Agentforce Architecture Analyze

agentforce-architecture-analyze — declared architecture snapshot

Design-time metadata tree for one Agentforce agent: planner → topics → actions → flows → Apex → prompts → NGA plugins. Reads declared metadata only — BotDefinition, GenAiPlanner*, GenAiPlugin*, GenAiFunction*, Flow, ApexClass, GenAiPromptTemplate. Does not read runtime audit rows.

Runtime budget: 30–45s typical, ≤60s hard cap on reference fixtures. Sequential baseline would be 90–220s; parallel Tooling SOQL fan-out delivers a 3–5× speedup. Large bots with many flows scale approximately linearly — each flow metadata retrieve is one round-trip.

Runs inline — no subagent. Every phase is deterministic file processing.

If the user hasn't given enough to proceed

When invoked with no agent_api_name AND no org alias, print the following block verbatim — do not paraphrase, do not pre-run any script. Trigger condition: $ARGUMENTS is empty OR names no agent (no --agent flag and no known agent API name in the prose) OR names no org (no --org flag and no known alias).

Which agent should I document, and in which org? I need: - Agent API name — the DeveloperName of the BotDefinition (e.g. MyAgent, MySalesAgent). Not the label. - Org alias — for sf CLI auth (the alias you configured with sf org login) Optional: - Version — an agent_version_api_name like v5. If omitted, I'll resolve the active BotVersion. - `--force` — ignore cached tree; re-fetch everything. - `--reprobe` — re-run the 7-day channel-probe cache (only needed after a Salesforce release). I'll run the metadata pipeline inline. Artifacts land under ~/.vibe/data/agentforce-architecture-analyze/<org_id15>/<agent_api_name>__<agent_version>/ (overridable with --data-dir).

Pipeline invocation

When the user has supplied --org <alias> + --agent <api_name> (plus any optional flags), run this block. One python3 invocation drives the full pipeline. main.py writes .emit_ctx.json; emit_result.py reads it and prints the final === RESULT === block last to stdout.

set -euo pipefail

# zsh arrays are 1-indexed by default; bash arrays are 0-indexed.
# This block uses 0-indexed semantics throughout (_args[$i] starting at i=0),
# so under zsh + `set -u` the very first read of `_args[0]` would trip
# `parameter not set`. KSH_ARRAYS makes zsh treat arrays as 0-indexed,
# matching the bash shebang's expectation. No-op under bash.
[ -n "${ZSH_VERSION:-}" ] && setopt KSH_ARRAYS

SKILL_ROOT="${SKILL_ROOT:-${PLUGIN_ROOT:-$HOME/.vibe/skills}/agentforce-architecture-analyze}"

# Argument parser. Accepts both `--org foo` and `--org=foo`.
# `$ARGUMENTS` is the raw user input Claude Code substitutes.
ARG_ORG=""
ARG_AGENT=""
ARG_VERSION=""
ARG_FORCE=""
ARG_REPROBE=""
ARG_PARALLELISM=""
ARG_MAX_MERMAID=""

# shellcheck disable=SC2206
_args=($ARGUMENTS)
i=0
while [ $i -lt ${#_args[@]} ]; do
 tok="${_args[$i]}"
 case "$tok" in
 --org=*) ARG_ORG="${tok#--org=}" ;;
 --org) i=$((i+1)); ARG_ORG="${_args[$i]:-}" ;;
 --agent=*) ARG_AGENT="${tok#--agent=}" ;;
 --agent) i=$((i+1)); ARG_AGENT="${_args[$i]:-}" ;;
 --version=*) ARG_VERSION="${tok#--version=}" ;;
 --version) i=$((i+1)); ARG_VERSION="${_args[$i]:-}" ;;
 --parallelism=*) ARG_PARALLELISM="${tok#--parallelism=}" ;;
 --parallelism) i=$((i+1)); ARG_PARALLELISM="${_args[$i]:-}" ;;
 --max-mermaid-nodes=*) ARG_MAX_MERMAID="${tok#--max-mermaid-nodes=}" ;;
 --max-mermaid-nodes) i=$((i+1)); ARG_MAX_MERMAID="${_args[$i]:-}" ;;
 --force) ARG_FORCE="1" ;;
 --reprobe) ARG_REPROBE="1" ;;
 esac
 i=$((i+1))
done

