pattern-of-life-from-socials: Reusable for AI Agents | OpenAgentSkill

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Pattern of Life from Socials

Pattern-of-life analysis turns scattered public posts into a model of where someone is, when, and with whom. It is the most abusable technique in this repo: the same method produces a due-diligence report and a stalking dossier. The difference is authorization and scope, not tradecraft. The beginner error is collecting posts instead of analysing them — screenshots of a feed are not intelligence. Work four layers: account metadata, network, content, temporal behaviour. The first and last are the two everyone skips, and the two the subject can't curate.

Step 1 — Authorized scope

Read ../../ETHICS.md, then write down before opening a single profile:

Subject — the account(s) and the real-world entity you believe is behind them.
Objective — the question that ends the investigation. "Pattern of life" is not one. "Does this vendor's EU lead actually live in the EU" is.
In / out of bounds — explicitly. Minors, uninvolved family, home address, health, religion, sexuality and immigration status are out unless the objective requires them and you can defend that.
Posture — observation only, or authorized interaction. Following, liking, messaging and viewing stories are all interaction.
Jurisdiction — yours, the subject's, the platform's.
Stop condition — you stop when the objective is answered, when the trail lands on an uninvolved third party, or when the only question left is "where do they sleep."

Done when all six are recorded in the case file.

Step 2 — Choose a viewing identity, then preserve

Logged-out leaks less and sees less; logged-in sees more and leaks more. Platforms variously report story views and profile visits to the subject, and recommendation systems surface accounts that look at each other — so merely viewing can put your research account in the subject's suggestions. Decide the tradeoff using investigate-without-getting-made; never browse a subject from a personal or employer account.

Then capture before you analyse. Accounts get locked or scrubbed mid-investigation, often because someone noticed. Archive the profile and every post you may cite via read-deleted-pages, and pull older snapshots — they routinely show a previous bio, link, or handle. Save media locally.

Done when the viewing identity is recorded and everything you intend to cite exists as an archive URL or a local file with a capture timestamp.

Step 3 — Layer one: account metadata

Go after what the subject never chose. Full per-platform behaviour is in the platform disclosure matrix.

Creation date. Shown outright on some platforms, derivable on others. Snowflake-style 64-bit IDs encode a millisecond timestamp in their high bits, offset from a platform-specific epoch — the ID is the signup time. Plain sequential IDs give registration order, so you can bracket a date against accounts of known age.
The numeric ID. It survives handle changes, so it — not the handle — is the durable selector. Record it.
Handle history. Seldom a feature, usually recoverable from old mentions, inbound links, archived snapshots and abandoned cross-posts. A freed handle can be reclaimed by a stranger, so an old link proves nothing about current control.
Verification and linked accounts. Whether a badge is paid or identity-checked changes what it's worth. Linked sites and business-account contact fields expose emails and phone numbers the personal profile wouldn't.

Done when ID, creation date, handle history and every linked selector are recorded with sources.

Step 4 — Layer two: network

A subject's OPSEC is nearly irrelevant if their relatives tag them.

Early followers. The first accounts to follow a personal account are overwhelmingly family, school friends and coworkers — it spread by word of mouth before it had reach. Where follower ordering is observable, the oldest tail is the highest-value segment on the page.
Mutual-follow clusters. Reciprocal edges map real-world communities: employer, school cohort, hometown, club. The cluster is the finding; a single edge isn't.
Tag direction. Who the subject tags is curated. Who tags the subject is not. Inbound tags from an open-book cousin routinely deliver the birthday, the house, the car and the workplace the locked-down subject withheld.
Reply latency. Accounts that reliably comment within minutes are the inner circle, regardless of follower counts.

Build this in graph-the-network, not as a list.

Done when the inner circle, one real-world cluster, and the third parties who leak about the subject are identified and graded.

Step 5 — Layer three: content

Read past the subject of each photo to the accidental content: reflections in windows, mirrors, glasses and dark screens; laptop and phone displays in frame; paperwork such as boarding passes, parcel labels and event badges; vehicles, plates, dealer frames and parking permits. Repeated backgrounds are what upgrade a room from "somewhere" to "home" or "workplace" — count occurrences and note the date span.

