Hire an AI private bug evidence assistant

Everything it can hand back

Your task sets the actual outputs. The specialist picks the methods that cover them.

• Reviewed redacted copies when local masking tools are available
• A draft issue with environment, steps, expected and actual behaviour, impact and repro limits
• A proofreading checklist and a safe-file list
• Open reproduction questions for the reporter or product owner
• A sharing gate marked PASS, FAIL or NOT RUN

How it does the job well

• Read the existing code, its conventions and its tests before changing anything. Match the project's patterns.
• Make the smallest change that fully solves the request. Keep unrelated refactors out.
• Reproduce a bug before fixing it, and add a test that fails without the fix.
• Treat input from users, files and networks as untrusted. Validate it at the boundary.

Mistakes it avoids

• Claiming code works without running it or its tests.
• Inventing APIs, package versions or settings instead of checking the installed ones.
• Changing behaviour outside the request, such as reformatting whole files or renaming public interfaces.

What a good result looks like

An example, not your task: You need to post a bug in a shared or public tracker and your screenshot has private details in it.

• Draft issue. A Markdown bug report with environment, steps, expected and actual behavior, impact and limits on how sure the repro is.
• Safe-output manifest and status. A list of each safe image copy, how it links to its original, and whether its pixels were reviewed.
• Proofreading checklist. Wording fixes as Original, Suggestion and Reason, with uncertain facts marked [verify].
• Unanswered reproduction questions. Open questions for the reporter or product owner, such as missing expected behavior.
• Sharing gate. A pass, fail or not-run status for the privacy checks before you share the issue.

How the work is checked

• The changed files build, and the relevant tests and linters pass. The commands and results are shown.
• Each change is explained in one line with its reason, and risky areas are named.
• Any check that could not run is reported as NOT_RUN with what it needs.
• States which checks passed, failed or could not run, and names missing inputs.

These checks have not been run against a model yet. Your AI uses them to check its own work.

A second opinion

Before it hands back, your AI reviews the draft the way Kent Beck and Margaret Hamilton would. This is a way of reviewing the work. It does not mean they wrote or endorse it.

Capabilities

• Create separate safe evidence copies. Process originals locally against declared sensitivity. Detect candidate regions, mask a separate copy and inspect output pixels. Pass only reviewed copies and an abstract redaction log to issue drafting.
• Draft the developer handoff. Use safe evidence and bug notes for environment, steps, expected/actual behavior, impact and reproducibility limits. Separate reported from reproduced actions. Remove personal or secret data from quoted logs.
• Check evidence and wording. Proofread the English issue draft for spelling, grammar, terminology, filenames, numbered steps and jargon. Return an Original -> Suggestion -> Reason checklist. Flag factual uncertainty [verify] rather than silently changing it. The coordinator returns the existing draft issue, checklist and unresolved privacy/reproduction checks.

How the role works

1. Checks your task, context and available tools.
2. Picks the methods needed and prepares a work plan.
3. Carries out supported tasks, with each method's limits and review gates.
4. Checks the real deliverables and corrects supported faults.
5. Hands back files, evidence and unresolved gaps.

Your specialist's working setup

Download the base specialist without a task form or account. README.md and SETUP.md explain how to use the folder. SOUL.md defines its scope. INTAKE.md asks missing questions inside your AI. PLAYBOOK.md, SKILLS.md, BEST_PRACTICES.md, CHECKS.md and HANDOFF.md guide the work and its checked deliverables.

Complete associated skills, supporting files and licences are included. Choose only the methods needed for the actual task. Keep context, working files and accepted feedback in your AI project.

Use this specialist through MCP or the API

Call prepare_specialist with role_id prepare-private-bug-evidence-for-developers only. No task is required to load it. Your AI asks what job you need done, what you want back and which relevant context it can use.

For task-led hiring, call find_specialist, explain the suggested specialists and let the user accept or change the team.

Complete ZIP: Download base specialist. API: GET /api/v1/specialists/prepare-private-bug-evidence-for-developers/pack. The host AI supplies its own model, tools and execution; a pack does not connect accounts or run a task.

Inputs and helpful context

The specialist starts from the task brief. Only active methods require their inputs.

For the primary method, when used

• BUG_NOTES: give expected and observed behavior, environment and actions already tried.
• EVIDENCE_FILES: give a local path for original images to local masking tools. In a cloud chat, attach only already-redacted copies; never upload raw sensitive images. Identify any remaining sensitive regions.
• SHARING_BOUNDARY: name intended recipient and public/private issue destination.
• TOOL_SCOPE: state local privacy-mask/Tesseract availability and image-processing permission.

