit, turn related findings into contextual remediation groups, prepare approval-gated Asana proposals, reconcile recurring runs, or configure twice-monthly quality reporting.

Quality Reporting

Turn the repository's deterministic quality evidence into a short, actionable review. This skill owns contextual grouping and triage; it does not replace the underlying findings or mutate Asana without explicit approval.

Default Workflow

  1. Run pnpm quality:reporting from the repository root.

  2. Stop if the command fails, any detector reports diagnostics, or resolutions are withheld. Explain the incomplete evidence instead of proposing progress.

  3. Read .quality/triage.json, references/triage-config.json, and only the source or architecture files needed to understand the leading candidates.

  4. Form cohesive remediation groups. Start with deterministic candidates, then:

    • combine candidates only when they share one root cause and fit one focused PR;
    • split a candidate when it contains independent remediations;
    • preserve every covered finding fingerprint;
    • apply approved merge/split overrides from the config before new judgment.
  5. Assign ownership and P1-P4 priority using the config plus evidence in the triage report. Never derive priority from finding count alone.

  6. Build the review batch using references/ticket-template.md:

    • On the initial backlog review, begin with the deterministically ranked candidates.
    • On recurring runs, examine all new, worsened changed, and resolved lifecycle evidence before unchanged existing debt, regardless of severity. Skip improved-only and unchanged groups unless the user asks for backlog review.
    • Generate provisional proposals in pages, reconcile each page read-only, discard exact-existing and unchanged outcomes, and continue until ten actionable proposals remain or candidates are exhausted. Ambiguous matches count as actionable decisions. Never cap the source candidates before this backfill step. Write the final machine batch to .quality/proposals.json. Retain unreviewed candidates for later pages; do not discard them.
  7. Reconcile each provisional page read-only with:

    bash
    node .agents/skills/qv-quality-reporting/scripts/asana-quality.mjs \
      --proposals .quality/proposals.json \
      --report .quality/triage.json

    Suppress exact duplicate creations and unchanged actions, surface ambiguous matches, and show the resulting final batch in chat as a numbered, concise review. If local Asana access is unavailable, label the proposals unreconciled and do not apply them until reconciliation succeeds.

  8. End the initial run by asking the user which proposal IDs to approve. Do not use --apply, even if the user previously authorized analysis.

  9. After the user explicitly approves IDs, rerun the helper for only that subset without --apply and show the exact actions. Stop if the match is ambiguous or the result differs materially from what the user approved. Otherwise run:

    bash
    node .agents/skills/qv-quality-reporting/scripts/asana-quality.mjs \
      --proposals .quality/proposals.json \
      --report .quality/triage.json \
      --approve <comma-separated-proposal-ids> \
      --apply

    Never infer approval for proposals the user did not name.

Review Shape

For each proposal, show:

  • proposal ID, P1-P4 priority, primary team, and collaborators;
  • a two-to-four sentence explanation of what is wrong and where;
  • one focused remediation boundary;
  • compact evidence: paths, rules, severity, threshold excess, and Git activity;
  • lifecycle action: create, comment on a materially worsened group, or review an apparent resolution.

Prefer source links in chat when a concrete line is available. Keep the deterministic finding list in .quality/report.md as the evidence trail.

Show full SKILL.md (307 more words)Show less

Priority Judgment

  • P1: evidenced current correctness, initialization, security, or release risk on a critical path. A high-severity smell alone is not P1.
  • P2: material maintainability risk in important or frequently changed production code, especially when several findings describe one root cause.
  • P3: bounded, credible debt worth scheduling without urgent operational risk.
  • P4: opportunistic cleanup, low-churn auxiliary code, or weak evidence.

State the evidence that justifies the band. If importance or ownership is uncertain, say so instead of inventing certainty.

Recurring Runs

pnpm quality:reporting compares the current findings with the previous successful run in .quality/previous-run-baseline.json. When no snapshot exists, the first run uses the committed audit baseline. The command advances the local snapshot only after complete analysis and successful triage. Do not delete it between recurring runs.

Reconcile by stable group marker before proposing work:

  • new actionable group with no match: propose ticket creation;
  • materially worse existing group: use changes[].direction and the retained before/after values to justify a proposed comment on its existing ticket;
  • improved-only drift: do not describe it as a regression;
  • apparently resolved group: show a review notice, never complete a task;
  • unchanged group: stay silent;
  • ambiguous match: request a decision and do not mutate Asana.

