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AWSKnown publisheraws/agent-toolkit-for-aws/plugins/aws-core/skills/aws-investigation/SKILL.md

aws-investigation

Skill3.4k starsChanged 5 months agoApache-2.0View on GitHub

Investigates what is happening in an AWS account from evidence already recorded there. Not for capturing new data from running code (snapshots of function arguments on a live service, Dynamic Instrumentation), questions answered from the CloudWatch Omni context graph or Application Signals service map, or building or changing infrastructure. Covers reading deployment records instead of the newest stored version (completed AppConfig deployments, the version an alias points to, a stack's current template), following a request through every service's own logs (exact per-layer fields, unique failed requests in an alarm's breach window, failure boundary per event), and answering questions from the account's data (exact counts and breakdowns, coverage of the requested range, exact filters and joins, the rule behind failed or unmatched records, verbatim values). Use to triage an alarm, error spike or failed request, trace a request across services, or count, break down, extract or join records.

Adopted by

Steady: 0 new projects took it in the last 4 weeks, none the 4 weeks before.thin data

Week of Sep 13: 0 new projectsWeek of Sep 20: 0 new projectsWeek of Sep 27: 0 new projectsWeek of Oct 4: 0 new projectsWeek of Oct 11: 0 new projectsWeek of Oct 18: 0 new projectsWeek of Oct 25: 0 new projectsWeek of Nov 1: 0 new projects
Sep 2026Oct 2026Nov 2026
New projects taking a version of this file each week, over the 8 weeks our latest run read: only 0 in all, so this is a hint, not a trend.

Who's talking about aws/agent-toolkit-for-aws

The biggest 5 of 55 mentions linking to the repository that our crawl found. Each line is their own headline.

See all 55

Talked about on GitHub, Bluesky and Hacker News (55 times).

On GitHub · 50

45 more on GitHub

On Bluesky · 4

On Hacker News · 1

The file

We split this skill into the rules it gives an agent

5 rules. About 0 are common, 4 are distinctive, and 1 is very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈4

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Read the reference that matches the question before you start.

    line 28
  2. Very specific:

    Separate evidence from inference: Keep a short provenance note per claim (claim <- artifact, record or query).

    line 44
  3. Distinctive:

    Count exactly: Use aggregate queries run in the engine, scoped to exactly what was asked, and report unique entities separately from raw …

    line 47
  4. Distinctive:

    Before submitting your analysis:

    line 56
  5. Distinctive:

    Before you finish, have you reread your conclusion against each piece of evidence you collected and removed any statement that contradicts …

    line 70

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

AWS Investigation

An investigation answers a question about what an AWS account is doing, and every claim in the answer must come from something you read: a deployed artifact, a service's own log record, or a query result. Inference from names, descriptions, the newest stored version or one service's view of another is a lead, never evidence.

Pick the reference

Read the reference that matches the question before you start; most investigations need more than one.

The question is aboutRead
What a deployed system currently does, serves or executes: live configuration, feature flags, parameters, which code runs, reproducing a code path's query, or a first data route that is blockedreferences/deployed-artifacts.md
A request, alarm or incident that spans several services: tracing one request, counting failed requests, deciding where an event was lostreferences/multi-service-logs.md
Facts in the account's data: finding where data lives, exact counts and breakdowns, filtering, joining or exporting records, the rules behind failed or unmatched recordsreferences/data-investigation.md
Whether a finding is complete and a root cause holds: searching every region and scope, paginating before counting, coverage of a date range, one entity vs the whole affected population, consistency of the root cause, whether a runbook fixes the cause, answering the user's own questionreferences/completeness-and-root-cause.md

Rules for every investigation

  1. Explore before you give up. Assume the credentials are the intended environment. Treat unfamiliar or internal terms (service nicknames, airport-code regions such as PDX = us-west-2, IAD = us-east-1) as search hints: list resources, log groups and data stores in the likely regions and search for the identifiers in the request before saying something cannot be done.
  2. Separate evidence from inference. Keep a short provenance note per claim (claim <- artifact, record or query). Label anything you did not read as a hypothesis or UNVERIFIED, with the routes you tried.
  3. Count exactly. Use aggregate queries run in the engine, scoped to exactly what was asked, and report unique entities separately from raw records.
  4. Quote, don't paraphrase. Give identifiers, field values, error codes and messages exactly as the source produced them, including null and empty values.
  5. Finish the task. Produce every requested artifact (files, tables, plans), and state the assumptions you made in the final answer.

Closing checklist

Before submitting your analysis:

  1. Have you searched all relevant regions and scopes?
  2. Have you paginated through all results, and said which part of the requested range has data?
  3. Have you verified the scope of failures (entity-specific vs fleet-wide, the whole affected population)?
  4. Does your root cause fit all the facts you found?
  5. Have you labelled every mechanism you could not confirm as unverified, and reported a missing component as a defect only if it was meant to exist?
  6. Did your claims about live configuration come from the deployed artifact, not the latest stored version (deployed-artifacts.md)?
  7. For a request that crosses services, did you read each service's own logs and place the failure boundary per event (multi-service-logs.md)?
  8. Have you answered the user's specific question directly?
  9. Before you finish, have you reread your conclusion against each piece of evidence you collected and removed any statement that contradicts it?
  10. Have you removed any contradictions between your findings and recommendations, and checked each runbook option against the confirmed cause?

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