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 named an agent

Trigger condition: $ARGUMENTS names no agent (no --agent flag and no exact BotDefinition API name in the prose). Never invent an agent name. Instead, discover what the org actually contains — one read-only query, no pipeline yet. Use the --org alias if given, otherwise the default target org (omit --target-org):

sf data query --query "SELECT DeveloperName, MasterLabel FROM BotDefinition ORDER BY DeveloperName" \
  [--target-org <alias>] --json

If the user gave no org alias, the pipeline block defaults --org to the CLI default target org itself (sf config get target-org --json → .result[0].value) — you don't need to resolve it.

Then branch on the rows returned:

• Zero agents — tell the user the org has no Agentforce agents and stop. Do not fabricate an agent or a tree.
• Exactly one agent, or exactly one whose DeveloperName/MasterLabel matches the user's description (e.g. "support", "service", "sales") — use it, say explicitly which agent you picked and why, then run the pipeline block below with ARG_AGENT=<DeveloperName> (and ARG_ORG=<alias> if the user named one) set as its first lines, e.g. ARG_AGENT=Customer_Support_Agent. The block reads ARG_* from the environment; flags in $ARGUMENTS still override.
• Otherwise — print the block below, with the discovered agents listed as a numbered DeveloperName — MasterLabel table under the first line, and wait for the user to pick.
• Query fails (no default org, auth error) — print the block below without the list.

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

Run this block once the agent is known — from --agent <api_name> in $ARGUMENTS, or preset as ARG_AGENT=<DeveloperName> after discovery. --org (or ARG_ORG) is optional; without it the CLI default target org is used. 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_DIR> = absolute path of this skill's own directory (the folder holding
# this SKILL.md); substitute it from the skill path in context. Runtimes install
# skills in different places, so also probe the known install roots and use the
# first that actually contains this skill's scripts. An exported SKILL_ROOT wins.
_skill=agentforce-architecture-analyze
for _c in "${SKILL_ROOT:-}" "<SKILL_DIR>" \
          "${CLAUDE_PLUGIN_ROOT:+$CLAUDE_PLUGIN_ROOT/skills/$_skill}" \
          "${VIBES_SKILLS_DIR:+$VIBES_SKILLS_DIR/$_skill}" \
          "$HOME/.claude/skills/$_skill" "$PWD/.claude/skills/$_skill" \
          "${PLUGIN_ROOT:-$HOME/.vibe/skills}/$_skill"; do
  [ -n "$_c" ] || continue
  # Some stagers (ADK eval) nest the bundled files under <skill>/artifacts/.
  for _r in "$_c" "$_c/artifacts"; do
    [ -f "$_r/scripts/main.py" ] && { SKILL_ROOT="$_r"; break 2; }
  done
done
[ -f "${SKILL_ROOT:-}/scripts/main.py" ] || { echo "$_skill: scripts not found — set SKILL_ROOT to this skill's directory" >&2; exit 1; }

# Argument parser. Accepts both `--org foo` and `--org=foo`.
# `$ARGUMENTS` is the raw user input Claude Code substitutes; it may be unset
# or plain prose. Each ARG_* starts from the environment, so a value resolved
# during discovery can be preset (e.g. `ARG_AGENT=MyAgent` on the first line);
# flags in $ARGUMENTS still override.
ARG_ORG="${ARG_ORG:-}"
ARG_AGENT="${ARG_AGENT:-}"
ARG_VERSION="${ARG_VERSION:-}"
ARG_FORCE="${ARG_FORCE:-}"
ARG_REPROBE="${ARG_REPROBE:-}"
ARG_PARALLELISM="${ARG_PARALLELISM:-}"
ARG_MAX_MERMAID="${ARG_MAX_MERMAID:-}"

# Word-split $ARGUMENTS (zsh needs SH_WORD_SPLIT for that) with globbing off
# so prose like `*` never expands to filenames.
[ -n "${ZSH_VERSION:-}" ] && setopt SH_WORD_SPLIT
set -f
# shellcheck disable=SC2206
_args=(${ARGUMENTS:-})
set +f
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

# No org named anywhere: fall back to the sf CLI default target org
# (`sf config get target-org --json` -> .result[0].value). Extracted with sed
# so the block needs neither jq nor an inline interpreter; any failure leaves
# ARG_ORG empty and the usage block below fires.
if [ -z "$ARG_ORG" ] && command -v sf >/dev/null 2>&1; then
 _cfg="$(sf config get target-org --json 2>/dev/null || true)"
 _cfg="$(printf '%s\n' "$_cfg" | sed -n 's/.*"value"[[:space:]]*:[[:space:]]*"\([^"]*\)".*/\1/p' || true)"
 ARG_ORG="${_cfg%%$'\n'*}"
fi

# 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`);
>   only needed when no default target org is set (`sf config set target-org <alias>`)
>
> 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

Input	Flag	Required	Default
org_alias	--org	no	sf CLI default target org (sf config get target-org)
agent_api_name	--agent	yes	—
agent_version_api_name	--version	no	active BotVersion
force_refresh	--force	no	false (honor cache)
reprobe	--reprobe	no	false (honor 7-day channel-probe cache)
parallelism	--parallelism	no	5
max_mermaid_nodes	--max-mermaid-nodes	no	80
data_dir	--data-dir	no	~/.vibe/data/agentforce-architecture-analyze
cache_dir	--cache-dir	no	~/.vibe/cache/agentforce-architecture-analyze