Run secrets-in-file-metadata on everything you downloaded: platforms differ in whether they strip EXIF, and the same platform may strip it from an inline image while preserving it in a file attachment or an original-quality download.

Do the geolocation itself in geolocate-from-pixels. Sanity-check anything that looks too convenient with is-this-photo-real.

Done when each location-bearing artefact is logged with post URL, date, and a pointer to the geolocation work.

Step 6 — Layer four: temporal behaviour

Extract every post timestamp into a table and plot hour-of-day and day-of-week. The extraction schema is in the analytic checklist.

A contiguous gap of roughly seven to nine hours is the sleep window, and its position gives a UTC offset — enough to separate continents, not neighbours. A weekday dip through business hours suggests employment with restricted device access; the inverse suggests shift work or a job spent online. Sudden multi-day offset shifts are travel.

What wrecks this: scheduling tools post at fixed wall-clock times regardless of where the human is, so a scheduled account measures the scheduler; platforms may render timestamps in the viewer's locale; edits can carry the edit time; and cross-posting bridges or shared team accounts blend several humans into one histogram. Establish that posting is manual before reading anything into shape.

Done when both distributions exist over a stated sample window, with an explicit inferred UTC offset and its confidence.

Step 7 — Consolidate and report

Link accounts on evidence: the same avatar file, the same link-in-bio target, follower-set overlap, aligned histograms. Writing style alone is a lead, not a link. Then run write-the-intel-brief. Every claim cites a post or archive URL and a date; every temporal conclusion states sample window and sample size.

Done when no claim lacks a citation and no inference lacks a grade.

Where this goes wrong

Sample bias. You're reading a self-published subset of a life. Silence means "didn't post," never "wasn't there."
Backdating. A post date is an upper bound on the event date. Photos get posted months late, reposted, or lifted from someone else entirely.
The account is not the person. Handles are sold, inherited, hacked and recycled; a long history may have changed hands. Partners, assistants and agencies post as the subject — two behavioural signatures in one histogram usually means two humans.
Curated self-report. Location, job title and relationship status are marketing copy, and a common name plus a matching city is a coincidence generator, not a match.
Rendering differences. Timestamps, follower ordering and mutual indicators change with login state, and are often approximate ("2h", "last week") rather than exact.
Observation changes the subject. One who locks down mid-case may have been tipped off by you.

Grading a finding

Confirmed — an authoritative record or the subject states it, or two independent artefacts of different types agree (an inbound tag from a separate account plus a geolocated background). Both archived.
Probable — several consistent signals of the same type, or one strong signal with nothing contradicting it: a repeated background plus a temporal pattern consistent with living there.
Unconfirmed — single-source, self-reported, style-based, or drawn from too small a sample. A timezone from a few dozen posts or fewer is unconfirmed, full stop.

Downgrade anything resting on an assumption you can't state in one sentence.

Worked example

Objective: confirm a supplier's "EU operations lead" is in Europe, as the contract requires.

Bio says Lisbon. The numeric ID decodes to a signup years before the company existed — so the bio says nothing about the present. Four months of timestamps cluster 14:00–05:00 UTC with a dead zone 06:00–13:00: a sleep window centred near 09:00 UTC, wrong for Lisbon, consistent with the Americas. Dead end: no geotags anywhere, and the platform stripped EXIF from every download.

The network layer breaks it. Early followers cluster around one US state university, and a relative tags the subject at a named local restaurant on a date the subject publicly claimed to be in Portugal; a repeated kitchen background appears on both sides of that date. Graded probable — no authoritative record places the subject anywhere, and a histogram can't separate adjacent countries. Reported with the sample window stated.

Pivots

You now have	Take it to
Handle and variants	`hunt-a-handle`
Avatar, banner, posted photo	`find-the-original-image`, `is-this-photo-real`
Photo needing place or time	`geolocate-from-pixels`
Downloaded media files	`secrets-in-file-metadata`
Exposed email / phone	`what-an-email-reveals`, `whose-number-is-this`, `what-leaked-about-you`
Corroborated personal name	`find-anyone`
Employer, brand page, link-in-bio domain	`x-ray-a-company`, `recon-a-domain-passively`
Follower and mutual edges	`graph-the-network`
Deleted or edited posts	`read-deleted-pages`
Aircraft or vessel in posts	`track-planes-and-ships`
Wallet address or ENS name	`follow-the-crypto`

Legal and ToS

Automated collection of profile and follower data breaches the terms of service of essentially every major platform and has been litigated as a computer-misuse matter in some jurisdictions; manual viewing of public content generally has not. Creating an account to view a subject is at minimum a ToS problem, and a fraud problem if you misrepresent identity to gain access.

