bug-receipt
Close defects and incidents with a BUG RECEIPT and VERIFIED, PARTIAL, or BLOCKED status after diagnosis, repair, or recovery.
- License 许可
- MIT
- 最近更新
- 2026-11-13
- Stars
- ★ 4.1w
- Forks
- 5.3k
01 前言 Front Matter
- name
- bug-receipt
- metadata
- { "version": "1.4.1" }
- description
- Close defects and incidents with a BUG RECEIPT and VERIFIED, PARTIAL, or BLOCKED status after diagnosis, repair, or recovery.
02 资源文件 4
- 资产文件assets/receipt.template.json573 B
- 参考文档references/receipt-contract.md1.8 KB
- 参考文档references/receipt.schema.json3.5 KB
- 脚本scripts/validate-receipt.mjs7.7 KB
03 SKILL.md 正文
Bug Receipt
Mandatory closeout output
For every bug or incident closeout decision, return the complete receipt below as the entire user-facing result, even when the user requests a concise reply or does not name this format. Concision shortens field values; it never removes or renames a row. Do not replace the receipt with prose.
BUG RECEIPT · VERIFIED | PARTIAL | BLOCKED
Problem <observed defect and intended behavior>
Baseline <failing interaction or command and decisive result; or not run>
Root cause <proven mechanism; or unproven hypothesis>
Change <responsible change; or none>
Proof <supplied or executed check: result; include every decisive layer>
Gaps <none; or exact missing proof and single next experiment/package>
Source executed now | supplied | mixed
Use not run, unproven, or none explicitly. Never omit a row to make the receipt look complete.
Establish the evidence boundary
Before editing, record the observed problem, intended behavior, strongest direct check, and evidence source: executed now, supplied, or mixed. Never imply that supplied evidence was executed in the current run.
Keep evidence privacy-minimal. Redact credentials, tokens, cookies, personal data, private URLs, and sensitive payloads; preserve only the identifiers and excerpts needed to reproduce or correlate the failure.
Reproduce the failure with the narrowest safe check when possible. If reproduction is unavailable, preserve the evidence obtained and cap the result at PARTIAL or BLOCKED.
Trace and repair
- Follow the live owner path from input to symptom.
- Separate observed facts, bounded inferences, and gaps.
- Require a concrete location or runtime transition before naming root cause.
- Make the smallest responsible change; avoid unrelated cleanup, retries, silent fallbacks, and fixture-specific exceptions.
Do not convert a plausible patch, stale log, source read, or passing build into proof of the user-visible behavior.
Close the proof loop
Run only checks required by the affected contract:
- original reproduction or direct acceptance check;
- nearest negative or regression check;
- affected build or integration gate;
- real UI, API, persistence, concurrency, or runtime path when the claim crosses that boundary.
Use these decisive boundaries:
| Surface | Required direct proof |
|---|---|
| Logic or failing test | Original failing input or focused test now passes |
| UI behavior | Real interaction plus relevant console and network observation |
| API or integration | Request, response, and responsible service behavior |
| Persistence | Write/read or reload round trip through the real owner path |
| Race or lifecycle | Repeated concurrent trigger; zero-or-one success; affected-row and transaction evidence; final invariant |
| Cross-system blocker | One sanitized failing request/response with timestamp or request ID, edge and application logs, and identity-provider logs when the trace reaches that owner |
Assign status
VERIFIED: observed baseline, concrete cause, responsible change, all declared checks passed, no material gap.PARTIAL: useful evidence exists, but a required proof layer is missing or inconclusive.BLOCKED: a specific external condition prevents reproduction, repair, or proof.
For PARTIAL or BLOCKED, name the single minimal experiment or correlated evidence package that closes the decisive gap. Never invent a command, observation, count, location, or result.
For a machine-readable receipt or CI integration, read references/receipt-contract.md and conform to its JSON fields, evidence-source marker, compatibility rule, and status invariants.
When a JSON artifact is requested, start from assets/receipt.template.json, write it to a task-owned path, and validate it with node scripts/validate-receipt.mjs <receipt.json> from this skill directory. Do not commit the generated receipt unless the user requests it.
Source and license
Originally published at https://github.com/lMysticl/bug-receipt under the MIT License.
04 同仓库更多 Skills
supabase-rls-policies
Write, review, and test Supabase Row Level Security (RLS) policies so that one user cannot read or change another user's rows. Use when creating or changing tables, policies, views, or security definer functions in a Supabase project, when reviewing a migration that touches RLS, or when asked to prove user isolation with pgTAP tests that sign in as two users.
skill-distribution-audit
Audit a skill collection before release or investigate installer omissions, archived/template leakage, duplicate identities, and manifest drift. Reconcile the intended public source set, distribution manifest, and actual discovery output for a specified consumer version. Includes a deterministic set-comparison helper.
logicapps-custom-code-net10-migration
Migrate Azure Logic Apps Standard local-function custom-code projects from .NET 8 to .NET 10. Use for legacy Logic Apps custom-code workspace upgrades, project/package configuration, custom-worker settings, publication, and integrated VS Code debugging. Not for ordinary .NET applications, standalone Azure Functions, Consumption workflows, or cloud deployment.
winmd-api-search
Find and explore Windows desktop APIs. Use when building features that need platform capabilities — camera, file access, notifications, UI controls, AI/ML, sensors, networking, etc. Discovers the right API for a task and retrieves full type details (methods, properties, events, enumeration values).
resemble-detect
Deepfake detection and media safety — detect AI-generated audio, images, video, and text, trace synthesis sources, apply watermarks, verify speaker identity, and analyze media intelligence using Resemble AI
quality-playbook
Run a complete quality engineering audit on any codebase. Derives behavioral requirements from the code, generates spec-traced functional tests, runs a three-pass code review with regression tests, executes a multi-model spec audit (Council of Three), and produces a consolidated bug report with TDD-verified patches. Finds the 35% of real defects that structural code review alone cannot catch. Works with any language. Trigger on 'quality playbook', 'spec audit', 'Council of Three', 'fitness-to-purpose', or 'coverage theater'.