Agent skill

Acceptance Demo Generator

by Chachamaru127 in Chachamaru127/claude-code-harness

Renders a single HTML page showing each acceptance criterion as verified or not, with a ship, wait, or reject recommendation for non-engineers.

MITAuto-check: notesProduct & Project Management

SKILL.md written in Japanese; this summary is our English description.

Install Acceptance Demo Generator

skills CLI
$ npx skills add Chachamaru127/claude-code-harness --skill harness-accept -a claude-code

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

GitHub CLI
$ gh skill install Chachamaru127/claude-code-harness harness-accept --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/Chachamaru127/claude-code-harness.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/harness-accept .claude/skills/harness-accept && 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
harness-accept
GitHub stars
3.2k
Token cost
~3.4k tokens
SKILL.md length
818 words
Files
3 (incl. references)
Skills in repo
25
Repo updated
First seen
Licence
MIT

At a glance

Renders a single HTML page showing each acceptance criterion as verified or not, with a ship, wait, or reject recommendation for non-engineers.

  • Works in 10 steps: project name と user_request_hash を解決 → harness-mem を project-only で検索し、Plan… → (alt): cross-project search (Phase… → …
  • Deciding whether to ship, wait on, or reject a completed task
  • SKILL.md covers Quick Reference, 責任境界, 入力 and 出力, plus 5 more sections
  • Calls bash and git

What it does

This skill reads back acceptance criteria stored earlier by a companion plan-brief skill, joined by a hash of the original request, and never writes a decision itself, since recording the final accept or reject call belongs to a separate script. It only ever searches the current project, never crossing into others.

It calculates its recommendation from the ratio of verified to total criteria against two thresholds, rounding a ship down to a wait when pending validations remain unresolved or when an optional blind-evaluator judges the result unconvincing, and it records the literal numbers behind each recommendation alongside the reasoning in the output.

When your agent uses it

  • Deciding whether to ship, wait on, or reject a completed task
  • Presenting acceptance criteria results to a non-engineer stakeholder
  • Reviewing verified-versus-pending criteria before a release decision

Example prompts

  • “Generate an acceptance demo for the feature we just finished.”
  • “Show me a ship or wait recommendation for this task.”
  • “Build a one-page HTML acceptance review for the client.”

Requirements

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

Workflow steps

10 steps, taken from the step headings in SKILL.md.

  1. project name と user_request_hash を解決
  2. harness-mem を project-only で検索し、Plan Brief 側 record を取得 (default)
  3. (alt): cross-project search (Phase 65.3.5 opt-in)
  4. 過去の問題パターンを取得 (Phase 65.2.2 委譲)
  5. verified_criteria を artifact から組み立てる (Phase 134.4)
  6. 5: blind evaluation (optional, Phase 137.2)
  7. recommendation を算出する
  8. HTML を生成する
  9. ブラウザで自動 open する
  10. ユーザー判断待ち

What it can do on your machine

Read from SKILL.md and the folder at commit 2b2b748. 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
    • Edit
    • Bash

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • bash
    • git

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

  • Network

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

Acceptance Demo Generator loads about 3.4k tokens when it runs, and up to ~6.2k if it reads all its reference files. Until then it costs about 154 tokens; SKILL.md has 818 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~154
When it runs · the whole SKILL.md, loaded when a task matches
~3.4k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~6.2k

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: 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, Edit, 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 Chachamaru127/claude-code-harness at commit 2b2b748, republished under its MIT licence (© Chachamaru127). 818 words, ~3,431 tokens.

Download SKILL.mdSave it as .claude/skills/harness-accept/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
harness-accept
description
Generate an Acceptance Demo HTML for non-engineer vibecoders right before ship/wait/reject decision. Reads back the acceptance_criteria that were stored as personal-preference.v1 by harness-plan-brief (joined by user_request_hash), then renders a single-file HTML showing each criterion as verified or unverified along with a ship/wait/reject recommendation. Use when the user asks for an acceptance review, wants to decide whether to ship a delivered task, or says: acceptance demo, accept demo, 受け入れ判断, 受入レビュー, ship/wait/reject 判定, 検収レビュー. Do NOT load for: implementation, code review, release work.
allowed-tools
Read, Write, Edit, Bash
description-en
Generate an Acceptance Demo HTML for non-engineer vibecoders right before ship/wait/reject decision. Reads back the acceptance_criteria that were stored as…
description-ja
実装完了直後の受け入れ判断 (ship / wait / reject) 前に Acceptance Demo HTML を生成する。harness-plan-brief が `personal-preference.v1` で書き込んだ acceptance_criteria を…
argument-hint
[task-description]
user-invocable
true

harness-accept

非エンジニアの発注者・プロデューサー職向けに、実装完了タスクの受け入れ判断 (ship / wait / reject) を HTML 1 枚 で提示するスキル。 発注者の認知負荷ピーク (3) 受け入れ判断の段階で使う。

