Write Fix Brief
n1m21n/Infinite
Turn a bug report, review findings, broken-UI screenshot or new-node idea into a verified, file/line-precise implementation prompt after checking the real code.
Hands-off, diff-scoped browser QA of the active branch or PR.
$ npx skills add leo-kuang-ai/spec-first --skill spec-dogfood -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install leo-kuang-ai/spec-first spec-dogfood --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/leo-kuang-ai/spec-first.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/spec-dogfood .claude/skills/spec-dogfood && rm -rf skills-srcUse ~/.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/
Install the "spec-dogfood" agent skill from https://github.com/leo-kuang-ai/spec-first/tree/master/skills/spec-dogfood into .claude/skills/spec-dogfood/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "spec-dogfood", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/leo-kuang-ai/spec-first/tree/master/skills/spec-dogfoodType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add leo-kuang-ai/spec-first --skill spec-dogfood -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install leo-kuang-ai/spec-first spec-dogfood --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/leo-kuang-ai/spec-first.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/spec-dogfood .agents/skills/spec-dogfood && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "spec-dogfood" agent skill from https://github.com/leo-kuang-ai/spec-first/tree/master/skills/spec-dogfood into .agents/skills/spec-dogfood/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "spec-dogfood", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add leo-kuang-ai/spec-first --skill spec-dogfood -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install leo-kuang-ai/spec-first spec-dogfood --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/leo-kuang-ai/spec-first.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/spec-dogfood .cursor/skills/spec-dogfood && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "spec-dogfood" agent skill from https://github.com/leo-kuang-ai/spec-first/tree/master/skills/spec-dogfood into .cursor/skills/spec-dogfood/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "spec-dogfood", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/leo-kuang-ai/spec-first.git --path skills/spec-dogfood--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add leo-kuang-ai/spec-first --skill spec-dogfood -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install leo-kuang-ai/spec-first spec-dogfood --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/leo-kuang-ai/spec-first.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/spec-dogfood .gemini/skills/spec-dogfood && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "spec-dogfood" agent skill from https://github.com/leo-kuang-ai/spec-first/tree/master/skills/spec-dogfood into .gemini/skills/spec-dogfood/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "spec-dogfood", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install leo-kuang-ai/spec-first spec-dogfoodInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add leo-kuang-ai/spec-first --skill spec-dogfood -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/leo-kuang-ai/spec-first.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/spec-dogfood .github/skills/spec-dogfood && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "spec-dogfood" agent skill from https://github.com/leo-kuang-ai/spec-first/tree/master/skills/spec-dogfood into .github/skills/spec-dogfood/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "spec-dogfood", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add leo-kuang-ai/spec-first --skill spec-dogfood -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install leo-kuang-ai/spec-first spec-dogfood --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/leo-kuang-ai/spec-first.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/spec-dogfood .opencode/skills/spec-dogfood && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "spec-dogfood" agent skill from https://github.com/leo-kuang-ai/spec-first/tree/master/skills/spec-dogfood into .opencode/skills/spec-dogfood/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "spec-dogfood", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
spec-dogfoodHands-off, diff-scoped browser QA of the active branch or PR.
Spec Dogfood is an agent skill from leo-kuang-ai/spec-first. Hands-off, diff-scoped browser QA of the active branch or PR. Use when a branch needs autonomous user-flow dogfooding before review or shipping: map changed flows, delegate exact-origin browser execution to spec-test-browser, fix small breakages with regression tests, record human-decision blockers, and write a durable report. Do not use for collaborative UI polish, ordinary browser smoke tests, code review, implementation planning, or broad whole-app exploration.
Its SKILL.md is about 6.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 21 other files, including reference files (for example `evals/cases/owner-unavailable-stops.yaml`, `evals/cases/polish-request-routes-out.yaml` and `evals/cases/r2-code-review-routes-out.yaml`).
