Best fit
Teams often choose channels from generic advice or vanity reach, without proving that current rules, product evidence, attribution, budget, owner availability, and a qualified success gate support the test.
// Marketing & SEO
Channel-Fit Evidence Matrix
Rank evidence-backed distribution tests by buyer intent, rules, attribution, access, budget, and qualified outcome gates.
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Package requirements: SKILL.md-compatible agents that can run a local Python helper. Python 3.10 or newer on Windows, macOS, or Linux using only the standard library. One documented UTF-8 JSON packet with 1 through 500 evidence records and 1 through 100 channel candidates.
You get: Complete evidence-backed channel ranking with READY, REVIEW, or BLOCK gates Aggregate cost and effort allocation for the bounded shortlist
Licence and purchase details
Review the package licence at the destination.
Review the offer and access method at your chosen marketplace. Agensi provides connected-agent access, not a download. Buying a skill does not include an AI-agent subscription or setup service.
Agensi access: This product’s bundled-script workflow through Agensi has not been verified here. Tests of other products do not establish this package’s compatibility. Local setup examples apply to separately obtained downloadable packages.
What problem does Channel-Fit Evidence Matrix solve?
Teams often choose channels from generic advice or vanity reach, without proving that current rules, product evidence, attribution, budget, owner availability, and a qualified success gate support the test.
Use it to
- Choose one or two attributable distribution tests without relying on generic channel advice
- Block a high-reach channel when its rules, offer path, owner participation, or qualified outcome gate are not ready
- Compare marketplaces, directories, communities, owned search, partnerships, outreach, social, and paid acquisition under one evidence contract
- Keep concurrent channel tests inside an explicit cash, effort, and time budget
What it returns
- Complete evidence-backed channel ranking with READY, REVIEW, or BLOCK gates
- Aggregate cost and effort allocation for the bounded shortlist
- Stable findings, stop conditions, evidence links, and qualified outcome gates
- JSON and Markdown reports with deterministic SHA-256 receipts
A representative input and result
input Two individually READY channel tests each reserve $40 against a $50 total budget. The agent-directory test scores 82 and the marketplace test ranks second; both use attributable buyer-behavior gates.
result The agent-directory test is selected. The marketplace remains READY but is not selected because the remaining aggregate budget cannot cover it. The report records the allocation, finding, stop conditions, and deterministic receipt.
Access and approval boundaries
- Terminal permission to run the bundled local Python matrix helper.
- Read access to one user-selected UTF-8 JSON input.
- Write access only to optional user-selected JSON and Markdown report paths.
- No browser, network, credential, account, environment-variable, messaging, payment, or publication permission.
Known limitations
- The matrix evaluates normalized owner-supplied evidence and does not retrieve or authenticate platform rules, analytics, audiences, buyer signals, or product state.
- Platform and community rules must be reverified before consequential action.
- The score is a deterministic comparison aid; READY, REVIEW, and BLOCK are evidence states rather than forecasts.
- Aggregate allocation is greedy by evidence-backed rank and does not solve every possible optimization objective.
- A selected channel is a test candidate, not proof of demand, revenue, retention, profitability, or platform acceptance.
// choose with context
Is Channel-Fit Evidence Matrix the right skill?
It returns
Complete evidence-backed channel ranking with READY, REVIEW, or BLOCK gates. Aggregate cost and effort allocation for the bounded shortlist.
Do not use it as
It does not retrieve current platform rules, analytics, private profiles, or audience data.
Compatibility, access, version, and licence
- Reads or accesses
- Declared local scope: one owner-selected local JSON input, optional owner-selected JSON and Markdown output paths.
- Compatibility
- SKILL.md-compatible agents that can run a local Python helper. Python 3.10 or newer on Windows, macOS, or Linux using only the standard library. One documented UTF-8 JSON packet with 1 through 500 evidence records and 1 through 100 channel candidates.
- Version
- 1.0.0
- Licence
- Review the package licence and seller terms at the linked destination.
Questions
Does it retrieve current platform rules or analytics?
No. You supply normalized evidence records with timestamps and HTTPS references. Reverify important rules before acting.
Can a high score override missing attribution or an unknown rule?
No. Gate status overrides score. REVIEW and BLOCK channels are never selected.
Does it keep several selected tests inside one budget?
Yes. It reserves the larger of each channel's estimate and stop condition, then selects in rank order only while aggregate cash and effort fit.
What counts as a qualified demand gate?
Attributable clicks, buyer replies or questions, checkout openings, and installs. Revenue and retention objectives use their own stricter metrics.
Do views, impressions, reactions, listings, scans, or sent messages count?
No. The matrix accepts them only so it can explicitly block them as vanity outcome gates.
Does READY prove that a channel will work?
No. READY means the supplied evidence clears the documented preconditions for a bounded test.
Does it publish or contact anyone?
No. It produces local reports and never browses profiles, sends messages, spends money, publishes, or controls an account.
Channel-Fit Evidence Matrix keeps proof and approval boundaries visible.
The listing includes the tested package, realistic samples, declared permissions, and known limitations.
Get Channel-Fit Evidence Matrix on Agensi