The helper records only the group action and deterministic evidence hash in .quality/reporting-state.json. This ignored local checkpoint prevents the same ticket creation, regression comment, or resolution notice from being proposed repeatedly. It contains no Asana IDs or private links. Do not delete it between recurring runs.

Use references/scheduling.md only when the user asks to create or modify the twice-monthly automation.

Feedback and Configuration

When the user corrects ownership, importance, or group cohesion, apply that feedback to the current review. Offer a minimal update to references/triage-config.json only when it expresses a reusable repository rule. Do not store task IDs, private Asana links, people, tokens, or transient ticket state in repository files.

© tetherto, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 6 other files (scripts, references) in .agents/skills/qv-quality-reporting of tetherto/qvac.

  • SKILL.md
  • agents/openai.yaml
  • references/scheduling.md
  • references/ticket-template.md
  • references/triage-config.json
  • scripts/asana-quality.mjs
  • scripts/asana-quality.test.mjs

Open the folder on GitHubat commit f874b3a

Compare with similar skills

Qv Quality Reporting 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.

Qv Quality Reporting compared with similar skills
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Qv Quality Reporting this skilltetherto/qvac683—~1.6kAutomated safety check: PassApache-2.0
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Self Reviewverl-project/verl-omni1.2k—~2kAutomated safety check: PassApache-2.0
Dev Rfcpproenca/dot-skills215—~3.8kAutomated safety check: PassMIT

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Works with

Questions about Qv Quality Reporting

What does Qv Quality Reporting do?

Run the deterministic code-quality audit, turn related findings into contextual remediation groups, prepare approval-gated Asana proposals, reconcile recurring runs, or configure twice-monthly…. Qv Quality Reporting is an agent skill from tetherto/qvac. Run the deterministic code-quality audit, turn related findings into contextual remediation groups, prepare approval-gated Asana proposals, reconcile recurring runs, or configure twice-monthly quality reporting.

When should I use Qv Quality Reporting?

Qv Quality Reporting fits situations like: tasks that involve Proposals and quotes; tasks that involve Code quality.

How do I install Qv Quality Reporting in Claude Code?

Run `npx skills add tetherto/qvac --skill qv-quality-reporting -a claude-code`. Or copy the skill folder (.agents/skills/qv-quality-reporting in tetherto/qvac) into .claude/skills/qv-quality-reporting in your project. Claude Code loads it when a task matches its description.

How do I install Qv Quality Reporting in Codex?

Run `npx skills add tetherto/qvac --skill qv-quality-reporting -a codex`. Or copy the skill folder (.agents/skills/qv-quality-reporting in tetherto/qvac) into .agents/skills/qv-quality-reporting in your project. Codex loads it when a task matches its description.

Can I use Qv Quality Reporting 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 tetherto/qvac --skill qv-quality-reporting -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/qv-quality-reporting, .gemini/skills/qv-quality-reporting, .github/skills/qv-quality-reporting and .opencode/skills/qv-quality-reporting in your project.

What does Qv Quality Reporting need to run?

Going by SKILL.md and its folder, Qv Quality Reporting needs JavaScript for the scripts in its folder and the command-line tools its instructions call (node and pnpm). Our summary lists: Node.js.

Does Qv Quality Reporting access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Qv Quality Reporting safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Qv Quality Reporting use?

Qv Quality Reporting is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Qv Quality Reporting use?

About 1.6k tokens (SKILL.md is roughly 6.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.6k tokens, read only when the agent opens those files.

What are the alternatives to Qv Quality Reporting?

Skills that share tags, products or a category with Qv Quality Reporting: LobeHub Alint Rule Set Maintenance (lobehub/lobehub, 83k stars), Plan Arbiter (BuilderIO/skills, 4.6k stars), RAG Code Review (lyonzin/knowledge-rag, 292 stars) and Self Review (verl-project/verl-omni, 1.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Qv Quality Reporting?

tetherto (a GitHub organization) maintains it in tetherto/qvac, which has 683 GitHub stars. The repository holds 50 skills in this directory. The repository was last updated on October 9, 2026.

Source: tetherto/qvac on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.