It sits in Testing & QA, covering QA and bug reports, UI design and UX design. The repository describes itself as: 仓库原生 AI Coding Harness —— 把一次性 AI 对话变成可治理、可验证、可沉淀的工程闭环 · spec-first.cn. The licence is MIT.
7 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 74655dc. It shows what the files ask for, not the result of running them.
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.
Ships script files (Shell and JavaScript, from the files we listed), which the agent can run.
Shell commands in SKILL.md call:
gitghrailsnpmFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md. Its commands use git, gh and npm, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Spec Dogfood loads about 6.1k tokens when it runs, and up to ~8.1k if it reads all its reference files. Until then it costs about 120 tokens; SKILL.md has 3,069 words of instructions outside code blocks.
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.
The automated check noted patterns worth knowing about, such as sudo or a known installer.
tructions > `package.json` dev script > `.env*` `PORT=` > default `3000`). If a server is already listening on it, reuseAutomated 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.
The full file from leo-kuang-ai/spec-first at commit 74655dc, republished under its MIT licence (© leo-kuang-ai). 3,069 words, ~6,126 tokens.
.claude/skills/spec-dogfood/SKILL.md (or your agent's skills folder). This skill also uses 13 other files; get the full folder from GitHub.Act as a QA engineer who dogfoods the active branch end-to-end: understand every change, test every change in a real browser as a user would, and fix small breakages autonomously until the branch has a clear readiness verdict.
This is diff-scoped, not whole-app exploration. You test what this branch introduced or modified versus the trunk.
Use when a PR, branch, or current non-trunk branch needs autonomous browser dogfooding before review or shipping: changed-flow mapping, persona-aware journey testing, small fixes, regression tests, and a durable report.
Do not use for collaborative UI polish (spec-polish), ordinary browser smoke tests (spec-test-browser when delegated), static code review (spec-code-review), implementation planning (spec-plan), broad whole-app exploration, or large product/architecture decisions. When routing out for one of these, name the listed destination skill explicitly in your reply — recognizing the mismatch without naming where the request belongs leaves the owner without a route.
A PR number, branch name, or current branch; optional --port; git diff against trunk; project dev-server conventions; persona/strategy docs when present; browser observations and test results.
Incrementally updated dogfood report under docs/dogfood-reports/, flowcharts, test matrix statuses, explicitly authorized small source fixes with regression evidence, blocked authorization/human-decision items, and a final readiness verdict.
docs/dogfood-reports/<YYYY-MM-DD>-<branch-slug>-dogfood.md, authorized source/test changes, commits only when separately requested, transient screenshots in OS temp, and optional reusable learnings handed to spec-compound.
Trunk target with no diff, unsafe checkout or dirty working tree, missing spec-test-browser execution owner/capability, missing or failing dev server, external-interaction flows needing human verification, ambiguous fixes requiring human product/architecture decisions, or failing automated suite after browser matrix completion.
Resolve the target branch/PR, optionally isolate with spec-worktree, analyze the diff, map changed user flows, build a matrix, start the app, execute each scenario through spec-test-browser, fix only small unambiguous issues, update the report throughout, then run the automated suite and finalize the verdict.
Human reviewers, spec-code-review, spec-work for larger follow-up fixes, spec-compound for reusable learnings, and PR/commit workflows that consume the readiness evidence.
This workflow never executes a browser CLI directly. Invoke spec-test-browser with mode:pipeline and an explicit exact loopback target-origin:<origin> so its unique wrapper owns capability probing, request-time exact-origin enforcement, action validation, private evidence, and cleanup. Do not use Chrome MCP tools, other browser-control tools, or hand-built browser argv as a second execution path.
