The front door. Everything downstream is faster than the thinking that should precede it, which is why most bad investigations are not collection failures — they are framing failures. You can run forty tools against a name and produce a confident dossier on the wrong person. The work here is deciding what question you are answering, what would count as an answer, and what would prove you wrong.

Used across every skill in this repo, defined only here.

Read ../../ETHICS.md, then write down five things:

If you cannot state who authorized this and on what basis, stop. Not "proceed carefully" — stop. An unauthorized investigation cannot be fixed later by a good report.

Done when all five are written down and you can name the specific action that would put you out of bounds.

"Find out about X" is not an objective; it has no stopping condition, so it terminates when you get bored or when you find something that feels like a result. Rewrite the request until it has a subject, a decision it feeds, and a condition that would settle it.

@handle

example.com

Then write the negative: what finding would mean no. If nothing could, the question is unfalsifiable and you will confirm it whatever you see.

Done when the objective is one sentence, and you have written what a "no" answer would look like.

Aimless pivoting feels productive because every pivot yields something. A collection plan is the list of questions, each mapped to the source most likely to settle it, ranked by cost and intrusiveness — so you notice when you're three hours into an interesting branch that answers nothing.

For each question: the indicator that would answer it, the source or skill that produces it, whether it is passive, and what you do if it comes back empty. Passive-first, always: exhaust archives, registries, and logs before anything that touches the subject. Plans are revised as you learn — the point is that deviations become visible.

Done when each objective question has a named source or skill and a first/fallback order.

Type the workflow skill's name.

find-anyone

x-ray-a-company

recon-a-domain-passively

hunt-a-handle

what-an-email-reveals

whose-number-is-this

where-was-this-taken

pattern-of-life-from-socials

No clear starting point? Start with the selector that is both unique and indexed — email and domain beat name and handle, because names collide and handles are claimed by strangers.

Technique skills load themselves when you describe what you are doing. Before touching anything the subject controls, run . Keep the case in a graph from the first pivot: .

Done when the routed workflow has been run and its findings are recorded with sources.

Use the Admiralty (NATO-style) scheme: a letter for the source and a number for the information, graded independently, on every item.

The independence matters: a corporate registry filing is B2, a well-run newspaper report of that filing is B2 at best, and an anonymous forum post repeating the newspaper is D3 — not new corroboration. Grade the source you actually touched, not the source it claims to have. Full grid, worked examples, and the common misgradings: reference/source-grading.md.

Done when every retained finding carries a two-character grade.

Analysis of Competing Hypotheses (ACH) exists because the natural mode of investigation — pick the likeliest story, look for support — always succeeds. Support is easy to find for any plausible story.

List every hypothesis including the boring ones ("it is a different person with the same name", "the account was sold", "the shared IP is shared hosting"). Build a matrix of evidence against hypotheses, and for each cell ask only whether the evidence is consistent with that hypothesis. Then work by column: the hypothesis with the fewest inconsistencies wins, not the one with the most support. Evidence consistent with every hypothesis has no diagnostic value — the subject having a LinkedIn does not distinguish anything. A handful of diagnostic items beats a hundred consistent ones. Worksheet and a filled example: reference/ach-worksheet.md.

Done when you have listed at least one hypothesis you did not want and recorded what evidence would refute your favoured one.

Confirmation bias, OSINT edition. You are given a name and told the person works in logistics. You find a logistics profile and stop asking whether it is a different person with the same name. The tell is that your discriminators disappear once you find a candidate — you selected on them to find, then stopped applying them to test. Fix: before you search, write the attributes the true subject must have and the ones they cannot have; check every candidate against both. Rejections are findings and belong in the report.

Circular reporting. Three sources agree, so you grade it confirmed. All three copied one blog post, or all three pull from the same aggregator or the same leaked dataset. This is the single most common cause of confident wrong attribution, and it is invisible unless you look for it. For each corroborating source, find its origin: check publication dates in order, look for identical phrasing or a copied typo, and check whether the "independent" people-search sites resell the same broker feed — see . Independent means different collection, not different websites.

Stale data presented as current. Registries, WHOIS, and broker records carry the date they were captured, not today's truth. Record the observation date next to every fact; archive the page via .

Selector drift. Each pivot carries the risk that you have changed people. A chain of five pivots each 90% likely is a coin flip. Re-anchor: after every pivot, state which confirmed selector ties the new one to the subject.

Tool output as evidence. An enumerator's hit list, a breach aggregator's match, a face-search score — these are leads. The tool did not verify identity; it matched a string or a vector.

Applies repo-wide unless a technique skill says otherwise.

Stop when the objective question is answered to the confidence the decision requires, or when you can document that available open sources cannot answer it and name what would (a records request, a subpoena, interviews). "We could not establish X, having checked A, B and C" is a deliverable, and often the honest one. Stop also when you cross a scope boundary — that is a re-authorization event, not a judgement call to make mid-flow.

Done when the objective is answered or documented as unanswerable, and has been run.

Objective: is the supplier on this invoice controlled by the ex-director of a barred entity? Discriminator: a director DOB month/year.

returns a registry record with a director "A. Kestrel", DOB matching, address a mail-forwarding suite. Two people-search sites and a business-listing site all show the same home address for A. Kestrel — apparent corroboration, until each is traced back and all three carry the same misspelling of the street name from one broker feed. Circular; graded D3 and dropped.

Competing hypotheses: (1) same person, (2) different A. Kestrel, (3) name used as a nominee. Diagnostic item: the barred entity's filings and Nordvale's list the same unusual accountancy firm, and the domain in the invoice footer shares a registrant email with the barred entity's old site (). That is inconsistent with (2), consistent with (1) and (3). Reported as probable, with the nominee hypothesis flagged as untested, and the gap named: beneficial ownership is not disclosed in that jurisdiction.

Every workflow feeds while it runs and at the end. Selector-to-skill routing is Step 4; the technique skills each list their own pivots.

Public availability is not permission. Data-protection law applies to aggregation of public personal data, and the aggregate is more sensitive than any part. Terms of service govern automated collection even where the data is public, and scraping disputes turn on authorization and contract, not on whether the page was visible. Never authenticate to, probe, or send traffic at systems belonging to the subject without written authorization.