Agent skill

Deep Dive

by ZaxbyHub in ZaxbyHub/opencode-swarm

Full execution protocol for MODE: DEEPDIVE — read-only codebase audit with parallel explorer waves, 2 independent reviewers, and sequential critic challenge for HIGH/CRITICAL findings.

MITAuto-check passed

Install Deep Dive

skills CLI
$ npx skills add ZaxbyHub/opencode-swarm --skill deep-dive -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install ZaxbyHub/opencode-swarm deep-dive --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/ZaxbyHub/opencode-swarm.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/deep-dive .claude/skills/deep-dive && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
deep-dive
GitHub stars
494
Token cost
~3.1k tokens
SKILL.md length
1,678 words
Files
1
Skills in repo
91
Repo updated
First seen
Licence
MIT

At a glance

Full execution protocol for MODE: DEEPDIVE — read-only codebase audit with parallel explorer waves, 2 independent reviewers, and sequential critic challenge for HIGH/CRITICAL findings.

  • Works in 8 steps: Parse Header → Repo Readiness → Scope Resolution → …
  • SKILL.md covers Graph-first evidence contract, Step 0 — Parse Header, Step 1 — Repo Readiness and Step 2 — Scope Resolution, plus 7 more sections
  • Calls git

What it does

Deep Dive is an agent skill from ZaxbyHub/opencode-swarm. Full execution protocol for MODE: DEEPDIVE — read-only codebase audit with parallel explorer waves, 2 independent reviewers, and sequential critic challenge for HIGH/CRITICAL findings. Loaded on demand by the architect when the deep-dive command emits a [MODE: DEEPDIVE ...] signal.

Its SKILL.md is about 3.1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

The repository describes itself as: Architect-centric agentic swarm plugin for OpenCode. Hub-and-spoke orchestration with SME consultation, code generation, and QA review. The licence is MIT.

Example prompts

  • “/deep-dive”

Workflow steps

8 steps, taken from the step headings in SKILL.md.

  1. Parse Header
  2. Repo Readiness
  3. Scope Resolution
  4. Explorer Missions (Parallel Waves)
  5. Normalize Candidates
  6. Always 2 Parallel Reviewers
  7. Critic Challenge (HIGH/CRITICAL only)
  8. Final Report

What it can do on your machine

Read from SKILL.md and the folder at commit b63a4bd. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • git

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Deep Dive loads about 3.1k tokens when it runs. Until then it costs about 74 tokens; SKILL.md has 1,678 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~74
When it runs · the whole SKILL.md, loaded when a task matches
~3.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from ZaxbyHub/opencode-swarm at commit b63a4bd, republished under its MIT licence (© ZaxbyHub). 1,678 words, ~3,120 tokens.

Download SKILL.mdSave it as .claude/skills/deep-dive/SKILL.md (or your agent's skills folder).
name
deep-dive
description
Full execution protocol for MODE: DEEP_DIVE — read-only codebase audit with parallel explorer waves, 2 independent reviewers, and sequential critic challenge for HIGH/CRITICAL findings. Loaded on demand by the architect when the deep-dive command emits a [MODE: DEEP_DIVE ...] signal.
audience
swarm-plugin

Deep Dive Audit Protocol

Read-only deep audit of a specified codebase scope using parallel explorer waves, always 2 parallel reviewers, and sequential critic challenge. This mode does NOT mutate source code, does NOT delegate to coder, and does NOT call declare_scope.

Graph-first evidence contract

Use repo_map graph_health and boundary discovery before a targeted source-bearing context_pack; existing ask, key_files, and localization remain discovery inputs, and repo_map action="retrieve" adds one bounded mixed pass routing across ask, symbols, callers, impact, routes, data, preflight, tests, diffs, and explanations — it does not replace explicit key_files or localization discovery. Graph evidence is advisory only. If freshness is stale or inconclusive, confidence is low, source is missing, the language is unsupported/dynamic, the graph is absent, or an action fails, inspect the direct source and searches before reporting a finding.

