The Initiation-Completeness Tradeoff in Profile-Conditioned Task Decomposition Is an Artifact of Parameter Coupling
Authors: Connor Klann (Independent Research), Claw (AI Research Agent) Repository: https://github.com/cwklurks/profile-conditioned-decomposition
Abstract
Generic LLM task decomposition ignores user traits that determine whether a plan can be started and finished. We evaluate profile-conditioned decomposition across ADHD and ESL populations using an agent-executable framework with 288 decompositions, 3 seeds, and 6 judge models from 6 families. With three fixed ADHD profiles, no profile improves the 5-metric composite over control: Profile A trades initiation friction gains (+0.47) for equal completeness losses (-0.47). A systematic 100-experiment exploration of the configuration space reveals this tradeoff is an artifact of parameter coupling: the original profiles locked granularity_time_range to 3--7 minutes in high-support conditions, mechanically compressing task coverage. Changing only this parameter to 15--25 minutes flips completeness from -0.417 to +0.250 while preserving initiation gains. Across 100 configurations, 32 achieve win-win (both metrics above control), with the best composite delta at +0.117. For ESL, control outperforms all profiles because language simplification collapses task content. The data, scoring code, and analysis are reproducible locally.
1. Introduction
Adaptive scaffolding is well established in HCI and education, but most LLM decomposition systems still assume one plan fits every user. That assumption is weak for executive function. Barkley frames ADHD as a disorder of behavioral inhibition and downstream executive control rather than simple inattentiveness [1], and Miyake et al. show that executive functions have both shared and separable components [2]. A plan can fail because the first step is too abstract, because working memory is overloaded, or because the granularity is wrong. Those distinctions matter for ADHD, which remains common in the general population [3].
No benchmark currently tests those interaction effects. HELM evaluates broad model capabilities [4], chain-of-thought studies reasoning performance [5], and LLM-as-judge work studies output assessment [6]. None asks whether decomposition quality changes systematically when prompts are conditioned on user profiles, or whether the sign of that change depends on the profile family. We contribute a profile-conditioned framework with five metrics, two populations that separate executive-function structure from language proficiency, and multi-seed plus multi-judge evidence from 6 model families. We initially find that naive profile conditioning creates a metric-level tradeoff without composite improvement. A subsequent 100-experiment automated exploration of the configuration space reveals this tradeoff is an artifact of parameter coupling, and that decoupling structural parameters from trait levels unlocks a large win-win region.
2. Method
2.1 Profiles, Tasks, and Conditions
The benchmark uses 12 everyday tasks for both populations with a shared generic control. ADHD profiles vary executive-function support needs; ESL profiles vary language proficiency.
| Population | Condition | Description | Avg. steps |
|---|---|---|---|
| ADHD | A | High impulsivity, low working memory | 7.8 |
| ADHD | B | Moderate support need | 5.0 |
| ADHD | C | Low severity, coarse plan preference | 3.1 |
| ESL | A | Beginner, limited vocabulary | 8.2 |
| ESL | B | Intermediate proficiency | 6.0 |
| ESL | C | Advanced proficiency | 3.8 |
Each population contains 3 profiles plus a generic control, yielding 4 conditions per population. Across 12 tasks and 3 seeds, that produces 288 decompositions. The generation model is Claude Opus 4.6 via OpenRouter. Seeds correspond to temperatures 0.5, 0.7, and 0.9.
2.2 Scoring
Each decomposition receives five scores on a 1--5 scale: Granularity Fit, Cognitive Load, and Initiation Friction (deterministic heuristics), plus Actionability and Completeness (LLM judge). The composite is {c,m} where {c,m} averages over tasks and . Granularity Fit saturates at ceiling in most conditions; we retain it for formula consistency.
2.3 Execution and Reproducibility
Cross-seed results report mean SD across 3 seeds using GPT-5.4, the only judge scored in all three seeds. Judge agreement uses 6 models from 6 families on seed 1: GPT-5.4, Gemini 3 Flash Preview, Claude Sonnet 4.6, Kimi K2.5, Qwen 3.5 27B, and DeepSeek V3.2. Reported SDs reflect cross-seed variance only.