bin/dev, rails server, npm run dev, etc.).spec-test-browser Skill is available to own browser execution. Do not probe or execute its private CLI directly; its mode:pipeline call returns the authoritative capability/exact-origin result. If the owner is unavailable, stop with: "Browser execution owner unavailable. Run spec-runtime-setup to inspect browser readiness, then rerun spec-dogfood. This does not block spec-first baseline."spec-dogfood is an orchestrator. Prefer delegating to existing Spec-First skills over re-deriving their behavior:
| When | Skill | Why |
|---|---|---|
| Phase 0 isolation | spec-worktree | Run the dogfood in an isolated worktree so the main checkout stays clean. |
| A failure's root cause is non-obvious | spec-debug | Systematic root-cause analysis instead of guess-and-check. |
| Authorized commit checkpoint | spec-commit | Create a consistent, well-scoped commit only after separate commit authorization. |
| A bug reveals a reusable lesson | spec-compound | Capture the learning so the team compounds knowledge. |
Before isolation/checkout, browser execution, or the first source fix, derive five independent run-local facts from the current user request and any visible upstream handoff:
branch_mutation_authorization: authorized | missing
browser_effect_authorization: authorized | missing
local_fix_authorization: authorized | missing
commit_authorization: authorized | missing
landing_authorization: authorized | missingbranch-selection-is-not-authorization. Branch/worktree mutation requires the current user or upstream owner to explicitly request the exact checkout/isolation action, or the user to approve it after disclosure.read-only | ephemeral-local | durable-local | external | unknown, regardless of whether the triggering action looks like navigation, click, form submit, or a key press. Read-only and ephemeral-local flows may enter the pipeline with synthetic data. Durable-local, external, and unknown flows require separate browser_effect_authorization: authorized; when it is missing, record browser_effect_authorization_missing, do not put the step in a browser test plan, and keep the scenario blocked. Even when such authority exists, use only behavior the spec-test-browser owner admits; its pipeline refusal remains authoritative.local_fix_authorization: authorized only when the current user/upstream explicitly requests applying small fixes; otherwise keep source findings report-only and record fix_authorization_missing.commit_authorization: authorized only when commit creation is separately explicit. A verified fix may remain uncommitted with commit_authorization_missing.landing_authorization: authorized only for an explicit push/PR request. Without landing authorization, do not push and do not open a PR.0. Scope Resolve the target; change checkout only with branch authorization
1. Analyze Diff branch vs trunk, understand every change
2. Map+Matrix Map user flows as Mermaid flowcharts, then derive the test matrix as a task list
3. Serve Detect port and start the caller-owned dev server
4. Execute Work the matrix through spec-test-browser mode:pipeline
5. Fix loop On failure: authorized fix -> regression proof -> optional commit -> continue
6. Report Write durable doc to docs/dogfood-reports/ (flows, matrix, fixes, learnings, verdict)Parse the invocation arguments supplied by the current host: a PR number, a branch name, or blank (use current branch). Preserve quoted paths/tokens while stripping a recognized --port PORT pair if present.