Phase 65.1.x (harness-plan-brief) の対構造として動作し、Plan Brief で承認した acceptance_criteria を read 側で取り戻して評価する。

Quick Reference

  • 「Acceptance Demo を作って」 → このスキル
  • 「受け入れ判断したい」 → このスキル
  • 「ship/wait/reject 判定」 → このスキル

責任境界

範囲このスキルの責務
検索現プロジェクトのみ (project: <current>, strict_project: true を必ず指定)
クロスプロジェクトやらない (Phase 65.3 以降で --cross-project-group <name> flag で opt-in 解放)
Plan Brief 連携user_request_hash を join key として personal-preference.v1 (Phase 65.1.4) を read
書き込みやらない (Acceptance 承認後の memory write は accept-record-decision.sh の責務)
recommendation 算出verified / 全 criteria の比率で 0.8 / 0.5 閾値判定。ロジックは scripts/render-html.sh 直前で計算

入力

引数 [task-description] にユーザーの request を渡す (Plan Brief 時と同じ文を使う)。 引数なしの場合は会話と Plan Brief の原依頼を読み取りで回収する。hash の照合に使うため原文を言い換えない。対象が確定しない時だけ確認する。

出力

出力パス形式
Acceptance Demo HTML.claude/state/views/accept-<timestamp>.html単独で開ける HTML (no server, no JS framework)
Acceptance context JSON.claude/state/views/accept-<timestamp>.context.jsonacceptance-context.v1 schema

Schema: acceptance-context.v1

json
{
  "schema": "acceptance-context.v1",
  "user_request": "string",
  "user_request_hash": "sha256 hex (Plan Brief 側の personal-preference.v1 と join)",
  "demo_artifacts": [
    { "kind": "video|screenshot|text", "path": "string" }
  ],
  "verified_criteria": [
    { "name": "string", "passed": true, "evidence": "string" }
  ],
  "tdd_verified": "yes|no|not-required|skip:<reason>",
  "unverified_caveats": ["string"],
  "past_issue_patterns": [
    { "pattern_id": "P5", "title": "string", "verified_in_current_task": true }
  ],
  "recommendation": "ship|wait|reject",
  "recommendation_evidence": ["string"],
  "project": "string",
  "generated_at": "ISO8601",
  "blind_evaluation": {
    "applicable": true,
    "eligibility_reason": "persuasive-doc|functional-skip|not_applicable|unavailable",
    "audience_purpose_line": "string",
    "evaluator_believable": "believable|not_believable|uncertain",
    "evaluator_useful": "useful|not_useful|uncertain",
    "evaluator_friction_points": ["string"],
    "internal_recommendation": "ship|wait|reject",
    "divergence": "none|internal_high_evaluator_low|internal_low_evaluator_high",
    "divergence_notes": "string"
  }
}

blind_evaluation は Phase 137.2 で追加した optional field (additive、既存 consumer は無視してよい)。 詳細は references/blind-evaluator.md を参照。

完全 schema は schemas/acceptance-context.v1.schema.json を参照。

Recommendation 算出ロジック

verified_count    = count of verified_criteria where passed=true
total_criteria    = count of verified_criteria
pending_count     = count of criteria whose evidence が "pending_validations: " prefix を持つ (Step 4 の記入規約)
ratio             = verified_count / total_criteria  (total=0 のときは 0)

  total = 0    → "reject" (criteria 0 件は判定不能、安全側 reject)
  ratio >= 0.8 → base = "ship"
  ratio >= 0.5 → base = "wait"
  ratio <  0.5 → base = "reject"

  # pending 補正 (Phase 134.4): 検証待ちの criteria が残っている限り ship にしない
  pending_count >= 1 かつ base == "ship" → interim = "wait" (ship から丸める)
  それ以外                                 → interim = base