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):
                          - plugins_by_planner (GenAiPluginDefinition)
                          - planner_bundle_functions (GenAiPlannerFunctionDef join)
                          - functions_by_plugins (GenAiFunctionDefinition)
                          - planner_attrs_by_parent_ids (GenAiPlannerAttrDefinition)
                          - plugin_functions_by_plugin_ids (GenAiPluginFunctionDef join)
                          - plugin_instructions_by_plugin_ids (GenAiPluginInstructionDef)
parse_bundle.py       → parse retrieved XML into normalized node shapes
parse_wave.py         → BFS expansion: flow/apex/prompt refs discovered in nodes
                          → SOQL for Flow/Apex bodies (batched by id list)
                          → Metadata retrieve ONLY for GenAiPromptTemplate (+ NGA external plugins conditionally)
finalize.py           → merge waves into metadata_tree.json
render_architecture.py → <agent>_<ver>_architecture.md + Mermaid invocation graph (capped at --max-mermaid-nodes)

Channel strategy — SOQL-first.

• Tooling SOQL for every normalized tree node (planner, plugins, functions, plugin-functions, plugin-instructions, planner-functions, planner-attrs) — 6 parallel channels keyed on planner id, plus the planner_definition_by_agent_chain seed query that resolves the planner id from the agent chain.
• Data API SOQL for Flow (by id) and Apex (by id or name) bodies — batched.
• Metadata retrieve only for two cases: (a) GenAiPromptTemplate (prompt bodies aren't cleanly exposed via Tooling SOQL), and (b) NGA external plugins when the planner is Native Generative Agent shape (skipped for classic ReAct).

This is where the 3–5× speedup comes from. A naive implementation would retrieve everything via Metadata API zips sequentially; parallel Tooling SOQL covers ~80% of the tree in a single fan-out.

Planner shapes — classic ReAct vs NGA

The skill normalizes two planner families into a single tree shape:

Shape	GenAiPlannerDefinition.PlannerType	InvocationTarget style	NGA plugins?
Classic ReAct	ReactAiPlannerV1 / SequentialPlannerIntentClassifier / etc.	DeveloperName strings	no
NGA	ConcurrentMultiAgentOrchestration / AnthropicCompatibleV1 / etc.	Sometimes 15/18-char Ids (ID-prefix routed)	yes (external plugins via Metadata retrieve)

The ID-prefix router in resolve_invocation_target.py distinguishes the two: NGA InvocationTargets that look like ids (01p… = ApexClass, 301… = Flow, etc.) get resolved via id-scoped SOQL; DeveloperName targets go through name-scoped SOQL. Unknown prefixes surface as _unresolved[] with reason="unknown-id-prefix:<prefix>" — never silently dropped.

Caching

• Tree cache: metadata_tree.json is reused unless --force is passed. Cache key includes the asset-hash of every .soql / .yaml / .mmd template bundled with the skill — bump a template, the cache busts automatically.
• Channel probe cache: 7-day TTL on the per-org sf sobject describe results that validate every field name the SOQL assets reference. A Salesforce quarterly release that renames / removes a field triggers status: PROBE_FAILED; --reprobe forces a refresh.

Prerequisites

Tool	Required
sf CLI (authenticated against the target org)	yes — sf org login web --alias <alias>, and the CLI must provide sf org auth show-access-token (startup preflight enforces this)
Python 3.10+	yes

Reference docs to load when needed

Do NOT load eagerly. Load when the user's question requires it:

• references/soql_fields.md — per-sObject field reference for the 13 sObjects this skill touches (2 Data API + 11 Tooling), with [mandatory] vs [optional] tags. Load when the user asks about a specific field, or when debugging an INVALID_FIELD SOQL error.
• references/contract.json — machine-readable schema for metadata_tree.json. Load when writing downstream tooling that consumes the tree.
• references/architecture_sections.md — section-by-section structure of the rendered <agent>_<ver>_architecture.md.

Invariants worth knowing upfront

• Pipeline is deterministic. Same (org, agent, version) + static org metadata → byte-identical <agent>_<ver>_metadata_tree.json and <agent>_<ver>_architecture.md. Only manifest timestamps drift across re-runs.
• Forward-only traversal. Every discovered ref goes forward from planner → children. No backward lookups.
• Partial results are surfaced, not silenced. Any unresolved reference lands in _unresolved[] with reason=.... STATUS=PARTIAL_OK if any channel failed; STATUS=OK only on a clean run.
• Cycle detection is per-branch. Same flow visited along its own ancestor chain emits _cycle_back_to:<path> instead of recursing. A defensive MAX_BFS_DEPTH=20 guard backs the per-branch ancestor set; real-world agents bottom out well before either limit fires. (Earlier docs claimed a hard cap of 5; that was the historical limit and was abandoned because shared utility flows like handleFlowFault tripped it on every nested tree — see config.MAX_BFS_DEPTH for the rationale.)
• Child ordering is alphabetical by api_name (case-insensitive). Topics come before non-topic plannerActions at the root level. Flow-actionCall order is NOT sorted — that's the flow author's execution sequence.