Functions and deliverables

• Private bug evidence assistant: Turn rough bug notes and a screenshot into a developer-ready bug report. You get a privacy checklist and a list of files that are safe to share.
  • Reviewed redacted copies when local masking tools are available
  • A draft issue with environment, steps, expected and actual behaviour, impact and repro limits
  • A proofreading checklist and a safe-file list
  • Open reproduction questions for the reporter or product owner
  • A sharing gate marked PASS, FAIL or NOT RUN
• Screenshot privacy masker: Mask phone numbers, emails, keys and IDs in a screenshot on your own computer. Check the masked copy before you share it.
  • A masked copy of your image, saved as a new file
  • A list of what was found and where
  • A test report you can read before anything is changed
  • A short summary of how many regions were masked
  Source: sickn33.
• Bug report writer: Turn logs, screenshots, or rough notes into a GitHub issue draft with steps and impact.
  • A GitHub issue draft with a one-line summary and steps to repeat
  • Expected and actual results side by side
  • A severity rating with a short reason
  • Placeholders for sensitive details such as project names and user IDs
  • A draft saved as a dated markdown file, if your AI can save files
  Source: sickn33.
• Technical doc proofreader: Share an English technical document. Get specific language fixes and fact-check flags without a full rewrite.
  • A checklist of each issue with the original, a suggestion and the reason
  • Terms flagged where the same idea has more than one name
  • Doubtful facts marked [verify], not changed
  • A count of issues by category
  Source: sickn33.
• AI project context organiser: Share your project and next task. Get a short rules file, a focused file list and a clear handoff for later sessions.
  • A ready rules file for your project
  • A list of which files to load for each task
  • Rules for when to reset or trim the session
  • A checklist for spotting drifting answers
  Source: Addy Osmani.
• Kent Beck: Plan a software change using Kent Beck’s recorded approach. List the cases, take small steps and keep code tidying separate from new behaviour.
  • Your problem worked on the way Kent Beck would: their questions, their method, their push-back
  • The answer or the work, in Kent Beck's voice, using their ideas by name
  • Anything that goes beyond their public record, marked
  • One suggested next step
• Margaret Hamilton: Review a system failure plan using Margaret Hamilton’s recorded approach. Check handoffs, priorities, warnings and what happens when people make mistakes.
  • Your problem worked on the way Margaret Hamilton would: their questions, their method, their push-back
  • The answer or the work, in Margaret Hamilton's voice, using their ideas by name
  • Anything that goes beyond their public record, marked
  • One suggested next step

Tasks you can use it for

• You need to post a bug in a shared or public tracker and your screenshot has private details in it.
• Your bug notes are rough and you want a report a developer can follow.
• You want a record of which images are safe to share and which were never checked.

Limits to check before starting

• You have only a raw screenshot and a cloud chat: mask it on your own computer first, then bring the redacted copy here.
• The evidence is already cleared to share: write the issue straight in your tracker, no privacy review needed.
• A real password, key or token was exposed: send it to your own incident process and rotate it, do not paste it here.
• You want the issue posted for you: it drafts only, and you post it yourself.

Best way to use this role

Give it one clear outcome, the actual files and a definition of done. Keep the work in one AI project so it can reuse checked context and earlier files.

• Download the pack and unzip it. Give the folder to your AI and start with START_HERE.txt.
• Give your bug notes: what you expected, what happened, your setup and what you tried. Say who will read the issue and whether the tracker is public or private.
• Pick one path for the screenshot. On your own computer with a privacy mask tool and Tesseract, give the file path and the AI masks a copy. In a cloud chat or without tools, attach only a copy you already redacted.
• Read the sharing gate. PASS means the checks that ran passed. FAIL means fix it and run again. NOT RUN means a check, such as the pixel review, did not happen, so do not share that image.
• Check the masked copy yourself against the place you will post. Automatic masking is never complete.
• Next, send the draft to the reporter or product owner to answer the open questions. Then post it yourself in the tracker you named. The assistant posts nothing.

Read it as data

Read the complete role setup as JSON. Connect your AI to the AI Blocks MCP library and read aiblocks://roles/prepare-private-bug-evidence-for-developers for its role instructions and pinned sources.

This is a prepared AI role. No task has run. Your AI carries out authorized work using its available tools and reports the actual deliverables and checks.