  # blind_evaluation 補正 (Phase 137.2): fresh fork 評価者が「信じられない/役に立たない」と
  # 判定し、かつ内側の recommendation が依然 ship なら wait に丸める。reject へはさらに丸めない
  blind_evaluation.applicable == true
    かつ blind_evaluation.divergence == "internal_high_evaluator_low"
    かつ interim == "ship"
    → recommendation = "wait"
  それ以外
    → recommendation = interim

評価根拠は recommendation_evidence に literal な数値で残す。 例: "verified 4 件 / 全 5 件 (80%) → ship 閾値以上" pending 補正が効いた場合は "pending_validations 該当 criteria N 件が未解消のため ship を wait に丸めた" のように理由を明記する。 blind_evaluation 補正が効いた場合は "blind evaluator が believable=not_believable / useful=not_useful と判定したため ship を wait に丸めた" のように理由を明記する。 アルゴリズムと Eligibility (説得系/文書系のみ適用、機能系は functional-skip で skip) の詳細は references/blind-evaluator.md 参照。

Execution Flow

スキル起動時、Claude は以下の手順で動作する。

Step 1: project name と user_request_hash を解決
bash
PROJECT_NAME="$(basename "$(git rev-parse --show-toplevel)")"
USER_REQUEST_HASH="$(printf '%s' "$USER_REQUEST" | sha256sum | awk '{print $1}')"

PROJECT_NAME が空 (git 外) の場合は current をデフォルトに使う。

Step 2: harness-mem を project-only で検索し、Plan Brief 側 record を取得 (default)

引数に --cross-project-group <name> flag がない場合 (default behavior):

mcp__harness__harness_mem_search を以下のパラメータで呼び出す:

project: <PROJECT_NAME>
strict_project: true
tags: ["personal-preference", "plan-brief-approval"]
limit: 10

重要: project パラメータは必須。strict_project: true を指定し、cross-project な検索は絶対に行わない。

取得した record を data.user_request_hash == <USER_REQUEST_HASH> でフィルタし、最も新しい 1 件を選ぶ。 これが Plan Brief 時の承認内容 (chosen_option / acceptance_criteria 等) を保持している。

Step 2 (alt): cross-project search (Phase 65.3.5 opt-in)

引数に --cross-project-group <name> flag がある場合のみ、横断 group 内の他プロジェクトでの 類似 plan-brief-approval / acceptance-decision 履歴を取得する (D43 Option α):

bash
MEMBERS_JSON="$(bash scripts/load-cross-project-groups.sh --group "<name>" 2>/dev/null)" || {
  echo "ERROR: cross-project group not found: <name>" >&2
  exit 1
}

MEMBERS_JSON が [] の場合は default の単一 project search に fallback。

MEMBERS_JSON が非空の場合、各 member project ごとに MCP search を 1 回発行:

for each project in MEMBERS_JSON:
  mcp__harness__harness_mem_search(
    project: <member>,
    strict_project: true,
    tags: ["personal-preference", "plan-brief-approval"],
    limit: 10
  )

結果を client 側でマージし、data.user_request_hash == <USER_REQUEST_HASH> でフィルタ。 hash 一致は基本的に同一 user request 由来のため複数 project での重複は稀だが、念のため id 単位で dedupe。

cross-project 由来の record を採用すると過去他案件の chosen_option / acceptance_criteria が混入する 可能性があるため、HTML 出力時は --with-redaction flag を必ず使用 すること:

bash
bash scripts/render-html.sh --template accept ... --with-redaction

詳細は .claude/rules/cross-repo-handoff.md の「Phase 65.3 実装決定事項 (D43)」を参照。

Step 3: 過去の問題パターンを取得 (Phase 65.2.2 委譲)
bash
bash scripts/accept-past-issues.sh --project "$PROJECT_NAME" --task "$USER_REQUEST" > "$PAST_ISSUES_JSON"

このスクリプトは patterns.md (P1-P33) と過去の acceptance-context.v1 record を semantic search し、 最大 3 件の past-issue.v1 を返す。各々 verified_in_current_task: bool 付き。

Step 4: verified_criteria を artifact から組み立てる (Phase 134.4)

まず scripts/accept-collect-evidence.sh <task-id> (read-only) を実行し、accept-evidence.v1 を取得する:

bash
EVIDENCE_JSON="$(bash scripts/accept-collect-evidence.sh "$TASK_ID")"

