Complex task protocol
AI coding agent runtime that makes LLMs understand your codebase with repository-aware context, memory, and continuous learning
npx -y skills add ldilov/harness-forge --skill complex-task-protocolAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
- 3 stars3 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
What its author says it does
Copied from the file, not written here
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
4.8 KB, as published. Nobody here has run it
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