| name | agent-capability-analyzer |
| description | Runs the description-drift experiment — spawns all Claude Code agents simultaneously to collect self-reported capabilities, then compares them against static frontmatter descriptions to reveal how reliable orchestrator routing based on descriptions actually is. Use when measuring description drift across the agent fleet, re-running the capability collection experiment, analyzing a specific agent's self-reported capabilities, or auditing whether frontmatter descriptions accurately reflect agent behavior. |
Agent Capability Analyzer
Overview
Description drift occurs when an agent's frontmatter description no longer matches what the agent actually does. Orchestrators use descriptions for routing decisions — stale descriptions cause misrouting. This skill collects self-reported capabilities from agents and compares them against their static frontmatter descriptions to quantify the gap.
The dataset lives in a LevelDB store (agent-map.db/ relative to $CLAUDE_PROJECT_DIR) with two fields per agent: description (from frontmatter) and capabilities (self-reported). The dump command exports both fields to JSON for analysis.
The canonical dataset location is .claude/audits/agent-map.json.
Invocation Mode
Choose invocation mode before spawning any Tasks. The routing decision is based on how many agents are being checked.
flowchart TD
Start([Capability check requested]) --> Q1{How many agents?}
Q1 -->|1 agent| Single[Single-agent mode]
Q1 -->|2 or more agents| Multi[Multi-agent mode]
Single --> S1[Read ./resources/describe-your-capabilities.template.md]
S1 --> S2["Append to prompt:<br>'Return only the tagged content.<br>Do not run any commands.'"]
S2 --> S3["Spawn one Task with subagent_type='agent-id'<br>Template prompt — AGENT_ID_HERE substituted"]
S3 --> S4[Orchestrator receives XML-tagged text directly]
S4 --> S5{agent-map.json exists?}
S5 -->|Yes| S6["Read .claude/audits/agent-map.json<br>Extract description field for this agent"]
S5 -->|No| S7[Read description from agent frontmatter]
S6 --> S8[Run gap analysis inline — see Gap Analysis section]
S7 --> S8
Multi --> M1[Read ./resources/describe-your-capabilities.template.md]
M1 --> M2["Spawn all agent Tasks simultaneously<br>Template as-is — AGENT_ID_HERE substituted per agent"]
M2 --> M3{Agent has Bash access?}
M3 -->|Yes — agent runs update script| M4[Agent writes to LevelDB directly<br>Confirms 'Updated agent-map.db']
M3 -->|No — agent returns XML text| M5["Orchestrator writes via script:<br>node scripts/update-agent-map.mjs --name 'agent-id' --capabilities 'TEXT'"]
M4 --> M6[Wait for ALL Tasks to complete]
M5 --> M6
M6 --> M7["Dump to .claude/audits/agent-map.json<br>node scripts/update-agent-map.mjs dump --file .claude/audits/agent-map.json"]
M7 --> M8[Run gap analysis — see Gap Analysis section]
Single-agent mode — the orchestrator receives the XML-tagged response directly in the Task reply. Gap analysis runs inline: compare the capabilities text against the agent's description field, reading from .claude/audits/agent-map.json if it exists, otherwise from the agent's frontmatter. No script execution is required in this mode.
Multi-agent mode — uses LevelDB via the scripts. The orchestrator waits for all Tasks to complete before dumping. Agents that lack Bash access return their XML text and the orchestrator writes it via update-agent-map.mjs directly. The final dump produces .claude/audits/agent-map.json as the analysis input.
Template usage — ./resources/describe-your-capabilities.template.md is used in both modes. In single-agent mode, append the instruction Return only the tagged content. Do not run any commands. to the end of the prompt. In multi-agent mode, use the template as-is with AGENT_ID_HERE substituted for each agent's id.
Setup (First Time Only)
The scripts require the level npm package. Verify package.json contains:
"level": "^10.0.0"
Then install from repo root:
pnpm install
Full Experiment Workflow
Run this to collect self-reported capabilities from all agents simultaneously.