これは 4 artifact (.claude/state/review/<task-id>.worker-report.json / .claude/state/review-result.json [task.id 一致時のみ採用] / <task-id>.runtime-review.json / <task-id>.browser-result.json) を読み、 各 artifact の present / reason / data と pending_validations (Reviewer が積んだ未解消レイヤー) を正規化して返す。

原則: artifact から引用する。新規主張を作らない。 Plan Brief 時の acceptance_criteria 各項目について、 EVIDENCE_JSON の 4 artifact の中から該当する記述を探し、その内容を evidence にそのまま転記する。 Claude が artifact に無い「動作確認した」を独自に主張することは禁止。 Worker の「完了した」という自己申告だけでは criterion を合格にしない。対象 task と現在の成果物に対応するコマンド結果、実測、参照箇所があるか確認する。 この評価のために勝手に実装や公開へ進まず、不足する証拠は未検証として残す。

各 criterion の判定:

  • artifact 内に該当する検証結果があり合格 → passed: true、evidence に artifact の該当箇所を引用する
  • 該当 artifact が欠損 (present: false) か、EVIDENCE_JSON.pending_validations に該当 layer がある場合 → passed: false。 evidence には次の literal prefix で実状態を転記する (pending_count の機械カウントに使う規約):
    • pending 由来: "pending_validations: " + <該当 pending_validations[].reason>
    • artifact 欠損由来: "artifact missing: " + <path> + " (" + <reason> + ")"
  • 上記いずれの場合も、同じ内容を unverified_caveats に 1 行追記する

evidence が空文字列の場合、HTML 上で警告表示される (DoD c、既存動作)。

demo_artifacts への video 合流 (Phase 134.6): EVIDENCE_JSON.demo_artifacts (browser-review-runner.sh が拾った playwright screencast の {kind:"video", path}) を、コンテキスト JSON の demo_artifacts 配列に 追記する。ただし path はプロジェクトルート相対のまま転記してはならない — 出力先 HTML は .claude/state/views/accept-<timestamp>.html にあり、<video src> はブラウザが HTML のある場所 (.claude/state/views/) を基点に解決するため、../../../ を前置してプロジェクトルートから そこへ辿れる相対パスに書き換えてから追記する (例: test-results/x/trace.webm → ../../../test-results/x/trace.webm)。accept.html.template は kind=="video" のエントリだけ <video> 埋め込みで表示する (それ以外の kind は従来通りテキスト表示のまま)。

TDD が必要な task では、Acceptance Demo に TDD verified: yes|no の 1 行を必ず出す。 TDD 不要または skip の場合は TDD verified: not-required または TDD verified: skip:<reason> と表示する。 yes にできるのは .claude/state/tdd-red-log/<task-id>.jsonl の Red 証跡、または literal failing test output が確認できる時だけ。

Show full SKILL.md (274 more words)Show less
Step 4.5: blind evaluation (optional, Phase 137.2)

Eligibility を確認する: 成果物の主目的が「説得すること・読ませて理解させること」の文書系 (提案書・レポート・README 等) なら適用、「動くこと」の機能系 (コード・設定・スキーマ等) なら skip する。詳細な Eligibility 表と手順は references/blind-evaluator.md 参照。

  • 機能系タスク → skip: blind_evaluation = { applicable: false, eligibility_reason: "functional-skip" } として Step 5 に進む (DoD b)。評価者は起動しない。
  • 説得系/文書系 → 適用: Task tool で fresh sub-agent (subagent_type: general-purpose) を起動し、 依頼文 + 成果物 + 読者像 1 行のみを渡す (verified_criteria / recommendation の閾値 / 過去の 判定は渡さない)。Judge Prompt Template で「信じられるか / 役に立つか / 引っかかった箇所」の 3点を受け取り、blind_evaluation を組み立てる。
  • Task tool が使えない環境: eligibility_reason: "unavailable" とし fake の結果を作らない。
Step 5: recommendation を算出する

上記「Recommendation 算出ロジック」に従って ship / wait / reject を決定する。 Step 4.5 で blind_evaluation を適用した場合、divergence による補正もここで適用する。

Step 6: HTML を生成する

scripts/render-html.sh (Phase 65.1.1) を templates/html/accept.html.template で呼ぶ:

bash
bash scripts/render-html.sh \
  --template accept \
  --data "$CONTEXT_JSON" \
  --out "$HTML_OUT"
Step 7: ブラウザで自動 open する

scripts/plan-brief-open.sh (Phase 65.1.2 で導入された 汎用 OS dispatcher) を再利用:

bash
bash scripts/plan-brief-open.sh "$HTML_OUT"