gh pr view <number> --json headRefName,isCrossRepository), but do not reduce it to a bare branch name: a fork PR's head can even be named main/master. Do not check out yet.main/master/the detected default), stop — there is no diff to dogfood. A PR is always diffable (it has a base), so this check never applies to a PR target; never refuse spec-dogfood <number> just because the PR's head branch happens to be named main.spec-worktree own the worktree mechanics. Do not re-derive worktree detection or creation here — spec-worktree handles existing-isolation detection, attaching to a ref, and the "already checked out" constraint, and reports its decision back. The target remains scope until branch mutation is authorized:spec-worktree existing-ref mode through its bundled script: isolate pr:<number> for PR targets, or isolate <branch> for branch targets. It may return already_checked_out branch=<name> path=<path>; use that existing checkout and never switch the primary checkout. On an explicitly authorized in-place switch, use gh pr checkout <number> or git checkout <branch> after dirty-state safety checks. If no blocking interaction exists or the user declines both mutations, stop with branch_mutation_authorization_missing; do not silently switch the primary checkout.docs/dogfood-reports/*-<branch-slug>-dogfood.md (see the branch-slug rule under Resumability). If one is found with unfinished scenarios, ask whether to resume it or start fresh. To resume, re-hydrate the task list from its matrix: Pass/Fixed/Skipped stay done; Pending and in_progress become the remaining auto-runnable work. The three Blocked states are not auto-runnable — Blocked (fix authorization), Blocked (needs human verify), and Blocked (human decision) all wait on a person, so surface them and ask how to proceed rather than silently re-queuing them.This workflow is designed to be interrupted and resumed. Two pieces of state make that safe:
TaskCreate/TaskUpdate on Claude Code, update_plan on Codex, or the equivalent elsewhere) is the live to-do — one task per matrix scenario. Mark each in_progress when you start it and completed only when it genuinely passes.docs/dogfood-reports/<YYYY-MM-DD>-<branch-slug>-dogfood.md is the durable checkpoint that survives across sessions. <branch-slug> is the branch name lowercased with every run of non-alphanumeric characters (slashes included) collapsed to a single - (e.g. feature/Foo_Bar -> feature-foo-bar). Create it as soon as the matrix exists (end of Phase 2) by instantiating references/dogfood-report-template.md (read that template now if you haven't) so the checkpoint carries the template-owned section shape from the start — then fill in every scenario at Pending, and update it incrementally after each scenario judgment and each verified fix/commit-status change, not only at the end. An interrupted run must leave a template-shaped checkpoint, not a bare matrix.Because tasks are session-scoped but the report doc is on disk, the report is the source of truth for resuming. Always keep the two in sync so a later run (or a teammate) can pick up exactly where this one stopped.
Derive the trunk ref once, then pull the full diff against it and read it. Do not hard-code main — a repo whose default branch is master (or anything else) would fail with fatal: ambiguous argument 'main...HEAD'.
# Resolve the trunk to a ref that actually exists. Start from the detected
# default name (origin/HEAD, then gh), then fall back to common names. For each
# candidate prefer a local branch; else use the remote-tracking ref QUALIFIED as
# origin/<branch> — an unqualified name resolves via refs/remotes/<name>, NOT
# refs/remotes/origin/<name>, so a remote-only trunk would otherwise miss. This
# qualification applies to the detected default too (PR/CI checkouts often have
# only origin/main, no local main).
DEFAULT=$(git symbolic-ref --quiet --short refs/remotes/origin/HEAD 2>/dev/null | sed 's@^origin/@@')
DEFAULT=${DEFAULT:-$(gh repo view --json defaultBranchRef -q .defaultBranchRef.name 2>/dev/null)}
TRUNK=""
for cand in "$DEFAULT" main master; do
[ -n "$cand" ] || continue
if git show-ref --verify --quiet "refs/heads/$cand"; then
TRUNK=$cand; break
elif git show-ref --verify --quiet "refs/remotes/origin/$cand"; then
TRUNK="origin/$cand"; break
fi
done
TRUNK=${TRUNK:-main}
git diff --name-only "$TRUNK...HEAD" # what changed
git diff "$TRUNK...HEAD" # how it changedBuild a mental model of every change: new features, modified behavior, new routes/views/components, touched data flows. Note anything that produces user-visible behavior — that is what the matrix must cover.
Ground in the product's personas and vision. Look for persona and vision context so flows can be judged from real users' eyes, not just "does it work." Check, in order: STRATEGY.md (its "Who it's for" section names the primary persona and their job-to-be-done), VISION.md, and any persona docs (e.g. docs/personas/, PERSONAS.md). Capture the 1-3 primary personas and what each cares about. If none exist, infer a reasonable primary persona from the product and the diff, and say so in the report.
Do not jump straight to a flat list of pages. First understand the user flows the diff touches, then derive the matrix from them. A matrix built without a flow model tests pages in isolation and misses the journey — the email that "sends" but lands in the wrong thread.
