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Complex task protocol

Skill ldilov/harness-forge/skills/complex-task-protocol

AI coding agent runtime that makes LLMs understand your codebase with repository-aware context, memory, and continuous learning

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npx -y skills add ldilov/harness-forge --skill complex-task-protocol

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Automatic operating protocol for complex agent tasks. Use when work is multi-step, risky, cross-module, orchestration-heavy, or likely to benefit from bounded sidecar help, verification discipline, recovery checkpoints, and durable learning capture.

SKILL.md

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Complex Task Protocol

Activation

Before meaningful coding work, classify the task as simple or complex.

Activate this protocol when either condition is true:

  • at least two ordinary complexity signals are present
  • at least one high-signal condition is present

Ordinary complexity signals:

  • touches three or more files or two or more modules
  • requires investigation plus implementation
  • changes architecture, runtime behavior, generated surfaces, install flows, release flows, security, or agent instructions
  • likely needs multiple verification commands
  • has independent sidecar work such as focused evidence gathering, review, or verification
  • the initial focused read does not reveal a clear path

High-signal conditions:

  • security-sensitive change
  • release, publish, or signoff work
  • cross-target agent behavior
  • recursive, runtime, orchestration, or subagent behavior
  • persistent bug after one direct fix attempt
  • explicit user request for complex-task, subagent, learning, or orchestration behavior

Simple Task Bypass

Keep simple work lightweight. Do not announce this protocol or spawn subagents by default for:

  • direct questions that do not require code changes
  • typo, wording, or single-surface documentation fixes
  • obvious one-file edits with a clear verification path
  • narrow configuration changes with low blast radius

Execution Map

When the protocol activates, give a concise execution map before broad scanning:

  • why the task is complex enough to activate the protocol
  • what the main agent will keep on the critical path
  • which sidecar work, if any, is independent enough to delegate
  • what verification will prove the result
  • what recovery threshold will stop unbounded exploration
  • whether durable learning capture is likely

Bounded Subagent Use

Consider subagents by default when this protocol is active, but spawn them only when independent work exists.

Default limit: zero to two subagents. Use three only when the user explicitly asks or when three independent tracks are clearly available.

Allowed subagent work:

  • focused evidence gathering
  • documentation or API verification
  • security, correctness, or regression review
  • verification while the main agent continues non-overlapping work
  • isolated implementation slices with clear file ownership

Disallowed subagent work:

  • the main agent's immediate blocking decision
  • vague exploration without a concrete question
  • duplicate work already assigned elsewhere
  • broad scans without a bounded purpose
  • edits that overlap another active editor's ownership scope

Keep critical-path decisions and the next blocking task with the main agent.

Subagent Result Schema

Require every sidecar result to include:

  • finding or change
  • evidence inspected
  • files inspected
  • files changed, if any
  • risks
  • confidence
  • verification performed, if any

Coding sidecars must also state the behavior changed and known gaps.

Verification

Before claiming completion, run appropriate verification or state what could not be verified. Report:

  • checks run
  • result
  • checks not run
  • residual risk

Recovery

Stop unbounded exploration when any recovery trigger appears:

  • repeated failed attempts
  • unclear root cause after focused inspection
  • expanding scope
  • context bloat or repeated broad scans

When triggered, produce a concise recovery summary with evidence, attempts, blockers, and the next bounded step. For hard investigations, prefer the recursive investigation flow or token-budget optimizer before adding more prompt context.

Learning Capture

At the end of complex tasks, decide whether anything reusable was learned.

Durable learning categories:

  • repository convention
  • command or verification recipe
  • recurring failure mode
  • architecture or runtime decision
  • agent tactic that improved outcomes
  • target support caveat

Do not persist:

  • temporary task state
  • obvious facts already documented
  • duplicate guidance
  • private or user-specific preferences unless explicitly requested
  • speculative lessons without evidence

Prefer existing destinations:

  • project docs
  • runtime task artifacts
  • decision records
  • observability summaries
  • canonical guidance or skill surfaces

Ask before creating a new top-level document when no established destination exists.

References

  • .hforge/runtime/agent-brief.md
  • .hforge/generated/agent-command-catalog.json
  • .hforge/library/skills/token-budget-optimizer/SKILL.md
  • .hforge/library/skills/recursive-structured-analysis/SKILL.md
  • .hforge/runtime/recursive/language-capabilities.json

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