注: スクリプト名に「plan-brief」が入っているが、実体は OS 別 browser open dispatcher で kind 中立。 Phase 65.1.2 で先に導入されたため historical name。Layer 3 (HTML 直前最終 scan) 等の他用途でも再利用される。 BROWSER=true の env が設定されている場合 (CI 環境)、open は skip され printf で path だけ出力する。

Step 8: ユーザー判断待ち

「ship / wait / reject の recommendation を採用するか、override するか」を確認する。 判断後の memory write は別スキル (accept-record-decision.sh、Phase 65.2.3) の責務。

失敗時の挙動

失敗挙動
mcp__harness__harness_mem_search 不達警告を表示し、verified_criteria を空配列で続行 (recommendation = reject)
Plan Brief 側 record が見つからないwarning を出し、verified_criteria を空配列で続行
git rev-parse --show-toplevel 失敗PROJECT_NAME=current で続行
accept-past-issues.sh 失敗past_issue_patterns: [] で続行 (best-effort)
render-html.sh 失敗エラーを stderr に出力し exit 1
  • harness-plan-brief (Phase 65.1.2) — 計画段階の対構造スキル。本スキルは Plan Brief 時の personal-preference.v1 を user_request_hash で join して read
  • scripts/accept-collect-evidence.sh (Phase 134.4) — 4 artifact (worker-report / review-result / runtime-review / browser-result) を読み accept-evidence.v1 を返す read-only script (Step 4 で使用)
  • references/blind-evaluator.md (Phase 137.2) — Step 4.5 の optional blind evaluation。説得系/文書系のみ適用、機能系は skip。blind-judge.md から設計原則を継承しつつ Output Contract と recommendation への反映は独立に定義
  • scripts/accept-past-issues.sh (Phase 65.2.2) — 過去の問題パターン取得 (read side)
  • scripts/accept-record-decision.sh (Phase 65.2.3) — 承認 memory write (acceptance-decision.v1)
  • scripts/render-html.sh (Phase 65.1.1) — HTML テンプレートエンジン
  • scripts/plan-brief-open.sh (Phase 65.1.2) — 汎用 OS browser dispatcher
  • harness-progress skill (Phase 65.4.1) — 進行管理スキル (3 surface のうち真ん中)

© Chachamaru127, MIT. 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 2 other files (references) in skills/harness-accept of Chachamaru127/claude-code-harness.

  • SKILL.md
  • references/blind-evaluator.md
  • schemas/acceptance-context.v1.schema.json

Open the folder on GitHubat commit 2b2b748

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    A skill your agent uses when validating a MeshLLM release candidate or current HEAD against the last GitHub release, assembling the canonical feature/fix/modification inventory, testing locally…

    3.5k GitHub stars~2.6k tokensUpdated today
    Product & Project ManagementAuto-check passed
  • Final Release Review

    openai/openai-agents-python

    Official

    Assess a Python SDK release candidate or release plan against the previous release and recommend ship or block.

    30k GitHub stars~5.4k tokensUpdated today
    Product & Project ManagementAuto-check passed
  • Final Release Review

    openai/openai-agents-js

    Official

    Assess a JS SDK release candidate or release plan against the previous release and recommend ship or block.

    3.9k GitHub stars~4k tokensUpdated today
    Product & Project ManagementAuto-check passed
  • Schematic

    blader/schematic

    Reverse engineer a detailed product and technical specification document from a git branch's implementation.

    240 GitHub stars~2.2k tokensUpdated 7 mo ago
    Product & Project ManagementAuto-check passed
  • Create Release Checklist

    quarto-dev/quarto-r

    Create a release checklist and GitHub issue for an R package.

    160 GitHub starsUsed in 1 repo~1.9k tokens
    Product & Project ManagementAuto-check: notes

More from Chachamaru127/claude-code-harness

All 25 skills in this repo
  • CI Failure Triage and Repair

    Chachamaru127/claude-code-harness

    Diagnoses failing CI pipelines and tests, deciding first whether the test or the implementation is at fault, and hands hard cases to a dedicated fixer subagent.