Under GDPR and comparable regimes "publicly available" is not itself a lawful basis, and profiling a person's location and routine is high-risk processing. Political opinion, health, religion, sexuality and union membership are special categories — if they surface inci

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File metadata

name: pattern-of-life-from-socials
description: >-
  Deep-dive a subject's social media presence — profile metadata, follower and mutual network,
  content analysis, and posting-time pattern of life across Instagram, Facebook, X/Twitter,
  TikTok, LinkedIn, Reddit, Telegram and Discord. Use when profiling a social account, mapping
  someone's associates, inferring a subject's timezone or routine from their posts, or
  archiving a profile before it is deleted. Applies to threat assessment and executive
  protection, insider-threat investigation, pre-litigation research, and personal exposure
  audits — with explicit limits on profiling uninvolved third parties. Reference at
  useosint.com/skills/pattern-of-life-from-socials.
disable-model-invocation: true

View original text

---
name: pattern-of-life-from-socials
description: >-
Deep-dive a subject's social media presence — profile metadata, follower and mutual network,
content analysis, and posting-time pattern of life across Instagram, Facebook, X/Twitter,
TikTok, LinkedIn, Reddit, Telegram and Discord. Use when profiling a social account, mapping
someone's associates, inferring a subject's timezone or routine from their posts, or
archiving a profile before it is deleted. Applies to threat assessment and executive
protection, insider-threat investigation, pre-litigation research, and personal exposure
audits — with explicit limits on profiling uninvolved third parties. Reference at
useosint.com/skills/pattern-of-life-from-socials.
disable-model-invocation: true
---

# Pattern of Life from Socials

Pattern-of-life analysis turns scattered public posts into a model of where
someone is, when, and with whom. It is the most abusable technique in this
repo: the same method produces a due-diligence report and a stalking dossier.
The difference is authorization and scope, not tradecraft. The beginner error
is collecting posts instead of analysing them — screenshots of a feed are not
intelligence. Work four layers: account metadata, network, content, temporal
behaviour. The first and last are the two everyone skips, and the two the
subject can't curate.

## Step 1 — Authorized scope

Read [../../ETHICS.md](../../ETHICS.md), then write down before opening a
single profile:

- **Subject** — the account(s) and the real-world entity you believe is behind
them.
- **Objective** — the question that ends the investigation. "Pattern of life"
is not one. "Does this vendor's EU lead actually live in the EU" is.
- **In / out of bounds** — explicitly. Minors, uninvolved family, home address,
health, religion, sexuality and immigration status are out unless the
objective requires them and you can defend that.
- **Posture** — observation only, or authorized interaction. Following, liking,
messaging and viewing stories are all interaction.
- **Jurisdiction** — yours, the subject's, the platform's.
- **Stop condition** — you stop when the objective is answered, when the trail
lands on an uninvolved third party, or when the only question left is "where
do they sleep."

**Done when** all six are recorded in the case file.

## Step 2 — Choose a viewing identity, then preserve

Logged-out leaks less and sees less; logged-in sees more and leaks more.
Platforms variously report story views and profile visits to the subject, and
recommendation systems surface accounts that look at each other — so merely
viewing can put your research account in the subject's suggestions. Decide the
tradeoff using `investigate-without-getting-made`; never browse a subject from
a personal or employer account.

Then capture before you analyse. Accounts get locked or scrubbed
mid-investigation, often because someone noticed. Archive the profile and every
post you may cite via `read-deleted-pages`, and pull older snapshots — they
routinely show a previous bio, link, or handle. Save media locally.

**Done when** the viewing identity is recorded and everything you intend to
cite exists as an archive URL or a local file with a capture timestamp.

## Step 3 — Layer one: account metadata

Go after what the subject never chose. Full per-platform behaviour is in the
[platform disclosure matrix](reference/platform-disclosure.md).