For every user-visible change, trace the complete journey end to end and draw it. Map each flow as a Mermaid flowchart so the journey is explicit and reviewable before any testing happens — entry point, each user action, branch points (success / validation error / empty / permission-denied), side effects (emails, jobs, notifications), and the true end state.
Email example: it's not enough that "an email sends." Does it go to the right recipient? When the user clicks through, does the app land on and scroll to the right message? Does the content make sense? Does the whole flow align with the product's vision and UX? The flowchart must carry the click-through and its destination, not stop at "email sent."
flowchart TD
A[User opens /threads] --> B[Clicks 'Reply']
B --> C{Form valid?}
C -->|No| D[Inline validation error shown]
C -->|Yes| E[Reply saved]
E --> F[Notification email sent to thread participants]
E --> G[UI scrolls to new reply, focus on it]
F --> H[Recipient clicks email link]
H --> I{Lands on correct thread + scrolls to the reply?}Produce one flowchart per distinct journey, scaled to the diff: a one-route or copy-only change gets a single small flowchart, a multi-step feature gets several. Cover the happy path and the branch points (error, empty, boundary, permission). Mapping the flows before the matrix is never skipped — these diagrams ARE the understanding; they become the spine of the matrix and belong in the final report.
Walk each flowchart and turn every node and branch into one or more test scenarios. Read references/test-matrix-taxonomy.md for the full set of dimensions (journeys, functional checks, experiential checks, edge/error/empty states, accessibility, responsiveness). Cover both functional ("does it work?") and experiential ("does it feel right and align with the product?").
Map changed files to concrete routes (views -> their pages, components -> pages rendering them, layouts -> all pages, stylesheets -> visual regression on key pages) and attach those routes to the flows that exercise them.
Load the matrix as a task list (the harness's task tool, as above), one task per scenario, so progress is tracked and nothing is skipped. Order tasks by flow, following the flowcharts, not by file.
Determine the port (priority: explicit --port > a port explicitly stated in your in-context project instructions > package.json dev script > .env* PORT= > default 3000). If a server is already listening on it, reuse it. Otherwise start the project's dev command (bin/dev, rails server, npm run dev, etc.) in the background and poll the port until it accepts connections. This skill is hands-off, so start the server automatically without asking. Freeze a credential-free exact loopback origin such as http://127.0.0.1:${PORT} for the owner call; do not open the browser here, accept redirects as a replacement origin, or infer browser readiness from the listener alone.
Work the task list one item at a time. For each scenario, mark the task in_progress, then:
Document what you're testing (the journey, expected outcome, exact target origin, and expected-effect classification).
Execute through the owner by invoking spec-test-browser with mode:pipeline, the current branch/PR selector, and target-origin:<exact-loopback-origin>. Supply only routes and expected-safe synthetic interactions derived from the flow. If the owner returns target-origin-*, exact-origin/conformance failure, browser-mutation-authorization-required, cleanup failure, or another not_run / not_supported result, preserve that reason and do not fall back to a direct browser command. Keep transient screenshots in owner-private temp evidence; copy one into the report only when intentionally embedding it.
Judge both correctness and experience: right data, right destination, sensible content, no console errors, and does it feel aligned with the product?
Walk it as each persona. Re-run the journey in your head from each primary persona's perspective (from Phase 1) and ask where they'd feel a paper cut — a small friction that wouldn't fail a functional test but degrades the experience: a confusing label, an extra click, an unexpected jump, a slow-feeling step, missing feedback, copy that doesn't match how that persona thinks. A scenario can be functionally Pass yet still carry paper cuts. Note each paper cut, which persona feels it, and its severity.
Record pass/fail plus any paper cuts, with specifics. Mark the task completed only when it genuinely passes. Paper cuts do not block a Pass, but a sharp paper cut (one severe enough to fix now) is routed into the Phase 5 fix loop just like a failure — apply the same auto-fix-vs-escalate judgment to it. Log the rest in the report.