# Usage block if required flags missing. Agent reads stderr,
# prints verbatim, and stops — does NOT pre-run main.py.
if [ -z "$ARG_ORG" ] || [ -z "$ARG_AGENT" ]; then
 cat >&2 <<'USAGE'
> Which agent should I document, and in which org?
>
> I need:
> - **Agent API name** — the BotDefinition.DeveloperName (e.g. `MyAgent`)
> - **Org alias** — for `sf` CLI auth (the alias you configured with `sf org login`)
>
> Optional flags:
> - `--version v5` — pin a specific BotVersion (default: Active+highest)
> - `--force` — bypass cache
> - `--reprobe` — force channel-probe refresh
> - `--parallelism N` — ThreadPoolExecutor size (default 5)
> - `--max-mermaid-nodes N` — cap Mermaid node count (default 80)
USAGE
 exit 2
fi

# Fresh work dir per invocation. Epoch + random suffix avoids collisions
# between concurrent runs on the same host.
WORK_DIR="/tmp/agentforce-architecture-analyze-$(date +%s)-$RANDOM"
mkdir -p "$WORK_DIR"

# Input validation at the boundary, BEFORE any python3 call.
# fs_guard exits 1 and prints an INVALID_INPUT RESULT block on failure;
# `|| exit 1` is mandatory — bare calls silently continue past failures.
python3 "$SKILL_ROOT/scripts/_shared/fs_guard.py" "$ARG_AGENT" agent_api_name api_name || exit 1
python3 "$SKILL_ROOT/scripts/_shared/fs_guard.py" "$ARG_ORG" org_alias not_empty || exit 1
python3 "$SKILL_ROOT/scripts/_shared/fs_guard.py" "$WORK_DIR" WORK_DIR symlink || exit 1
python3 "$SKILL_ROOT/scripts/_shared/fs_guard.py" "$WORK_DIR" WORK_DIR owned || exit 1
if [ -n "$ARG_VERSION" ]; then
 python3 "$SKILL_ROOT/scripts/_shared/fs_guard.py" "$ARG_VERSION" agent_version api_name || exit 1
fi

# Single python3 call drives all pipeline phases. main.py writes
# `.emit_ctx.json` into $WORK_DIR — emit_result.py then renders the
# RESULT block from that ctx. No subprocess-per-phase.
_main_args=(--org-alias "$ARG_ORG" --agent "$ARG_AGENT" --work-dir "$WORK_DIR")
[ -n "$ARG_VERSION" ] && _main_args+=(--version "$ARG_VERSION")
[ -n "$ARG_FORCE" ] && _main_args+=(--force)
[ -n "$ARG_REPROBE" ] && _main_args+=(--reprobe)
[ -n "$ARG_PARALLELISM" ] && _main_args+=(--parallelism "$ARG_PARALLELISM")
[ -n "$ARG_MAX_MERMAID" ] && _main_args+=(--max-mermaid-nodes "$ARG_MAX_MERMAID")

# main.py returns nonzero on terminal failures; we DON'T short-circuit —
# emit_result still publishes the failure RESULT block. `set -e` is
# temporarily relaxed around this single call.
set +e
python3 "$SKILL_ROOT/scripts/main.py" "${_main_args[@]}"
_rc=$?
set -e

# Final RESULT block is emit_result.py's stdout — MUST be the last thing
# stdout sees. emit_result exits 0 on render success; the bash harness
# propagates main.py's rc for the agent's exit status.
WORK_DIR="$WORK_DIR" python3 "$SKILL_ROOT/scripts/emit_result.py"
exit "$_rc"

Inputs

Outputs

All artifacts under ~/.vibe/data/agentforce-architecture-analyze/<org_id15>/<agent_api_name>__<agent_version>/ (default; override with --data-dir <path>):

<agent>_<ver>_metadata_tree.json   primary artifact — normalized planner/topic/action/flow/apex/prompt/plugin tree
<agent>_<ver>_architecture.md      human-readable section-by-section rendering (H1 + 7 numbered sections, plus a conditional Dependency graph appendix). Mermaid diagrams are embedded inside the relevant sections (Action tree, Data flow, and Dependency graph)

Pipeline — inline, no subagent

resolve_bot.py        → BotDefinition + BotVersion + planner name lookup
retrieve_planner.py   → Metadata API zip retrieve for GenAiPlannerBundle (+ NGA plugins if present)
parallel_retrieve.py  → 6 parallel Tooling SOQL channels fan out from the planner id
                          (resolved by the `planner_definition_by_agent_chain` seed query):