reference

name	reference
description	Component-authoring reference for HarnessX evolve rounds. Covers the @tool signature and TOOL_SPEC workflow, MultiHookProcessor (hook dispatch table, event fields, messages-mutation contract), system-prompt template editing (Cases A/B/C), config.yaml shape, and the signal→knob guide for the Configuration lever. Read when implementing any lever — Action, Control, Instruction, or Configuration. Use when this capability is needed.
metadata	{"author":"Darwin-Agent"}

Reference — HarnessX component authoring

Where things live

File	Purpose
output_dir/config.yaml	The new HarnessConfig the caller loads
output_dir/tools/<name>.py	One Tool object per file
output_dir/processors/<name>.py	One MultiHookProcessor subclass per file
output_dir/prompt/<name>	System prompt for SystemPromptProcessor

All authored files are referenced from config.yaml with absolute paths. Relative paths are rejected by the loader.

---

Authoring a new @tool

This section assumes you've already decided Action is the right lever. For whether to author a new tool at all (vs. retune an existing one, write a Control hook, or edit the prompt), see the analyze skill — lever choice lives there, authoring mechanics live here.

Before you type — write the shape spec

For anything larger than a short stdlib helper, write the spec in _meta_scratch/TOOL_SPEC.md before any code. Short sections, no prose padding:

# TOOL_SPEC — <one-line capability name>

## Capability name
What the agent literally cannot do right now.

## Class, not single task
Which class of failing tasks would this help? If only one exotic
task — abandon, this isn't a capability gap.

## Inputs the agent will pass
Real examples from failing trajectories — URLs, file tokens,
queries, record ids. Synthetic examples are disallowed; they would
pass dry-fire but tell you nothing about how the tool behaves on the
inputs the benchmark keeps presenting.

## Output shape the caller needs (not what you want to produce)
From the caller's POV: structured string? count? page-indexed text?
When the agent gets this back, what can it do with it?

## Failure modes
Named modes — auth denied, timeout, unsupported format, empty
result, rate-limited. What does the tool return in each? Never
return `""` silently — always a diagnostic string the caller can
detect.

A spec you cannot write honestly is a spec that is too broad — narrow it. The spec's value is framing the inputs concretely and pinning anti-patterns (no generic replacements, no hardcoded task ids) at the design stage.

Pre-flight

For pure-stdlib utilities, the pre-flight is small:

1. Dump the Tool dataclass fields — learn what the decorator builds before you write a call site for it:

   python -c "from dataclasses import fields; from harnessx.tools.base import Tool; \
       [print(f.name, '=', f.type) for f in fields(Tool)]"
   

2. Read the nearest builtin in harnessx/tools/builtin/ that is structurally similar (same return shape, same env access pattern, same failure mode). Match its signature and error-handling style; don't invent a new convention.

If your tool touches the network or imports a third-party package:

3. Write the imports as usual. If dry-fire reports ImportError, either fall back to stdlib or record the dep in _meta_scratch/NEEDS_FROM_HUMAN.md and try a different approach.
4. Check prior art under harnessx/tools/builtin/ for the keyword in your diagnosis — but decide by observed behaviour on this benchmark's inputs, not by whether a similarly named builtin exists (see Prior-art rule).

Minimal shape

# output_dir/tools/<name>.py
from harnessx.tools.base import tool


@tool(description="One-line capability sentence — what can the agent now do that it couldn't before.")
async def <fn_name>(<typed args>) -> str:
    # Import third-party deps at module top-level when possible;
    # inline imports are fine for narrow capabilities that don't
    # justify package-level visibility.
    #
    # Return a diagnostic string on every failure mode named in
    # TOOL_SPEC.md. Never return "" — empty output gets absorbed by
    # the caller; a tagged message like "[<tool> failed: <reason>]"
    # lets the agent fall through deliberately.
    ...

Concrete tool examples (search fallbacks, file-format parsers, domain-specific clients) belong in the recipe's <bench>-playbook skill — they encode the benchmark's input distribution and failure classes, which this skill deliberately does not.

Rules:

• Exactly one module-level Tool object per file (produced by the @tool decorator on a function).
• async def or regular def — both are supported.
• Input args must have type hints; they become the JSON Schema.
• Return a string, or something stringifiable. Tool outputs feed back to the model as text.
• Third-party imports: just import at module top-level. Dry-fire
  • replay-gate handle env uncertainty downstream.
• No hardcoded task ids, URLs, or answers in fn bodies — the literals validator rejects these (R4).