External-interaction flows (OAuth, real email delivery, payments, SMS) can't be fully driven headlessly — pause, ask the user to verify that leg, and mark the scenario Blocked (needs human verify) until they confirm. Then continue.
When a scenario fails — or a passing scenario carries a sharp paper cut worth fixing now — first decide whether local fix authority exists and whether the change is small enough for this workflow.
Judge the size of the fix before touching code. A fix is eligible only when separately authorized and small, well-understood, and low-risk: a clear bug with an obvious correct fix, contained to a few files, with no schema/architecture/product trade-off. Even with local fix authority, do not implement a large or ambiguous change that requires an architectural, schema, product, or UX decision; record it for a human instead.
For authorized fixes:
When local_fix_authorization: missing, do not touch product source. Record the issue and fix_authorization_missing, mark the scenario Blocked (fix authorization), and continue with other independent scenarios. Do not relabel an unfixed failure as Pass.
spec-debug.commit_authorization: authorized, commit the fix with a clear message using spec-commit, one logical fix per commit. Otherwise leave the verified fix uncommitted, record commit_authorization_missing, and use uncommitted in the report's Commit field.spec-compound.For changes too big to make autonomously: do not implement. Record it in the report's Decisions for a human section with: what's broken, why it's not a safe autonomous fix, the options you see (with trade-offs), and your recommendation. Mark the scenario Blocked (human decision) in the matrix, then continue with the rest. Never make a large, irreversible, or product-altering change just to clear a matrix item.
Keep iterating until every task is completed or in a terminal Blocked state — Blocked (fix authorization), Blocked (human decision), or Blocked (needs human verify). All three wait on a person, so do not re-queue them. Re-test anything a fix might have affected.
Before declaring the branch ready, run the project's automated test suite once (the new regression tests plus everything that already exists). Discover the test command from the project's active instructions and conventions already in your context — do not assume a specific runner. Record the result in the report; a green matrix with a red suite is not "ready."
The report doc was created at the end of Phase 2 and updated incrementally throughout (see Resumability). When the matrix is green (or every remaining item is explicitly blocked), finalize it at docs/dogfood-reports/<YYYY-MM-DD>-<branch-slug>-dogfood.md in the repo under test, then surface a short summary in chat with the file path.
Finalize against references/dogfood-report-template.md — the same template the Phase 2 checkpoint was instantiated from, which owns the required sections and what each must carry. Confirm every template-owned section is present and complete; do not reconstruct the section list from memory, as that drifts from the template. Carry forward the cross-phase obligations this skill produced: the Mermaid flowcharts from Phase 2a, a matrix row per scenario with its commit SHA or uncommitted, each fix's root cause and the regression test added (or why none was meaningful), authorization blockers, paper cuts attributed by persona, learnings worth feeding to spec-compound, and a final readiness verdict that records the Phase 5 automated-suite result. Without landing authorization, do not push and do not open a PR.
© leo-kuang-ai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 13 other files (references) in skills/spec-dogfood of leo-kuang-ai/spec-first.
Open the folder on GitHubat commit 74655dc
Spec Dogfood 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Spec Dogfood this skillleo-kuang-ai/spec-first | 107 | — | ~6.1k | Automated safety check: Notes | MIT | |
| Write Fix Briefn1m21n/Infinite | 271 | — | ~2.9k | Automated safety check: Pass | Custom licence | |
| Feature Verifysd0xdev/sd0x-harness | 192 | — | ~3.2k | Automated safety check: Notes | MIT | |
| Pre-Merge Checkjsmastery-pro/skills | 1.5k | — | ~1.1k | Automated safety check: Notes | MIT | |
| Actionbook Web Testactionbook/actionbook | 1.6k | — | ~9.7k | Automated safety check: Pass | Apache-2.0 | |
| Fix The Classjoetawil7/first-pass | 98 | — | ~1.5k | Automated safety check: Pass | MIT |
n1m21n/Infinite
Turn a bug report, review findings, broken-UI screenshot or new-node idea into a verified, file/line-precise implementation prompt after checking the real code.