- **Creation date.** Shown outright on some platforms, derivable on others.
Snowflake-style 64-bit IDs encode a millisecond timestamp in their high bits,
offset from a platform-specific epoch — the ID *is* the signup time. Plain
sequential IDs give registration order, so you can bracket a date against
accounts of known age.
- **The numeric ID.** It survives handle changes, so it — not the handle — is
the durable selector. Record it.
- **Handle history.** Seldom a feature, usually recoverable from old mentions,
inbound links, archived snapshots and abandoned cross-posts. A freed handle
can be reclaimed by a stranger, so an old link proves nothing about current
control.
- **Verification and linked accounts.** Whether a badge is paid or
identity-checked changes what it's worth. Linked sites and business-account
contact fields expose emails and phone numbers the personal profile wouldn't.

**Done when** ID, creation date, handle history and every linked selector are
recorded with sources.

## Step 4 — Layer two: network

A subject's OPSEC is nearly irrelevant if their relatives tag them.

- **Early followers.** The first accounts to follow a personal account are
overwhelmingly family, school friends and coworkers — it spread by word of
mouth before it had reach. Where follower ordering is observable, the oldest
tail is the highest-value segment on the page.
- **Mutual-follow clusters.** Reciprocal edges map real-world communities:
employer, school cohort, hometown, club. The cluster is the finding; a single
edge isn't.
- **Tag direction.** Who the subject tags is curated. Who tags the subject is
not. Inbound tags from an open-book cousin routinely deliver the birthday,
the house, the car and the workplace the locked-down subject withheld.
- **Reply latency.** Accounts that reliably comment within minutes are the
inner circle, regardless of follower counts.

Build this in `graph-the-network`, not as a list.

**Done when** the inner circle, one real-world cluster, and the third parties
who leak about the subject are identified and graded.

## Step 5 — Layer three: content

Read past the subject of each photo to the accidental content: reflections in
windows, mirrors, glasses and dark screens; laptop and phone displays in frame;
paperwork such as boarding passes, parcel labels and event badges; vehicles,
plates, dealer frames and parking permits. Repeated backgrounds are what
upgrade a room from "somewhere" to "home" or "workplace" — count occurrences
and note the date span.

Run `secrets-in-file-metadata` on everything you downloaded: platforms differ
in whether they strip EXIF, and the same platform may strip it from an inline
image while preserving it in a file attachment or an original-quality download.

Do the geolocation itself in `geolocate-from-pixels`. Sanity-check anything
that looks too convenient with `is-this-photo-real`.

**Done when** each location-bearing artefact is logged with post URL, date, and
a pointer to the geolocation work.

## Step 6 — Layer four: temporal behaviour

Extract every post timestamp into a table and plot hour-of-day and day-of-week.
The extraction schema is in the
[analytic checklist](reference/analytic-checklist.md).

A contiguous gap of roughly seven to nine hours is the sleep window, and its
position gives a UTC offset — enough to separate continents, not neighbours. A
weekday dip through business hours suggests employment with restricted device
access; the inverse suggests shift work or a job spent online. Sudden
multi-day offset shifts are travel.

What wrecks this: scheduling tools post at fixed wall-clock times regardless of
where the human is, so a scheduled account measures the scheduler; platforms
may render timestamps in the *viewer's* locale; edits can carry the edit time;
and cross-posting bridges or shared team accounts blend several humans into one
histogram. Establish that posting is manual before reading anything into shape.

**Done when** both distributions exist over a stated sample window, with an
explicit inferred UTC offset and its confidence.

## Step 7 — Consolidate and report

Link accounts on evidence: the same avatar file, the same link-in-bio target,
follower-set overlap, aligned histograms. Writing style alone is a lead, not a
link. Then run `write-the-intel-brief`. Every claim cites a post or archive URL
and a date; every temporal conclusion states sample window and sample size.