Reference from config.yaml:

tools:
  builtin: [Read, Write, Edit, Glob, Grep, Bash, WebSearch, WebFetch]
  custom:
    - file:///abs/path/output_dir/tools/<name>.py::<fn_name>

The string after :: is the function name (what the tool will be called as). If the inner agent should use your tool instead of a builtin, remove the builtin's PascalCase name from tools.builtin.

---

Authoring a new MultiHookProcessor

Mandatory pre-flight

Most contract violations in evolve rounds come from skipping one of these. Walk them in order every time, regardless of how trivial the hook looks:

1. Pick the hook deliberately. Cross-check against the hook dispatch table — an on_* method whose suffix isn't a known event becomes an import-time TypeError.
2. Dump the event dataclass (see Event fields) before typing any event.<field> access. Field names drift; don't trust memory or pattern-match from another processor.
3. If your hook mutates event.messages (only on_step_start and on_before_model are allowed to), read harnessx/core/processor.py::_validate_messages_contract end-to-end, plus check_post_hook_invariants in the same file. Then re-check your hook body against every violation category listed in Messages-mutation contract.
4. Grep prior art under harnessx/processors/control/, context/, memory/. If a builtin's observed behaviour is close to what you want, a config-layer retune beats a new file.

Minimal shape

# output_dir/processors/tool_error_tail_injector.py
from __future__ import annotations
import dataclasses

from harnessx.core.events import ToolResultEvent, Message
from harnessx.core.processor import MultiHookProcessor


class ToolErrorTailInjector(MultiHookProcessor):
    """When a tool errors, append a short tail note to its result so
    the agent sees the failure mode without re-reading the full
    stderr block."""

    _singleton_group = "tool_error_tail_injector"
    _order = 25  # after loop_detection(20), before otel(30)

    def __init__(self, tail_chars: int = 200):
        self.tail_chars = int(tail_chars)

    async def on_after_tool(self, event: ToolResultEvent):
        result = event.result or ""
        if not isinstance(result, str) or not event.is_error:
            yield event
            return
        tail = result[-self.tail_chars:].strip()
        annotated = f"{result}\n\n[tail] {tail}"
        yield dataclasses.replace(event, result=annotated)

Zero-arg-friendly constructor: the loader may instantiate with no kwargs if the YAML entry doesn't pass any.

(Benchmark-specific processors — for example, one that enforces a particular answer-format marker — belong in the recipe, authored by the meta-agent when the benchmark's playbook identifies a grader requirement that the current config isn't meeting.)

Hook dispatch table

Event class	Method name
TaskStartEvent	on_task_start
StepStartEvent	on_step_start
BeforeModelEvent	on_before_model
ModelResponseEvent	on_after_model
ToolCallEvent	on_before_tool
ToolResultEvent	on_after_tool
StepEndEvent	on_step_end
TaskEndEvent	on_task_end

Name a method on_* but not one of those → import-time TypeError. Use @on(CustomEventClass) to handle a custom event under a free name.

Event fields — look them up, never guess

python -c "
from dataclasses import fields
from harnessx.core.events import BeforeModelEvent  # swap in your event
for f in fields(BeforeModelEvent):
    print(f.name, '=', f.type)
"

Referencing a non-existent field (event.message when the field is messages) passes canonicalize but explodes on the next benchmark run. Copy-paste field names from the dataclass.

Messages-mutation contract

Only two hooks may touch event.messages: on_step_start and on_before_model. Violations surface at runtime as CONTRACT [<type>] hook=... processor=... warnings, and raise ContractViolationError under HARNESSX_CONTRACT_MODE=strict.

Single source of truth is harnessx/core/processor.py::_validate_messages_contract (plus check_post_hook_invariants for chain-level rules). The list below is a scanning checklist.

Violation categories:

• Empty messages (empty_messages) — before_model output must be non-empty.
• Length delta out of range (length_exceeded) — before_model: a processor may not remove messages, and may add at most +1 per step.
• Tail role & insertion rules — when before_model adds +1: the pre-hook tail must not already be user (content-edit only in that case), and the inserted message itself must have role="user".
• Chain-level insertion cap — across all processors in a single before_model chain, at most +1 user insertion total.
• system_window invariance — step_start may not change messages[0] when its role is "system".
• Structural-vs-content edits — step_start may not ONLY change the tail (that's before_model's job). before_model with delta==0: if tail is user, only its content may change and the prefix must be identical; if tail is not user, no change allowed.