MODE: DEEP_DIVE

Step 0 — Parse Header

Parse the MODE: DEEP_DIVE header to extract:

  • scope: the codebase area to audit (e.g., "auth", "payment flow", "src/hooks/")
  • profile: one of standard | security | ux | architecture | full (default: standard)
  • max_explorers: integer 1..8 — upper bound on explorer waves (default: 6, or 8 for full profile). This is a CAP, not a fixed count: scale the actual wave size to the resolved scope surface — a trivial scope needs 1–2 explorers, a typical scope 3–5, a large multi-module scope up to the cap — never fix the count in advance.
  • output: markdown | json (default: markdown)
  • update_main: boolean (default: true) — whether to fetch/ff-only main before starting
  • allow_dirty: boolean (default: false) — whether to proceed with uncommitted changes

If the header is malformed or missing required fields, report the error and stop.

Step 1 — Repo Readiness

  1. Check git working tree status. If dirty and allow_dirty is false, warn the user and ask whether to proceed. Do NOT proceed automatically.
  2. If update_main is true and tree is clean: check current branch. If not on main, report current branch to user and ASK FOR CONFIRMATION before switching. Only after explicit user approval: git fetch origin main && git checkout main && git merge --ff-only origin/main. If ff-only fails, warn the user and ask before proceeding.
  3. Record the current HEAD commit hash for the report.

Step 2 — Scope Resolution

Use the following tools to map the audit scope:

  1. repo_map with action "graph_health" — only if the graph is missing or incomplete (stale beyond the refresh cap, extraction failures) run repo_map with action "build"; plugin init and the session-start probe keep the graph fresh otherwise
  2. repo_map with action "ask" (the audit scope as the question) and action "key_files" to rank scope-relevant files and repo hubs before any manual symbol walk
  3. repo_map with action "localization" for the scope target
  4. symbols and batch_symbols on key files identified by ask or localization
  5. imports to trace dependency boundaries
  6. doc_scan if documentation coverage is relevant
  7. knowledge_recall with query matching the scope domain

Produce a SCOPE MAP: list of files, modules, and interfaces within the audit boundary. Cap at 50 files total.

Step 3 — Explorer Missions (Parallel Waves)

Dispatch explorer waves with dispatch_lanes_async when available. Each wave contains up to max_explorers missions. Before the first dispatch, verify from the session's actual tool list whether the controller's lane tools are present; when they are absent, use the native parallel subagent path from the start rather than discovering the gap on first failure.

File caps per mission:

  • 8 files maximum per mission
  • ~3500 total lines across all files in a mission
  • Group files by import proximity (files that import each other go in the same mission)

Partition is the contract: missions own non-overlapping file sets — no file appears in two missions — and the union of all missions must cover every file in the Step 2 scope map. Any scope-map file not assigned to a mission is an explicit coverage gap, not an optional skip.

Profile-based lane selection — each profile activates specific lanes:

LaneTemplatestandardsecurityuxarchitecturefull
SCOPE_MAPMap structure, exports, boundaries✓✓✓✓✓
WIRING_DATAFLOWTrace data flow, API contracts, state propagation✓✓✓✓
RUNTIME_BEHAVIORError handling, edge cases, lifecycle, async patterns✓✓✓
UX_FLOWUser-facing behavior, accessibility, responsiveness✓✓
SECURITY_TRUSTAuth boundaries, input validation, trust transitions✓✓
TEST_COVERAGECoverage gaps, flaky tests, missing assertions✓✓
PERFORMANCE_RELIABILITYResource leaks, N+1 queries, race conditions✓✓
DOCS_CONFIG_DEPLOYMENTConfig consistency, docs accuracy, deployment drift✓

Each explorer mission receives:

  • Lane template name and description
  • Assigned files (8 max, grouped by import proximity)
  • The scope map context from Step 2
  • Instruction: "You are performing a [LANE] audit. Report ALL findings as pipe-delimited [CANDIDATE] rows. Header row first, then one row per finding:

[CANDIDATE] | candidate_id | lane | severity | category | file:line | claim | evidence_summary | impact_context | confidence

  • candidate_id: unique within this lane (e.g. C-001, C-002)
  • severity: INFO | LOW | MEDIUM | HIGH | CRITICAL
  • confidence: LOW | MEDIUM | HIGH
  • If you find zero issues, emit the header row with no data rows.
  • Do NOT emit findings as prose or free text — the downstream parser requires pipe-delimited rows."