**Done when** no claim lacks a citation and no inference lacks a grade.

## Where this goes wrong

- **Sample bias.** You're reading a self-published subset of a life. Silence
means "didn't post," never "wasn't there."
- **Backdating.** A post date is an upper bound on the event date. Photos get
posted months late, reposted, or lifted from someone else entirely.
- **The account is not the person.** Handles are sold, inherited, hacked and
recycled; a long history may have changed hands. Partners, assistants and
agencies post as the subject — two behavioural signatures in one histogram
usually means two humans.
- **Curated self-report.** Location, job title and relationship status are
marketing copy, and a common name plus a matching city is a coincidence
generator, not a match.
- **Rendering differences.** Timestamps, follower ordering and mutual
indicators change with login state, and are often approximate ("2h", "last
week") rather than exact.
- **Observation changes the subject.** One who locks down mid-case may have
been tipped off by you.

## Grading a finding

- **Confirmed** — an authoritative record or the subject states it, or two
independent artefacts of *different* types agree (an inbound tag from a
separate account plus a geolocated background). Both archived.
- **Probable** — several consistent signals of the same type, or one strong
signal with nothing contradicting it: a repeated background plus a temporal
pattern consistent with living there.
- **Unconfirmed** — single-source, self-reported, style-based, or drawn from
too small a sample. A timezone from a few dozen posts or fewer is
unconfirmed, full stop.

Downgrade anything resting on an assumption you can't state in one sentence.

## Worked example

Objective: confirm a supplier's "EU operations lead" is in Europe, as the
contract requires.

Bio says Lisbon. The numeric ID decodes to a signup years before the company
existed — so the bio says nothing about the present. Four months of timestamps
cluster 14:00–05:00 UTC with a dead zone 06:00–13:00: a sleep window centred
near 09:00 UTC, wrong for Lisbon, consistent with the Americas. Dead end: no
geotags anywhere, and the platform stripped EXIF from every download.

The network layer breaks it. Early followers cluster around one US state
university, and a relative tags the subject at a named local restaurant on a
date the subject publicly claimed to be in Portugal; a repeated kitchen
background appears on both sides of that date. Graded **probable** — no
authoritative record places the subject anywhere, and a histogram can't
separate adjacent countries. Reported with the sample window stated.

## Pivots

| You now have | Take it to |
|---|---|
| Handle and variants | `hunt-a-handle` |
| Avatar, banner, posted photo | `find-the-original-image`, `is-this-photo-real` |
| Photo needing place or time | `geolocate-from-pixels` |
| Downloaded media files | `secrets-in-file-metadata` |
| Exposed email / phone | `what-an-email-reveals`, `whose-number-is-this`, `what-leaked-about-you` |
| Corroborated personal name | `find-anyone` |
| Employer, brand page, link-in-bio domain | `x-ray-a-company`, `recon-a-domain-passively` |
| Follower and mutual edges | `graph-the-network` |
| Deleted or edited posts | `read-deleted-pages` |
| Aircraft or vessel in posts | `track-planes-and-ships` |
| Wallet address or ENS name | `follow-the-crypto` |

## Legal and ToS

Automated collection of profile and follower data breaches the terms of service
of essentially every major platform and has been litigated as a computer-misuse
matter in some jurisdictions; manual viewing of public content generally has
not. Creating an account to view a subject is at minimum a ToS problem, and a
fraud problem if you misrepresent identity to gain access.

Under GDPR and comparable regimes "publicly available" is not itself a lawful
basis, and profiling a person's location and routine is high-risk processing.
Political opinion, health, religion, sexuality and union membership are special
categories — if they surface inci

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License: MIT
Version: Unknown
Last GitHub push: Aug 3, 2026
Registry updated: Sep 11, 2026
Instruction path: skills/pattern-of-life-from-socials/SKILL.md @ 06243a5620b0

Version reported in registry metadata; check source releases before relying on it.