Chain-level invariants in check_post_hook_invariants:

• system count ≤ 1 and must sit at index 0.
• raw_track and effective_track must have equal length, matching roles at every index, and matching tool_call_id for role=='tool'.

Safest default when unsure: a pure content rewrite of the existing tail user message (delta==0, prefix untouched) — valid in both step_start and before_model's user-tail branch.

Yielding

A hook is an async def generator. Yield one or more events:

• yield event — pass through unchanged
• yield dataclasses.replace(event, messages=new_tuple) — mutate a frozen dataclass field
• yield ProcessorTriggerEvent(...) — emit an observability event alongside the main one

Yielding nothing blocks the hook; don't do this unless you intentionally want to suppress a tool call or model turn.

Ordering

_singleton_group prevents two processors of the same kind stacking. _order: 10 = pre, 20 = normal, 30 = post. Builtins use token_budget=10, cost_guard=10, checkpoint=10, loop_detection=20, otel=30. Slot new guards at 15–25 unless you specifically need to run before or after a named one.

Reference from config.yaml:

processors:
  - _target_: file:///abs/path/output_dir/processors/tool_error_tail_injector.py::ToolErrorTailInjector
    tail_chars: 200

Kwargs in the YAML map directly to the processor's __init__ args.

---

Editing the inner agent's system prompt (instruction layer)

The inner agent's system prompt is produced by a SystemPromptProcessor entry in config.yaml. How you edit it depends on which builder the processor currently uses — first read current_config and look at the system_builder._target_ of the SystemPromptProcessor entry before you plan a change. The three shapes you'll see in practice:

Case A — the builder is TemplateSystemPromptBuilder

- _target_: harnessx.processors.context.system_prompt.SystemPromptProcessor
  system_builder:
    _target_: harnessx.processors.context.strategies.system_prompt.template.TemplateSystemPromptBuilder
    template_path: /abs/path/to/baseline.j2

A .j2 file exists and is the source of truth for the prompt. Do not edit it in place. Instead:

1. Read the current template at template_path.
2. Write a modified copy to output_dir/templates/<name>.j2.
3. In your new config.yaml, update the SystemPromptProcessor entry's template_path to the absolute path of your copy.

Case B — the builder is a static Python class (_StaticSystemPromptBuilder, DefaultSystemPromptBuilder, or a benchmark-provided subclass that returns a hardcoded string)

- _target_: harnessx.processors.context.system_prompt.SystemPromptProcessor
  system_builder:
    _target_: <some_package>.<SomeStaticBuilder>

There is no template_path — the prompt is baked into a Python class. Two legal paths:

• B1 (preferred — promote to a template). Read the builder's source to extract the literal prompt string it returns. Write that string (with your edits) to output_dir/templates/<name>.j2. Then swap the builder in config.yaml:

  system_builder:
    _target_: harnessx.processors.context.strategies.system_prompt.template.TemplateSystemPromptBuilder
    template_path: /abs/output_dir/templates/<name>.j2
    extra_context: {}
  

  This is the smallest diff that gives you edit power. The baseline class no longer runs — your template is now authoritative.

• B2 (overlay). Author a MultiHookProcessor with an on_before_model hook that inserts a supplementary user message before the model call. The original static builder keeps running; your processor adds to its output. Right choice when the static prompt is load-bearing (it encodes a protocol the harness depends on) and you only want to append a nudge, not replace. See the MultiHookProcessor section above for the shape.

Case C — the builder is NullSystemPromptBuilder

- _target_: harnessx.processors.context.system_prompt.SystemPromptProcessor
  system_builder:
    _target_: harnessx.processors.context.strategies.system_prompt.null.NullSystemPromptBuilder

The benchmark intentionally owns the prompt through a different mechanism (e.g. role-play turns injected per step). Editing the system prompt usually isn't the right lever — the prompt is empty on purpose, and the benchmark feeds its own instructions in-turn. Prefer Control (a processor that edits the messages the benchmark injects) or Instruction-via-a-new-skill-file over trying to stuff content into the system slot.