    3.2k GitHub starsUsed in 1 repo~1.1k tokens
    Auto-check: notes
  • Cursor Composer Task Delegate

    Chachamaru127/claude-code-harness

    Hands one implementation task to Cursor Composer in an isolated git worktree, then reviews its diff and cherry-picks the result into the main branch.

    3.2k GitHub stars~4.4k tokensUpdated 4 days ago
    Auto-check: notes
  • Harness Long-Running Task Loop

    Chachamaru127/claude-code-harness

    Repeats a long task as a series of scheduled wake-ups, each re-entering with fresh context and calling harness-work for one task per cycle.

    3.2k GitHub stars~2.3k tokensUpdated 4 days ago
    Auto-check: notes
  • Harness Plan

    Chachamaru127/claude-code-harness

    Creates and maintains Plans.md task plans with a spec delta, updates task markers and syncs plan progress with the implementation.

    3.2k GitHub stars~3.7k tokensUpdated 4 days ago
    Auto-check: notes
  • Harness Release

    Chachamaru127/claude-code-harness

    Runs a release for any project that keeps a Keep a Changelog file on GitHub, from version bump to merge, tag and GitHub Release after a single approval.

    3.2k GitHub stars~4.4k tokensUpdated 4 days ago
    Auto-check: notes
  • Harness Review Dispatcher

    Chachamaru127/claude-code-harness

    Routes a review request to the right mode and reference file in the claude-code-harness project: code, plan or scope review, a quick closeout, a dual Claude-and-Codex opinion, or a security-only pass.

    3.2k GitHub stars~3.6k tokensUpdated 4 days ago
    Auto-check: notes

Questions about Acceptance Demo Generator

What does Acceptance Demo Generator do?

Renders a single HTML page showing each acceptance criterion as verified or not, with a ship, wait, or reject recommendation for non-engineers. This skill reads back acceptance criteria stored earlier by a companion plan-brief skill, joined by a hash of the original request, and never writes a decision itself, since recording the final accept or reject call belongs to a separate script. It only ever searches the current project, never crossing into others.

When should I use Acceptance Demo Generator?

Acceptance Demo Generator fits situations like: deciding whether to ship, wait on, or reject a completed task; presenting acceptance criteria results to a non-engineer stakeholder; reviewing verified-versus-pending criteria before a release decision.

How do I install Acceptance Demo Generator in Claude Code?

Run `npx skills add Chachamaru127/claude-code-harness --skill harness-accept -a claude-code`. Or copy the skill folder (skills/harness-accept in Chachamaru127/claude-code-harness) into .claude/skills/harness-accept in your project. Claude Code loads it when a task matches its description.

How do I install Acceptance Demo Generator in Codex?

Run `npx skills add Chachamaru127/claude-code-harness --skill harness-accept -a codex`. Or copy the skill folder (skills/harness-accept in Chachamaru127/claude-code-harness) into .agents/skills/harness-accept in your project. Codex loads it when a task matches its description.

Can I use Acceptance Demo Generator 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 Chachamaru127/claude-code-harness --skill harness-accept -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/harness-accept, .gemini/skills/harness-accept, .github/skills/harness-accept and .opencode/skills/harness-accept in your project.

What does Acceptance Demo Generator need to run?

Going by SKILL.md and its folder, Acceptance Demo Generator needs the command-line tools its instructions call (bash and git). Its frontmatter pre-approves these tools: Read, Write, Edit, Bash.

Does Acceptance Demo Generator access the network?

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

Is Acceptance Demo Generator 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 Acceptance Demo Generator use?

Acceptance Demo Generator is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Acceptance Demo Generator use?

About 3.4k tokens (SKILL.md is roughly 14k 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 2.7k tokens, read only when the agent opens those files.

What are the alternatives to Acceptance Demo Generator?

Skills that share tags, products or a category with Acceptance Demo Generator: .NET MAUI Release Readiness (dotnet/maui, 23k stars), Release Validation (Mesh-LLM/mesh-llm, 3.5k stars), Final Release Review (openai/openai-agents-python, 30k stars) and Final Release Review (openai/openai-agents-js, 3.9k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Acceptance Demo Generator?

Chachamaru127 (a GitHub user) maintains it in Chachamaru127/claude-code-harness, which has 3,158 GitHub stars. The repository holds 25 skills in this directory. The repository was last updated on October 5, 2026.

Source: Chachamaru127/claude-code-harness on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.