drhalto/agentmako/mako-workflow

Claude Code and agent skill by drhalto/agentmako

Trust tier 3
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Checking that the SKILL.md file is still in the repository.

TRIGGER when: user wants an investigation packet, pre-ship artifact, recall of prior work or answers, or to log tool-run feedback / inspect telemetry. Covers `suggest`, `investigate`, `workflow_packet`, `tenant_leak_audit`, `health_trend`, `issues_next`, `session_handoff`, all four artifact tools, `recall_answers`, `recall_tool_runs`, `agent_feedback`, `agent_feedback_report`, `runtime_telemetry_report`, `finding_ack`, `finding_acks_report`.

Install

npx vskill@latest install drhalto/agentmako/mako-workflow

SKILL.md

Mako Workflow

Use this skill when the user needs a workflow output rather than a single trace: investigation packets, pre-implementation or review artifacts, prior-session recall, feedback, telemetry, or finding acknowledgements.

Operator And Workflow Tools

suggest

Use to propose useful Mako queries or investigation directions.

investigate

Use to run a broader bounded investigation workflow around a target.

workflow_packet

Use to produce a bundled workflow summary from gathered context.

tenant_leak_audit

Use to inspect likely tenant-boundary and data-leak risks.

  • Pair with db_rls, route_context, rpc_neighborhood, or review artifacts

when the user is evaluating security-sensitive changes.

health_trend

Use to summarize health trends from recorded project signals.

issues_next

Use to identify likely next issues or triage targets.

session_handoff

Use to produce a concise handoff for later continuation.

Artifact Tools

task_preflight_artifact

Use before implementation to create task context, risks, likely move surfaces, and verification plan.

implementation_handoff_artifact

Use when handing coding work to another agent or future session.

review_bundle_artifact

Use for pre-ship review findings, evidence, and risks.

verification_bundle_artifact

Use to summarize verification steps, outcomes, trust state, and remaining gaps.

Recall Tools

recall_answers

Use to retrieve prior answer artifacts relevant to the current task.

recall_tool_runs

Use to retrieve recent Mako tool runs.

  • Use this before agent_feedback.
  • Copy the returned requestId into agent_feedback.referencedRequestId.
  • Default limit is 50 and max is 500; use a smaller limit first when locating a

recent run.

Feedback And Telemetry

agent_feedback

Use to log whether a specific prior Mako tool run was useful.

  • Use sparingly for notable full, partial, or no-usefulness cases.
  • Requires both referencedToolName and referencedRequestId.
  • Do not call after every Mako tool.
  • Use recall_tool_runs first; do not fabricate request IDs.

Starter reason codes:

  • full: answer_complete, evidence_sufficient, trust_matches
  • partial: partial_coverage, noisy, stale_evidence,

missing_known_caller, top_not_useful

  • no: answer_wrong, wasted_turn, tool_did_nothing, schema_missing
agent_feedback_report

Use to inspect feedback events by referenced tool, grade, time window, and aggregate counts.

runtime_telemetry_report

Use to inspect runtime telemetry aggregates and event lists.

  • Use for review/debugging, not automatic tool selection.

Finding Acknowledgements

finding_ack

Use to acknowledge a specific static finding as reviewed, accepted-risk, false-positive, or otherwise handled.

  • Use for findings from ast_find_pattern and lint_files.
  • Do not use it to rate Mako search or trace quality.
  • Defaults to preview. Re-call with preview: false only after the

acknowledgement is intentionally being applied.

finding_ack_batch

Use to acknowledge multiple reviewed findings at once.

  • Defaults to preview and returns wouldApply plus rejected rows.
  • Re-call with preview: false only when the batch suppression is intentional.
finding_acks_report

Use to inspect acknowledged finding history and trends.

See Also

  • Use /mako-ai:mako-guide for the full feedback versus finding-ack policy.
  • Use /mako-ai:mako-trace, /mako-ai:mako-neighborhoods, and

/mako-ai:mako-graph to gather evidence before producing artifacts.

Security

  • Security scanPassed, 95 of 100Pattern checks on the skill's files.
  • Model reviewPassed

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

Source

Popularity

Repository stars
52
Repository forks
4
7-day trend score
20

Stars and forks are for the whole GitHub repository, not this skill alone. Refreshed 5 Nov 2026, 4 days ago.

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