Rules for all instruction-layer edits

• Check the benchmark's grader before touching answer-format rules. Graders vary across benchmarks — some parse a marker string, some run a judge LLM, some run verifier scripts inside a sandbox, some use a simulated user. Removing format requirements that feed the grader silently breaks the round. Consult the <bench>-playbook skill for what the grader actually reads before editing any answer-format block.
• Keep core tool descriptions that the inner agent needs to know what's available. Which tool descriptions are load-bearing varies by benchmark — check the playbook.
• Make edits surgical. Add or modify specific sections; don't rewrite from scratch. A small targeted change is easier to attribute when it lands or reverts.
• Bundle by evidence, not by reflex. Multiple instruction changes in the same round are fine when each is independently grounded — its own cited candidate, its own cluster of affected tasks, and Pareto-positive against regression risk + cost shift. See analyze skill → Multi-lever rounds for the general rule. What to avoid is the opposite reflex: stacking two weakly-supported edits to "cover more ground", which makes attribution impossible when the round reverts. Rule of thumb: if you can write a separate ## Candidate C-N section (with its own Signal / Verified / Retroactive check / Why X not Y) for each edit, they can ship together; if two edits collapse into one candidate with the same evidence, pick the stronger framing and keep the other as a follow-up in the journal.

---

config.yaml shape — non-negotiable

processors:
  # Flat list. NEVER nest under `processors.'*':` or any hook bucket —
  # the loader rejects that shape.
  - _target_: harnessx.processors.context.system_prompt.SystemPromptProcessor
    system_builder:
      _target_: harnessx.processors.context.strategies.system_prompt.template.TemplateSystemPromptBuilder
      template_path: /abs/path/to/template.j2
      extra_context: {}
  - _target_: harnessx.processors.control.token_budget.TokenBudgetProcessor
    ratio: 0.85
  - _target_: harnessx.processors.control.cost_guard.CostGuardProcessor
    max_usd: 2.0
  - _target_: file:///abs/path/output_dir/processors/tool_error_tail_injector.py::ToolErrorTailInjector
    tail_chars: 200

tools:
  builtin: [WebSearch, WebFetch, Browser, Read, Write, Edit, Glob, Grep, Bash]
  custom:
    - file:///abs/path/output_dir/tools/<name>.py::<fn_name>

Key rules:

• processors: is a flat list, not a dict keyed by hook bucket.
• tools.custom entries are bare strings (file://...::fn_name), not _target_ dicts.
• All file:// paths are absolute; relative paths are rejected.
• Kwargs in each entry map directly to the target's __init__ args.

Starting point for edits

Copy the current config, then edit the copy — smaller diff = fewer ways to break canonicalize:

cp <current_config_path> <output_dir>/config.yaml

No-op case

When no change is warranted, a valid no-op config.yaml is a byte- for-byte copy of <current_config_path>. Still write it; the caller treats absence of config.yaml as a failure.

Common canonicalize failures

Error	Likely cause
'processors:' must be a flat list	Legacy hook-bucket form. Remove the '*': wrapper; emit a flat list of _target_ dicts.
'tool_registry:' is not a recognised key	Legacy format. Put tools under tools.builtin / tools.custom.
tools.custom entries must be '...' strings	You wrote {_target_: file://...} instead of the bare string. Drop the _target_: wrapper.
TemplateSyntaxError	Fix the Jinja in the .j2 file named in the error.
FileNotFoundError on template_path	You referenced a template you forgot to write.
ModuleNotFoundError from file:// import	Authored file path typo, or ::symbol doesn't match the exported name.
singleton_group ... conflicting registrations	Same processor _singleton_group listed twice.

Verify before end_turn:

python -c "from harnessx.core.harness import HarnessConfig; HarnessConfig.from_yaml_file('<output_dir>/config.yaml').canonicalize(); print('OK')"

Or use the validate skill's CLI suite.

---

Tuning existing builtins (Configuration layer)

When the lever is Configuration rather than Action/Control/Instruction, edit kwargs on an existing processor in place. The common tunables:

# Cost ceiling per task
- _target_: harnessx.processors.control.cost_guard.CostGuardProcessor
  max_usd: 2.0

# Loop detection — fingerprints recent steps
- _target_: harnessx.processors.control.loop_detection.LoopDetectionProcessor
  window_size: 10       # how many steps to compare
  threshold: 4          # consecutive matching fingerprints before kill

# Token budget — compaction trigger
- _target_: harnessx.processors.context.token_budget.TokenBudgetProcessor
  ratio: 0.85           # fraction of model context window to use

# Checkpoint cadence
- _target_: harnessx.processors.control.checkpoint.CheckpointProcessor
  every_n: 5            # save state every N steps

Judgement on when to move each knob is trajectory-driven. Use the signal table below as a starting point, read the trajectory evidence, pick the direction, retune, verify with canonicalize. The orchestrator's gating layer will revert a miscalibration.