Explorer missions are dispatched in parallel waves. Launch the wave promptly — do not accumulate extensive planning prose before the call, or output truncation may swallow the tool call itself. Launch the wave, record the returned batch_id, then continue deterministic architect work that does not depend on lane output: refine the scope map, build the candidate ledger shell, inspect local evidence with read-only tools, and prepare reviewer shard structure. Do not synthesize findings from running lanes. Keep each lane prompt compact: send shared context ONCE via the common_prompt field, or have lanes read it from a file by absolute path, instead of inlining the same large blob into every lane prompt — oversized inline prompts produce malformed or truncated tool-call JSON.

Incremental collection pattern: While lanes are running, use collect_lane_results without wait (or wait: false) to poll progress. Process any settled lanes immediately — extract candidates, check output_ref, update the candidate ledger — while continuing independent architect work (scope refinement, local evidence reads, reviewer preparation) between polls. This avoids idle waiting and lets you pipeline candidate normalization with lane completion. Only use wait: true at the Step 4 boundary if lanes are still pending and no more independent work remains.

At the Step 4 boundary, all lanes must be settled before proceeding. If non-blocking polls show lanes still running and you have exhausted independent work, call collect_lane_results with wait: true to block on the remaining lanes. COVERAGE GATE: Every lane must produce validated candidate output before proceeding. Missing, stale, cancelled, or failed lanes are coverage gaps that must be closed — not documented and skipped. If a lane fails: (1) retry max 2 times with materially different parameters; (2) if retries fail, deploy an equivalent alternative (same agent type, same prompt, same scope, same isolation — different dispatch mechanism acceptable when verified, including Task-tool dispatch as the final fallback when lane tools do not work); (3) if no equivalent exists, stop and surface the lane failure to the user as BLOCKED. Do not proceed past a required lane with unclosed coverage or produce a degraded review.

When a collected or blocking lane result includes output_ref, treat output as a preview and call retrieve_lane_output before extracting candidate findings or declaring a lane clean. If the result is output_degraded, transcript_incomplete, truncated without a usable ref, missing, stale, cancelled, or failed — or if the lane reports status: completed but parse_lane_candidates returns 0 candidates (Mode B: intermediate reasoning only) — apply the COVERAGE GATE: retry, deploy equivalent including Task-tool dispatch as the final fallback when lane tools do not work, or stop and surface the lane failure to the user as BLOCKED. Do not mark findings/coverage UNVERIFIED to proceed past the gap.

Explorers generate CANDIDATE FINDINGS only — they do NOT make verdicts. All findings are unverified until Step 5.

Show full SKILL.md (420 more words)Show less

Step 4 — Normalize Candidates

  1. Collect all candidate findings from all explorer missions.
  2. Deduplicate: merge findings that reference the same location and issue.
  3. Assign DD-C001 through DD-CNNN identifiers to unique findings.
  4. Sort candidates by severity (CRITICAL → HIGH → MEDIUM → LOW → INFO).
  5. Shard into ≤10-candidate shards until all candidates are assigned to a shard.

Step 5 — Always 2 Parallel Reviewers

Split the candidates into shards of ≤10 each and dispatch 2 parallel the active swarm's reviewer agent calls.

Each reviewer receives:

  • Their shard of candidates (up to 10)
  • The scope map context
  • The original scope description
  • Instruction: "Verify or reject each candidate finding. For each: verdict (VERIFIED / REJECTED / NEEDS_MORE_EVIDENCE), confidence (0-1), and brief reasoning."

Reviewers MUST NOT suggest fixes — they verify findings only.

Step 5b — Reviewer Merge/Dedup

After both reviewers return, perform a lightweight sync pass:

  1. Cross-reference findings between reviewers — flag correlations
  2. Deduplicate any findings both reviewers verified independently
  3. For NEEDS_MORE_EVIDENCE findings: if the other reviewer verified a related finding, merge
  4. Produce a unified findings list with verified/rejected status

Step 6 — Critic Challenge (HIGH/CRITICAL only)

For verified findings rated HIGH or CRITICAL, dispatch sequential critic passes:

Pass 1 — False-positive / root-cause challenge:

  • the active swarm's critic agent receives each HIGH/CRITICAL finding
  • Challenge: "Is this a false positive? Is the root cause correctly identified? Provide verdict: SURVIVES / DOWNGRADE / REJECT"
  • Only findings that SURVIVE proceed to Pass 2

Pass 2 — Impact / severity challenge:

  • the active swarm's critic agent receives surviving findings
  • Challenge: "Is the severity correctly rated? Could this be lower impact than claimed? Provide verdict: SURVIVES / DOWNGRADE / REJECT"
  • Final severity is the critic's assessed severity

CRITICAL: Do NOT challenge MEDIUM/LOW/INFO findings. Only HIGH and CRITICAL go through critic review.