sd0xdev/sd0x-harness
Feature verification (READ-ONLY, P0-P5). An agent skill from sd0xdev/sd0x-harness.
jsmastery-pro/skills
A gate before merge: verify runs the real app against the spec, and review has a different model do a senior code review, without editing code.
actionbook/actionbook
Run browser-based web tests against websites using Actionbook CLI.
joetawil7/first-pass
Bug-fix routine that fixes the whole class of bug, not just the reported instance.
madebyaris/spec-kit-command-cursor
Compare implementation against specifications, identify gaps and issues.
leo-kuang-ai/spec-first
Audit mobile App PRD/Figma/local-source consistency across page routes, KMP/Clean Architecture, components, analytics, i18n, engineering quality, and industry lenses before runtime validation; use…
leo-kuang-ai/spec-first
Create a durable cross-session handoff or resume from a user-selected continuity source.
leo-kuang-ai/spec-first
Give a decisive, project-grounded verdict on an external input — judged against the current project, not in the abstract.
leo-kuang-ai/spec-first
Resolve PR review feedback by evaluating validity and fixing issues with conflict-aware resolver dispatch.
leo-kuang-ai/spec-first
Analyze explicit Riffrec product-feedback captures, including riffrec-.zip, the Riffrec session.json + events.json + recording.webm + voice.webm bundle, or media/notes the user identifies as a…
leo-kuang-ai/spec-first
Document a recently solved problem or durable project vocabulary in docs/solutions/ or CONCEPTS.md.
Categories
Hands-off, diff-scoped browser QA of the active branch or PR. Spec Dogfood is an agent skill from leo-kuang-ai/spec-first. Hands-off, diff-scoped browser QA of the active branch or PR.
Spec Dogfood fits situations like: A branch needs autonomous user-flow dogfooding before review; shipping: map changed flows; delegate exact-origin browser execution to spec-test-browser; fix small breakages with regression tests.
Run `npx skills add leo-kuang-ai/spec-first --skill spec-dogfood -a claude-code`. Or copy the skill folder (skills/spec-dogfood in leo-kuang-ai/spec-first) into .claude/skills/spec-dogfood in your project. Claude Code loads it when a task matches its description.
Run `npx skills add leo-kuang-ai/spec-first --skill spec-dogfood -a codex`. Or copy the skill folder (skills/spec-dogfood in leo-kuang-ai/spec-first) into .agents/skills/spec-dogfood in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add leo-kuang-ai/spec-first --skill spec-dogfood -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/spec-dogfood, .gemini/skills/spec-dogfood, .github/skills/spec-dogfood and .opencode/skills/spec-dogfood in your project.
Going by SKILL.md and its folder, Spec Dogfood needs a shell and JavaScript for the scripts in its folder and the command-line tools its instructions call (git, gh, rails and npm). Our summary lists: Node.js; A Bash shell.
SKILL.md contains no URLs. Its commands use git, gh and npm, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found notes only (mentions a .env file), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.
Spec Dogfood is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 6.1k tokens (SKILL.md is roughly 25k 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 2k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Spec Dogfood: Write Fix Brief (n1m21n/Infinite, 271 stars), Feature Verify (sd0xdev/sd0x-harness, 192 stars), Pre-Merge Check (jsmastery-pro/skills, 1.5k stars) and Actionbook Web Test (actionbook/actionbook, 1.6k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
leo-kuang-ai (a GitHub user) maintains it in leo-kuang-ai/spec-first, which has 107 GitHub stars. The repository holds 35 skills in this directory. The repository was last updated on October 8, 2026.
Source: leo-kuang-ai/spec-first on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.