When to move which knob

Signal in trajectories	Knob to move	Direction
Recurring exit_reason: budget_exceeded while agent was still making progress	CostGuardProcessor.max_usd	raise
Most failures hitting the step limit (steps = max_steps)	Not a config knob — record as NEEDS_FROM_HUMAN: raise max_steps in runner	—
exit_reason: loop_detected on tasks where the agent was visibly making progress	LoopDetectionProcessor.threshold	raise
Agent repeats the same action many times before loop detection fires	LoopDetectionProcessor.threshold or window_size	lower
Compaction artifacts visible (lost context mid-task, agent re-asking already-answered questions)	TokenBudgetProcessor.ratio	lower

The signal column describes a pattern across multiple tasks, not a single incident. A one-off is idiosyncratic — log it as NEEDS_FROM_HUMAN rather than tuning a knob.

---

Prior-art grep

Before authoring anything new, check what's already there:

• harnessx/processors/control/ for Control-lever components (token_budget.py, cost_guard.py, loop_detection.py, parse_retry.py, repeated_edit_detector.py, self_verification.py, tool_failure_guard.py, …)
• harnessx/tools/builtin/ for Action-lever components (local file reads, web search, HTTP fetch, shell commands).

The goal of this grep is mechanical orientation — match naming conventions, signature style, error-handling idioms of the nearest sibling. The decision of whether a similarly named builtin covers your need lives in analyze skill → Disambiguating adjacent levers, and turns on observed behaviour on this benchmark's trajectories, not on filename similarity.

---

Integration check — orchestrator post-flight

After your end_turn, the orchestrator runs:

1. Validity (always): canonicalize + dry_fire + contract
   • synthetic_task replay. Each in turn; first failure stops and fails the round.
2. Policy (when config diff is non-empty): novelty (no re-proposing reverted hypotheses) + evidence (candidates.md
   • cited_candidates cross-ref).
3. Advisory (never blocks): literals writes to _meta_scratch/LITERALS_WARNING.md when hardcoded task-shaped literals appear in authored code. Fix them when you see the warning — they're not fatal but they signal a candidate that likely won't generalize.

Run the validity checks yourself before end_turn via the validate skill — that catches the common bugs without paying the orchestrator-replay round cost.

---

Anti-patterns — apply to tools, processors, and templates alike

1. Generic replacement. "A better WebFetch that handles everything." Scope to the cluster. Existing tools compete in the general case; new tools compete on a specific class of inputs.
2. Lifting an implementation from outside this repo. Other teams' blog posts / papers describe shapes that worked on their input distribution. You have not seen their failing trajectories. Use outside work as shape inspiration, not as code.
3. Skipping the shape spec (TOOL_SPEC.md). Typing @tool without the spec produces components that don't match the caller's actual need.
4. Multi-responsibility component. "fetch-and-extract-and- summarize" → split into separate @tools or processor hooks. One responsibility per file. The agent composes; you don't.
5. Sprawling third-party deps. pdfplumber + pandas + openpyxl + sympy in one tool. Pick one axis per tool.
6. Silently suppressing failure with "" / None. Return a diagnostic string ([<tool> failed: <reason>]), not empty. Empty gets absorbed; diagnostics let the agent fall through.
7. Hardcoded task ids, URLs, or answers in fn bodies — R4 rule. Triggers a literals advisory warning; not fatal on its own, but such components almost never survive the next benchmark round (they only work on the one task the author memorised).
8. For processors: mutating event.messages outside on_step_start / on_before_model — contract violation, automatically rejected.
9. For templates: editing the target's source .j2 in place — always copy to output_dir/templates/<name>.j2 and point template_path at the copy.

---

End-of-turn checklist

• _meta_scratch/TOOL_SPEC.md exists when the change is non-trivial (any new tool / processor beyond a stdlib helper)
• New files under output_dir/tools/ or output_dir/processors/ are referenced from config.yaml with absolute file:// paths
• canonicalize validator → {"ok": true}
• dry_fire / contract validators → "ok": true when you authored new tools / processors / templates (see validate skill)
• literals validator run (advisory, non-blocking): if it reports findings, fix them before end_turn — they don't fail the round but the component likely won't survive the next benchmark round
• memo_path has a new ## Round N entry with YAML frontmatter (see journal skill): round, hypothesis_id, levers, predicted_affected, plus prose with Why / Changes / Evidence

---

Source: Darwin-Agent/HarnessX — distributed by TomeVault.