- Seed
description fields from agent frontmatter:
node $CLAUDE_PLUGIN_ROOT/scripts/populate-agent-descriptions.mjs
Output: Populated N agents, skipped M
- Read the capabilities prompt template:
Read $CLAUDE_PLUGIN_ROOT/resources/describe-your-capabilities.template.md — this is the exact prompt each agent receives.
-
Spawn all known agents simultaneously via the Task tool. For each agent, use the template as the prompt (replacing AGENT_ID_HERE with the agent's id). Each agent runs the update script itself.
Agents that lack Bash access return their capabilities as text. Collect those responses and write them directly from the orchestrator:
node $CLAUDE_PLUGIN_ROOT/scripts/update-agent-map.mjs --name "agent-id" --capabilities 'CAPABILITIES_TEXT'
-
After all Tasks complete, export the dataset:
node $CLAUDE_PLUGIN_ROOT/scripts/update-agent-map.mjs dump --file .claude/audits/agent-map.json
- Perform gap analysis — see Gap Analysis section below.
Single-Agent Analysis
When you only want to check one agent:
- Read
$CLAUDE_PLUGIN_ROOT/resources/describe-your-capabilities.template.md
- Spawn one Task with
subagent_type="<agent-id>", using the template as the prompt (replace AGENT_ID_HERE)
- The agent writes its own result to the DB
- Dump and inspect:
node $CLAUDE_PLUGIN_ROOT/scripts/update-agent-map.mjs dump --file .claude/audits/agent-map.json
- Perform gap analysis on the agent — see Gap Analysis section below.
Looking Up Agent Data by Name
To retrieve a specific agent's data from the dataset, read the JSON file and extract the entry:
node -e "const d=JSON.parse(require('fs').readFileSync('.claude/audits/agent-map.json','utf8')); const k='AGENT_NAME'; console.log(JSON.stringify(d[k],null,2));"
Replace AGENT_NAME with the agent id (e.g. plugin-creator:subagent-refactorer).
Alternatively, use the Read tool on .claude/audits/agent-map.json and locate the entry by key.
Gap Analysis
For each agent (or a target agent), compare description against capabilities and identify entries in three categories:
Category 1 — In description but absent from capabilities
These are things the frontmatter says the agent does, but the agent did not self-identify. Possible causes: description is aspirational, the agent's training doesn't surface that behavior unless prompted, or the agent's scope has narrowed since the description was written. Flag these as underclaimed capabilities.
Category 2 — In capabilities but absent from description
These are things the agent self-reports doing that are not in the frontmatter description. These cause misrouting: the orchestrator does not know the agent can do them. Flag these as undocumented capabilities.
Category 3 — Out-of-lane claims
These are capabilities the agent self-reports that fall outside the domain implied by its name and plugin context. For example, a documentation-engineer agent claiming to perform security audits, or a performance-monitor claiming to write production code. These indicate either description drift in the other direction (the agent has been trained too broadly) or self-report inflation. Flag these as scope violations.
Gap Analysis Output Format
For each agent analyzed, produce a structured report:
Agent: <agent-id>
Description: <description field>
Capabilities: <capabilities field>
Underclaimed (in description, not in capabilities):
- <item>
Undocumented (in capabilities, not in description):
- <item>
Scope violations (outside expected domain):
- <item>
Verdict: ALIGNED | MINOR_DRIFT | SIGNIFICANT_DRIFT | SCOPE_VIOLATION
Use ALIGNED when all three lists are empty or contain only minor phrasing differences. Use SIGNIFICANT_DRIFT when Category 1 or 2 lists contain substantive functional differences. Use SCOPE_VIOLATION when Category 3 is non-empty.
Resources
./resources/describe-your-capabilities.template.md — standardized prompt template for capability collection. Read this file before spawning agents../scripts/update-agent-map.mjs — LevelDB-backed metadata store (write, dump, and load modes)./scripts/populate-agent-descriptions.mjs — batch-seeds description fields from frontmatter
