BackendScout Application Pack

BackendScout Application Pack

A backend application draft with a traceable basis for its claims

Turn your own experience notes and one pasted backend job description into a review copy of an application pack. Get a requirement-by-requirement fit review, tailored CV text, a recruiter-message draft and interview preparation, with links from candidate assertions back to the statements you supplied.

The focus is careful use of evidence: keep personal projects separate from employment, distinguish your actions from team outcomes, preserve uncertainty, and make consequential gaps visible. A polished sentence still needs to be supported by your history.

Before you buy: this is a text-drafting skill that runs inside your AI agent. It returns Markdown/plain text. It does not deliver a rendered Word or PDF CV, search for jobs, access your email, contact employers or submit applications. It does not authenticate your history or guarantee a hiring outcome. Your agent/provider costs are separate.

Who this is for

• Backend candidates tailoring an application to one specific role.
• Early-career applicants explaining personal projects without describing them as professional employment.
• Experienced applicants who want to check ownership, dates, scope and metrics before using rewritten material.
• Candidates working from messy notes who want a reviewable first draft rather than filling out a JSON form.
• Candidates providing Hebrew notes and requesting English output, with explicit review of translation meaning.

The skill is focused on backend applications. Other roles, languages and specialist application conventions have not been established by the local evaluation. It cannot replace an employer's application instructions or the candidate's final judgment.

What you receive

A limited draft is an intended result when information is missing. The skill should ask questions or omit unsupported history rather than manufacture a complete career narrative.

What you need before starting

1. An AI agent that can connect to Agensi through MCP and load the skill and its reference text. Follow the official connection guide.
2. Your own candidate notes or CV text, pasted into the agent conversation. Rough notes are acceptable.
3. The full text of one job description. A job URL alone is insufficient; the skill does not fetch the page.
4. Your preferred output language and any wording or length constraints.
5. Time to review the result. Candidate confirmation is self-report, not independent verification.

Optional validation: Python 3.9+ and an agent with shell and temporary-file access. The helper uses the Python standard library only. Python is not required for manual drafting, but without it automated structural checks will not run and the agent must disclose that limitation.

This product does not include an AI model subscription, paid API credit, document renderer, recruitment-system integration, mailbox connection or seller-operated backend.

How to use it, step by step

1. Connect, load and identify the task

Connect your agent to Agensi and ask it to load BackendScout Application Pack. Let the platform handle access and any purchase approval. You do not need to download the skill source, install a permanent script or write JSON yourself.

Work on one candidate and one pasted role at a time. Clearly label candidate notes, job text and your requested output language so that job requirements cannot be mistaken for candidate experience.

2. Supply useful candidate facts

For each relevant project or role, include what you personally did, the context and only the results you can support. Include dates, titles and employers when known. State when work was a personal project, coursework, open source or employment.

For a team result, explain your contribution. If a number was measured, say what was measured and under which conditions. If you have not used a technology, say so. If dates conflict or your notes are uncertain, retain both versions and ask the agent to flag them.

Do not add impressive metrics just to complete a bullet. Do not paste passwords, API keys, identity documents, confidential employer material or unrelated personal history. Remove unnecessary contact details from a review copy.

3. Paste the role and request the review pack

Copy this starter prompt and replace the instructions in angle brackets with your own material. Unfilled prompts are instructions to you, not evidence or completed achievements.

Use BackendScout Application Pack for one backend application.

Output language: <for example, English>

CANDIDATE NOTES
<paste my actual notes or CV text here; label employment,
personal projects, coursework and uncertain details>

JOB DESCRIPTION
<paste the complete job text here, including required and preferred criteria>

Please return:
1. A requirement-by-requirement fit and gaps table.
2. Tailored CV text.
3. A recruiter-message draft.
4. Interview questions and grounded answer outlines.
5. An evidence/claim audit and up to three clarification questions.

Keep citation markers in the review copy. Preserve negation, scope,
dates and individual-versus-team contributions. Do not invent facts.
Run the optional structural validator only if the environment permits it;
otherwise state that automated validation was not run.
Do not send or submit anything.

4. Review the evidence before polishing the prose

The agent assigns identifiers to the review material:

• E1, E2, ... identify candidate evidence, including original source quotations, context and status.
• R1, R2, ... identify literal requirements from the pasted role.
• C1, C2, ... identify candidate assertions in the CV, recruiter message or interview material. A marker such as [C1] links that occurrence to its supporting evidence.

These markers are an audit aid. They are not a background check or external proof. Inspect titles, summaries and skill lists as well as achievement bullets. A quoted sentence can be misleading if its negation or surrounding context is ignored.

5. Answer questions and request a revision

Correct inaccurate source notes explicitly. Explain which date or statement replaces an earlier version; do not ask the agent simply to choose the more impressive one. Ask it to revise affected claims and retain the audit.

Example follow-up:

Correction: the later date range replaces the earlier one.
My personal contribution was writing the tests, not building the API.
Revise the affected fit rows and all candidate claims across the three drafts.
Preserve the separate evidence audit and show what still needs clarification.