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    "label": "Manual review",
    "version": "trust-score-v4",
    "install_policy": "block",
    "evidence": {
      "stars": "33 GitHub stars",
      "repoActivity": "33 stars, 2 forks",
      "lastPushed": "1mo since push",
      "license": "MIT",
      "repository": "https://github.com/UseOSINT/Skills/tree/main/skills/pattern-of-life-from-socials",
      "install": "npx skills add UseOSINT/Skills --skill pattern-of-life-from-socials",
      "installSafety": "standard package or runtime install path",
      "permissionSurface": "secrets or environment access, filesystem or document access",
      "documentation": "Usable metadata, review docs",
      "agentOutcomes": "No agent outcome data yet"
    },
    "outcome_evidence": {
      "total": 0,
      "successes": 0,
      "failures": 0,
      "not_relevant": 0,
      "success_rate": null,
      "recent_success_rate": null,
      "recent_failure_rate": null,
      "install_attempts": 0,
      "install_success_rate": null,
      "risk_blocked": 0,
      "setup_required": 0,
      "avg_output_quality": null,
      "production_outcomes": 0,
      "last_outcome_at": null,
      "label": "No agent outcome data yet"
    },
    "auto_install": {
      "allowed": false,
      "sandbox_required": true,
      "reason": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
    },
    "best_for": [
      "productivity",
      "agent-skill"
    ],
    "known_risks": [
      "AI review approval is missing",
      "Financial research output is not financial advice; require human review before any live investment decision.",
      "This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.",
      "Low GitHub adoption signal",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, filesystem or document access",
      "GitHub adoption: 33 GitHub stars",
      "Stars/forks activity: 33 stars, 2 forks; issue activity unavailable in current metadata"
    ]
  },
  "agent_proven": {
    "version": "agent-proven-v1",
    "score": 0,
    "tier": "unproven",
    "label": "Needs first agent run",
    "summary": "No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.",
    "metrics": {
      "totalOutcomes": 0,
      "successfulOutcomes": 0,
      "failedOutcomes": 0,
      "installAttempts": 0,
      "installSuccessRate": null,
      "successRate": null,
      "recentSuccessRate": null,
      "recentFailureRate": null,
      "riskBlocked": 0,
      "setupRequired": 0,
      "notRelevant": 0,
      "avgOutputQuality": null,
      "avgTimeToUsefulMs": null,
      "productionOutcomes": 0,
      "humanReviewRequired": 0,
      "uniqueAgents": 0,
      "lastOutcomeAt": null
    },
    "signals": [],
    "penalties": [
      "No real agent outcome evidence yet"
    ]
  },
  "audit": {
    "score": 70,
    "risk_level": "risky",
    "risk_label": "Risky",
    "warnings": [
      "Permission surface may require sandboxing",
      "Financial research output is not financial advice; require human review before any live investment decision",
      "Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required",
      "Low GitHub adoption signal",
      "AI review approval is missing",
      "Financial research output is not financial advice; require human review before any live investment decision.",
      "This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.",
      "Quality score needs review"
    ]
  },
  "safety_gate": {
    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 51,
    "label": "Needs review"
  },
  "supply": {
    "track": "Research and knowledge work",
    "scenario": "Workflow automation",
    "maintenance": "1mo since push",
    "risk": "Risky"
  },
  "alternative_skills": [],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "production agents without a repository review",
    "Low GitHub adoption signal",
    "No OpenAgentSkill engagement data yet",
    "Audit risk risky exceeds max_risk=medium",
    "High-risk permission hints: Secrets or environment access",
    "Permission surface may require sandboxing",
    "Financial research output is not financial advice; require human review before any live investment decision"
  ],
  "agent_contract": {
    "task_input": "Use pattern-of-life-from-socials in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 69/100 Manual review",
      "Audit: 70/100 Risky",
      "Safety: 38/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "useosint-pattern-of-life-from-socials (pattern-of-life-from-socials)",
      "install_command": "npx skills add UseOSINT/Skills --skill pattern-of-life-from-socials",
      "risk_summary": "Risky; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "useosint-pattern-of-life-from-socials",
      "task": "Use pattern-of-life-from-socials in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/useosint-pattern-of-life-from-socials",
    "api": "https://www.openagentskill.com/api/agent/skills/useosint-pattern-of-life-from-socials",
    "audit": "https://www.openagentskill.com/skills/useosint-pattern-of-life-from-socials/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=useosint-pattern-of-life-from-socials&task=Use%20pattern-of-life-from-socials%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20pattern-of-life-from-socials%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20pattern-of-life-from-socials%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/useosint-pattern-of-life-from-socials/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/useosint-pattern-of-life-from-socials"
  }
}

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Creator: UseOSINT
Source: UseOSINT/Skills
Indexed by: OpenAgentSkill community index

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