Best fit
You have a target role and career facts you permit the agent to use, and want traceable resume and cover-letter drafts to review.
// HR & Recruiting
Evidence-Bound Resume Builder
Prepare a role-specific resume and matching cover-letter draft from your permitted career facts, with excluded claims and shared provenance.
Prefer Gumroad? Get Evidence-Bound Resume Builder for $12 USD on Gumroad. Review checkout for any applicable tax or currency conversion.
Format: Agent skill, not a browser app. Agensi offers agent-connected access; Gumroad offers a downloadable package. You need an AI agent that can load its instructions and use the required tools.
Package requirements: Python 3.10+ and access to the required supporting files. Agensi supplies these through its agent connector; the separate Gumroad download uses the local package setup. The helper uses the standard library. Tested locally on Windows and, in a bounded synthetic test, through Agensi in Claude with Python 3.13.16.
You get: Resume and cover-letter drafts, source provenance, requirement coverage and questions for your review.
Licence and purchase details
JustHandled Labs paid skill license; included LICENSE.txt governs
Review access and purchase details at your chosen marketplace. Buying a skill does not include an AI-agent subscription or setup service.
Agensi access: On October 24, 2026, package 1.1.2 passed 15 synthetic controls in Claude through Agensi creator access. The retrieved SKILL.md, helper and input contract match sealed source after host transcription. This tests the stdin helper workflow, not buyer checkout, installed-package use or other hosts. Read the compatibility evidence. Local setup examples apply to the separate downloadable package.
Your first useful result
Choose your access route first. With Agensi, ask your connected agent to load the skill and its input contract, then guide you through a fictional example. With the separate Gumroad download, use the package-folder steps below. Neither route is a standalone resume website or includes an AI-agent subscription.
Gumroad download: run the included example
- Prepare. Extract the whole package, keeping scripts, references and fixtures together. You need Python 3.10 or newer. Open a terminal in that package folder; the example below creates a new output folder there.
- Run from the package folder.
python scripts/run.py fixtures/practice.json --output first-resume-practice - Review the output. Expect REVIEW, not a failure: two supported statements appear in resume-draft.txt and cover-letter-draft.txt. The invented “Increased revenue by 73%” claim is excluded, and missing budget-leadership evidence is flagged. Read candidate-questions.md and the provenance files before changing the example.
The helper assembles approved source statements; it does not independently verify your career or produce a finished page design. Your agent may process prompts remotely. Review its privacy settings before supplying personal information. Use a new output folder for each run; its parent folder must already exist.
Included synthetic example re-run on Windows with Python 3.12.14. This verifies the local helper, not automatic installation in every agent host.
Agensi or an agent-loaded package: copy this starting prompt
Read this package’s SKILL.md and references/input-contract.md. Help me prepare a draft for my target role using only sources I explicitly permit. Preserve volunteer labels, caveats and negation. Ask me to approve the source units, create the required input packet, then run the helper into a new output folder. Return both drafts and the unresolved questions. Do not invent facts, contact anyone or submit anything.
Make your experience clear. Keep it yours.
28 seconds · illustrative animation · mechanical sound effects, no narration. On a small screen, use fullscreen or read the example below.
Read the film transcript
A supplied reporting-assistant role asks for Python reporting and budget leadership. Volunteer Python reporting experience supports the first requirement. A proposed draft adds “Increased revenue by 73%,” but that claim is absent from the permitted source. The helper excludes it. Before and after views retain the volunteer statement with its source locator and remove the unsupported metric. Budget leadership becomes a question for the candidate. A cover-letter draft is an additional output. Review both drafts; supplied records are not independent career verification.
A readable version of the example
Permitted source:
I built Python reporting tools for our weekly volunteer rota.
Proposed addition, excluded: Increased revenue by 73%.
Retained: the supplied volunteer experience, linked to notes:line-2. No revenue figure is added.
Still unresolved: evidence for budget leadership. The helper returns REVIEW, produces two supported statements in each draft, excludes c2 and flags r2. The film illustrates one retained statement; the complete outputs include both. It does not design the finished resume page.
Use it to
- For an applicant tailoring to a supplied role, prepare resume and cover-letter drafts from one permitted fact set so unsupported claims are excluded from both, with a shared provenance file.
- Prepare your next application or practice session using your own approved facts.
- Return to a saved packet without losing earlier work.
What it returns
Two editable drafts, plus the questions, coverage and source trail needed to review them.