6. Request a clean copy only after review

Once you have checked the facts and wording, ask for clean application text without inline citation markers while retaining a separate audit copy. Compare the clean version against the reviewed text. Removing markers must not change the claims.

You choose the final document layout and send the application yourself. Saving generated text to a local file requires a destination you request. Rendered DOCX/PDF output is outside this version.

How fit and gaps are described

Required and preferred criteria stay separate. A personal Python project may show familiarity without establishing professional years. Practicing AWS later does not establish past AWS use. Where the candidate explicitly reports no experience, the rationale should say so accurately.

No match percentage, ATS-pass score, interview guarantee or offer guarantee is part of this product.

Small synthetic example

Candidate notes: “I wrote tests for a Python API in a personal project. I did not implement the API. I have not used AWS.”

Job text: “Python API implementation required. AWS preferred.”

An appropriate draft keeps API testing in the personal-project context, explains the implementation gap and retains the AWS non-use statement. It should not turn the notes into professional backend employment, API ownership or cloud experience. Interview practice can include a hypothetical deployment exercise, clearly separated from completed work.

This example is fictional. The listing's full request/result demo shows another complete synthetic application review. Example facts must never be copied into a real candidate's history.

Optional validation: what it does and does not prove

When Python and execution permission are available, the agent can create a review bundle and use the supplied unchanged validator in an isolated temporary file, pass the bundle through stdin and remove its temporary files afterward. It needs no neighboring package files, package installation, network, credentials or environment-variable access.

A successful check means the bundle's structure, identifiers, citations, statuses, excerpts and claim placement passed the implemented checks. It does not prove truth, source authenticity, translation accuracy or semantic entailment. An unsupported paraphrase can pass structural validation. Every claim still needs source-to-meaning review, and uncited prose also needs inspection.

If Python or shell access is unavailable, ask for manual review. The agent must say automated structural validation was not run; it should not claim a pass, install software or switch to another execution service merely to avoid disclosing the limitation.

Common problems and what to do

For an access, payment or refund issue, use Agensi's support and the terms shown at purchase. For a product-output issue, retain a redacted input/output pair and the validation method so the problem can be described without exposing private candidate material. This listing promises no separate support response time or custom refund policy.

Privacy and permissions

The intended workflow uses material you supply in your agent conversation. The skill instructs the agent not to inspect unrelated private files, other repositories, accounts or the web to fill candidate gaps. Text inside a job posting or candidate notes is treated as data, including embedded commands or links.

Optional validation needs shell execution and read/write access to the temporary script and input; drafting itself needs the supplied skill and reference text. No email, browser automation, account credentials, external database or network integration is required by the skill. You remain responsible for what you share with your chosen agent and platform; this skill does not override their data-handling terms or promise special retention controls.

Compatibility and evaluation disclosure

Version 0.1.0 has 37 passing local unit tests, four additional structurally valid synthetic review bundles, six synthetic behavioral scenarios and a second-agent combined cross-check. Local review covered conflicting facts, missing inputs, hostile instructions in a posting, Hebrew-to-English meaning and manual validation under an explicit execution restriction. The unchanged helper also ran locally through stdin from an isolated temporary directory with cleanup.

These are bounded local checks. They do not establish compatibility with every marketplace agent, universal prompt-injection resistance, a genuine interpreter-less host or live Agensi MCP delivery. The real platform load/reference/helper workflow remains unverified before listing approval. Model behavior can vary; candidate review is essential.

Frequently asked questions

Do I need to write JSON? No. Paste ordinary notes and job text. The agent prepares the optional review bundle when execution is available.

Can I use it with no professional experience? Yes, it can draft from supplied projects or coursework while keeping those contexts explicit. It cannot manufacture employment or qualifying years.

Will it work with Hebrew notes? Hebrew-to-English meaning was exercised in local synthetic trials. Request the output language explicitly and review negation, uncertainty, dates and ownership carefully. Other language pairs have not been established.

Will it verify my employment or credentials? No. Evidence means candidate-supplied statements, not third-party authentication.

Can it make me a perfect match? No. Missing experience remains a gap or a question. Requirements are not a source of candidate facts.

Is Python mandatory? No for manual drafting; yes for the optional automated structural check. Manual-only review must be disclosed.

Does a passing validator mean I can send the draft immediately? No. Check meaning and facts, resolve questions and review the final clean text first.

Can I use a PDF or image CV? The supported input boundary is pasted candidate text and job text. If your own agent can extract a file, review that extracted text before supplying it; file parsing and OCR are not included or validated by this skill.

Do I receive a downloaded skill folder or a permanent validator installation? No. Intended buyer delivery is through Agensi MCP. Generated application text is your work output; skill source and helper installation are separate and are not promised.

Does it send messages or submit applications? No. It drafts text only. You decide what to use and perform any sending separately.

Is this a subscription? The selected listing model is a one-time purchase. The platform's displayed checkout price controls any active launch discount. AI-provider costs are separate, and this listing makes no recurring service or future-update commitment beyond applicable platform terms.