GRIDLOCK-NYC Agent Capacity
Assess agent feasibility for a task using Anthropic's proven decision frameworks. Produce a dispatch plan grounded in official best practices, not guesswork.
Important
- Every classification must trace back to a specific framework from
references/decision-frameworks.md. No invented heuristics. - Report what you find. If a task is straightforward, say so. Do not manufacture complexity to justify the skill's existence.
- Read the actual codebase files a task references before assessing. Armchair scoping without reading code is unreliable.
- Take your time with decomposition. A wrong dispatch plan wastes more time than a careful assessment.
Instructions
Step 1: Identify the Task
Extract the task from one of these sources:
$ARGUMENTSpassed to the skill- A plan in the current conversation context
- A pasted task description or bug list
Restate the task in one sentence so the user can confirm scope.
Step 2: Decompose into Atomic Units
Break the task into the smallest units where each unit:
- Has a single, verifiable completion condition (test passes, grep confirms change, file exists)
- Can be described in one sentence
- Touches a bounded set of files
List every unit. Number them.
Step 3: Run the Enumeration Test
For the task as a whole, answer: "Can you list all subtasks before starting?"
- YES → This is a workflow problem. Subtasks are predictable. Classify using the six workflow patterns from
references/decision-frameworks.md(prompt chaining, routing, parallelization, orchestrator-workers, evaluator-optimizer). - NO → This needs agent-level autonomy. Identify which specific subtasks are unpredictable and why.
State the answer and reasoning explicitly.
Step 4: Classify Each Unit
Apply Anthropic's decision ladder to each atomic unit. Consult references/decision-frameworks.md for the full framework.
For each unit, assign exactly one classification:
| Classification | Criteria | Claude Code approach |
|---|---|---|
| Inline | Single prompt, 1-2 files, clear completion condition | Do it directly in main conversation |
| Explore subagent | Read-only investigation, codebase search, file discovery | subagent_type: Explore |
| General subagent | Self-contained task, clear deliverable, 3-10 tool calls | subagent_type: general-purpose |
| Sequential chain | Depends on a previous unit's output | Must run after its dependency completes |
| Human checkpoint | Ambiguous requirement, judgment call, or moving-target risk | Pause and ask the user |
Step 5: Check Delegation Specs
For every unit classified as a subagent, verify all four elements of Anthropic's delegation spec are present:
- Objective — Is the goal specific and unambiguous?
- Output format — Will the agent know what to return?
- Tool guidance — Does it know which files/tools to use?
- Task boundaries — Are scope limits explicit?
Flag any unit missing an element. A subagent dispatched without all four will "duplicate work, leave gaps, or fail to find necessary information."
Step 6: Flag Failure Patterns
Scan the full task for known failure patterns from references/decision-frameworks.md:
- Error accumulation — Long chains where each step's error compounds
- Over-delegation — Units that a single grep or one-file edit would handle faster than a subagent
- Shared-context splitting — Phases that need shared context being split across subagents
- Moving targets — Fixing unit A changes the landscape for unit B
- Vague delegation — Units missing delegation spec elements (caught in Step 5)
Step 7: Output the Dispatch Plan
AGENT CAPACITY ASSESSMENT
═══════════════════════════════════════════
TASK: [one-line summary]
UNITS: [N total]
ENUMERABLE: [YES/NO — from Step 3]
─── DISPATCH PLAN ──────────────────────────
Phase 1 — Parallel [units that can run simultaneously]
[U1] [INLINE] [one-line description]
[U2] [SUBAGENT:explore] [one-line description]
[U3] [SUBAGENT:general] [one-line description]
Phase 2 — Sequential [units depending on Phase 1]
[U4] [INLINE] [one-line description] (depends on U1)
Phase 3 — Human checkpoint
[U5] [HUMAN] [what needs user judgment and why]
─── FAILURE RISKS ──────────────────────────
[List any flagged patterns from Step 6, or "None identified"]
─── DELEGATION SPECS ───────────────────────
[For each subagent unit, show the 4-element spec or flag what's missing]
═══════════════════════════════════════════
TIER: [SIMPLE / COMPARISON / COMPLEX — from the three-tier scale]
Tier definitions (from Anthropic's multi-agent research system):
- SIMPLE — 1 agent, 3-10 tool calls. Most single-file edits and lookups.
- COMPARISON — 2-4 parallel subagents, 10-15 calls each. Module-level analysis.
- COMPLEX — 10+ subagents with divided responsibilities. Cross-codebase work.
Step 8: Offer Execution Options
After the report, offer exactly 2 options:
- "Execute this dispatch plan?" — Proceed to run the phases as described.
- "Adjust the plan?" — User modifies scope, grouping, or classifications.
Error Handling
- Task is too vague to decompose: Report: "Task lacks sufficient detail. Specify: [what's missing — files, goals, success criteria]." Do not guess at decomposition.
- Task is a single atomic unit: Report it as SIMPLE tier, INLINE classification. Do not over-decompose. A one-line fix does not need phases.
- Task references files that don't exist: Flag as a potential issue — the task may be creating them, or it may be based on stale information.
- No plan in conversation context: If invoked without
$ARGUMENTSand no plan exists, say: "No task found. Pass a task description:/agent-capacity [description]or create a plan first." - All units are inline: This is valid. Report SIMPLE tier and note that no subagents are needed. Do not manufacture reasons to use subagents.
Examples
Example 1: Simple bug fix
Input: "Fix the risk.py per-market cost check — it doesn't include existing position cost"
TASK: Fix risk.py per-market cost validation to include existing position cost
UNITS: 1
ENUMERABLE: YES
Phase 1 — Inline
[U1] [INLINE] Add existing position cost to per-market check in risk.py
FAILURE RISKS: None identified
TIER: SIMPLE
Example 2: Multi-item audit list
Input: The 12-item mm-alpha audit list (bugs + dead code + strategy gaps)
TASK: Address 12 audit findings across mm-alpha codebase
UNITS: 12
ENUMERABLE: YES
Phase 1 — Parallel (independent fixes)
[U1] [INLINE] Fix risk.py per-market cost check (1 file, 1 line)
[U8] [SUBAGENT:general] Remove 4 dead code items (_ensure_sdk_client, get_skew_cents, check_inventory_limits, get_expiry_bonus)
[U5] [SUBAGENT:explore] Investigate max_quote_size usage — confirm it's truly unused
Phase 2 — Sequential
[U2] [INLINE] Add pagination to get_fills() (depends on understanding current implementation)
[U6] [HUMAN] Amend threshold inconsistency — which behavior is correct? (needs user judgment)
[U7] [INLINE] Add market dequeue logic for out-of-zone markets
Phase 3 — Design required
[U3] [HUMAN] Adverse-selection detection — needs design approval before implementation
[U4] [HUMAN] TIME_OF_DAY_FILTERS integration — needs strategy decision
FAILURE RISKS:
- Moving target: if dead code removal (U8) changes imports, U2/U7 may need adjustment
- Over-delegation risk: U1 is a one-line fix, do not subagent it
TIER: COMPARISON