See every output file
- application-provenance.csv
- application-review.md
- candidate-questions.md
- claim-provenance.csv
- cover-letter-draft.md
- cover-letter-draft.txt
- findings.csv
- manifest.json
- original-resume.txt
- report.md
- requirement-coverage.csv
- resume-draft.md
- resume-draft.txt
- summary.json
A representative input and result
Supplied volunteer reporting experience reaches the cover letter. An unsupported revenue claim does not.
Inspect the complete fictional input and draft
input {
"original_resume": "I built Python reporting tools for our weekly volunteer rota.\nI checked the rota against availability notes and documented the corrections for the coordinator.",
"job_description": "Python reporting role",
"sources": [
{
"id": "s1",
"text": "I built Python reporting tools for our weekly volunteer rota.",
"locator": "notes:line-2",
"permitted": true
},
{
"id": "s2",
"text": "I checked the rota against availability notes and documented the corrections for the coordinator.",
"locator": "volunteer-notes:4",
"permitted": true
}
],
"claims": [
{
"id": "c1",
"text": "I built Python reporting tools for our weekly volunteer rota.",
"source_id": "s1",
"requirement_ids": [
"r1"
]
},
{
"id": "c2",
"text": "Increased revenue by 73%.",
"source_id": "s1",
"requirement_ids": [
"r1"
]
},
{
"id": "c3",
"text": "I checked the rota against availability notes and documented the corrections for the coordinator.",
"source_id": "s2",
"requirement_ids": [
"r1"
]
}
],
"requirements": [
{
"id": "r1",
"text": "Python"
},
{
"id": "r2",
"text": "Budget leadership"
}
],
"application": {
"role": "Reporting assistant",
"company": "Example Community Centre",
"job_locator": "supplied-job:1",
"claim_ids": [
"c1",
"c2",
"c3"
]
}
}
result Dear hiring team,
I am applying for the Reporting assistant role at Example Community Centre.
I built Python reporting tools for our weekly volunteer rota.
I checked the rota against availability notes and documented the corrections for the coordinator.
Thank you for considering my application.
Access and approval boundaries
- Read one local input JSON file.
- Write a new local output directory; no network or external actions.
Known limitations
- Extractive source-unit assembly; candidate and agent improve presentation without inventing facts.
- No automatic career verification, ATS score, PDF resume designer or application submission.
- Role/company and fact relevance are supplied, not independently authenticated.
Questions
What do I receive?
An agent skill with a Python helper, input contract, synthetic fixtures, tests and sample reports. Agensi provides agent-connected access; Gumroad provides the separate downloadable package.
What do I need to run it?
Python 3.10 or newer and your authorized normalized JSON records. The helper uses the standard library; Windows CLI execution was tested.
Does it connect to my HR software?
No. You supply a local export normalized to the documented schema. There is no ATS or HRIS integration.
Will it apply, contact employers or judge my suitability?
No. It helps you prepare your own materials or practice. It never sends or submits, infers sensitive traits or predicts job outcomes.
What does READY mean?
No encoded finding in the supplied scope. It does not prove source truth, comprehensive readiness, legal compliance or approval to act.
What are the main limits?
Extractive source-unit assembly; candidate and agent improve presentation without inventing facts.
Where does my data go?
Where processing happens depends on your route. With the downloadable package, the Python helper runs on your computer. With Agensi, execution and prompts use your chosen agent host and its privacy settings. Start with fictional data; authorize any processing of real career information.
Evidence-Bound Resume Builder keeps proof and approval boundaries visible.
The listing includes the tested package, realistic samples, declared permissions, and known limitations.
Available on Agensi for $12, one-time purchase.
Get it on Agensi — $12// choose with context
Is Evidence-Bound Resume Builder the right skill?
It returns
Resume and cover-letter drafts, source provenance, requirement coverage and questions for your review.
Do not use it as
Inventing career facts, guaranteeing ATS results or submitting applications automatically.
Compatibility, access, version, and licence
- Reads or accesses
- Read one local input JSON file. Write a new local output directory; no network or external actions.
- Compatibility
- Python 3.10+ and access to the required supporting files. Agensi supplies these through its agent connector; the separate Gumroad download uses the local package setup. The helper uses the standard library. Tested locally on Windows and, in a bounded synthetic test, through Agensi in Claude with Python 3.13.16.
- Version
- Agensi: package 1.1.2 (platform release 1.2). Gumroad download: 1.1.2. Checked October 24, 2026.
- Licence
- JustHandled Labs paid skill license; included LICENSE.txt governs