Step 7 — Final Report

Assemble and present the audit report:

  1. Wiring Map: Visual summary of the scope's module structure and data flow
  2. Functionality Assessment: High-level summary of what the scope does and how well
  3. Verified Findings Table: DD-ID, severity, location, description, evidence
  4. Rejected Candidates: Brief list with rejection reasons
  5. Enhancements: Non-blocking improvement suggestions
  6. Recommended Implementation Phases: If findings suggest follow-up work, outline phases
  7. JSON Block (when output=json): Structured machine-readable findings

Important Constraints

  • Do NOT mutate source code under any circumstances
  • Do NOT delegate to coder
  • Do NOT call declare_scope
  • Do NOT create or modify any files outside .swarm/
  • No final finding may appear in the report without reviewer verification
  • Explorers generate candidate findings only — reviewers verify or reject
  • Critics challenge only HIGH/CRITICAL findings — do NOT waste cycles on lower severity

© ZaxbyHub, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .claude/skills/deep-dive of ZaxbyHub/opencode-swarm.

Open the folder on GitHubat commit b63a4bd

Compare with similar skills

Deep Dive next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Deep Dive compared with similar skills
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Retellai Migration Deep Divejeremylongshore/tons-of-skills-marketplace2.8k—~522Automated safety check: PassMIT

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Questions about Deep Dive

What does Deep Dive do?

Full execution protocol for MODE: DEEPDIVE — read-only codebase audit with parallel explorer waves, 2 independent reviewers, and sequential critic challenge for HIGH/CRITICAL findings. Deep Dive is an agent skill from ZaxbyHub/opencode-swarm. Full execution protocol for MODE: DEEPDIVE — read-only codebase audit with parallel explorer waves, 2 independent reviewers, and sequential critic challenge for HIGH/CRITICAL findings.

How do I install Deep Dive in Claude Code?

Run `npx skills add ZaxbyHub/opencode-swarm --skill deep-dive -a claude-code`. Or copy the skill folder (.claude/skills/deep-dive in ZaxbyHub/opencode-swarm) into .claude/skills/deep-dive in your project. Claude Code loads it when a task matches its description.

How do I install Deep Dive in Codex?

Run `npx skills add ZaxbyHub/opencode-swarm --skill deep-dive -a codex`. Or copy the skill folder (.claude/skills/deep-dive in ZaxbyHub/opencode-swarm) into .agents/skills/deep-dive in your project. Codex loads it when a task matches its description.

Can I use Deep Dive in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add ZaxbyHub/opencode-swarm --skill deep-dive -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/deep-dive, .gemini/skills/deep-dive, .github/skills/deep-dive and .opencode/skills/deep-dive in your project.

What does Deep Dive need to run?

Going by SKILL.md and its folder, Deep Dive needs the command-line tools its instructions call (git).

Does Deep Dive access the network?

SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Deep Dive safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Deep Dive use?

Deep Dive is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Deep Dive use?

About 3.1k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Deep Dive?

Skills that share tags, products or a category with Deep Dive: Management Deep Dive (HKUDS/Vibe-Trading, 35k stars), Management Deep Dive (xbtlin/ai-berkshire, 17k stars), Deepgram Migration Deep Dive (jeremylongshore/tons-of-skills-marketplace, 2.8k stars) and Gamma Migration Deep Dive (jeremylongshore/tons-of-skills-marketplace, 2.8k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Deep Dive?

ZaxbyHub (a GitHub organization) maintains it in ZaxbyHub/opencode-swarm, which has 494 GitHub stars. The repository holds 91 skills in this directory. The repository was last updated on October 10, 2026.

Source: ZaxbyHub/opencode-swarm on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.