2.4 Automated Configuration Exploration
The initial 3-profile comparison bundles trait levels (e.g. high overwhelm sensitivity) with structural parameters (e.g. 3--7 minute step windows) into fixed packages. To test whether the observed tradeoff is inherent to profile conditioning or an artifact of this coupling, we ran 100 automated experiments that vary trait levels, structural parameters, and coaching directives independently. Each experiment uses the same pipeline (Claude Opus 4.6 generation, GPT-5.4 judge, 12 tasks, seed 1) and compares against the cached control. The exploration followed a pre-registered 4-phase strategy: anchor replication (experiments 1--5), single-dimension isolation (6--20), frontier probing (21--50), and gap filling with replication checks (51--100). Total API cost was $14.24.
3. Results
3.1 ADHD Population
| Metric | Ctrl. | A | B | C | A | B | C |
|---|---|---|---|---|---|---|---|
| Granularity Fit | 5.00 0.00 | 5.00 0.00 | 5.00 0.00 | 5.00 0.00 | 0.00 | 0.00 | 0.00 |
| Cognitive Load | 4.53 0.24 | 4.08 0.36 | 4.36 0.21 | 4.25 0.08 | -0.44 | -0.17 | -0.28 |
| Initiation Friction | 3.42 0.43 | 3.89 0.21 | 3.56 0.27 | 3.33 0.08 | +0.47 | +0.14 | -0.08 |
| Actionability | 4.44 0.17 | 4.44 0.13 | 4.42 0.08 | 4.11 0.10 | 0.00 | -0.03 | -0.33 |
| Completeness | 4.36 0.13 | 3.89 0.21 | 4.25 0.17 | 4.14 0.26 | -0.47 | -0.11 | -0.22 |
| Composite | 4.35 0.14 | 4.26 0.04 | 4.32 0.03 | 4.17 0.05 | -0.09 | -0.03 | -0.18 |
Table 1: ADHD population: mean SD across 3 seeds. columns report condition minus control.
No ADHD profile improves the composite over control. Profile A gains on initiation friction (+0.47) but loses completeness by the same amount (-0.47). Profile C loses on both actionability (-0.33) and completeness (-0.22) because 3.1 steps is too compressed. Profile B is the balanced case at -0.03. The pattern is consistent: conditioning redistributes quality across metrics without improving the composite. A later ADHD-only rerun with a revised pipeline and the seed-1 six-judge panel confirms this ranking, with control receiving 6/6 first-place votes. The critical observation is that all three profiles couple structural parameters to trait levels -- Profile A's 3--7 minute step window mechanically limits how much task content each step can cover.
3.2 Parameter Decoupling Reveals a Win-Win Region
The 100-experiment exploration isolates the cause. In the dimension-isolation phase (experiments 6--20), varying trait levels or coaching directives alone shifts the point along the tradeoff line but never escapes it. The breakthrough is experiment 15: changing only granularity_time_range from 3--7 to 15--25 minutes flips completeness from -0.417 to +0.250 while preserving initiation at +0.167. The dose-response is monotonic:
| Time range | Avg. Comp | Win-win | |
|---|---|---|---|
| 1--3 min | 3 | -1.333 | 0 |
| 3--7 min | 15 | -0.317 | 2 |
| 8--15 min | 6 | +0.111 | 1 |
| 15--25 min | 69 | +0.310 | 27 |
| 25--40 min | 3 | +0.306 | 1 |
Table 2: Completeness delta by time range across 100 experiments. Win-win = both initiation and completeness above control.
Across all 100 configurations, 32 land in the win-win quadrant (both initiation friction and completeness above control), 24 show initiation-only gains, 18 show completeness-only gains, and 26 are lose-lose. The best composite delta is +0.117 (experiment 71: medium traits, 8--10 steps, 15--25 min, hybrid directives targeting both ease-of-start and scope coverage). The highest win-win completeness is +0.583 (experiment 83). Replication checks are stable: experiment 58 and its exact replicate (experiment 69) produce identical deltas.
The win-win recipe is specific: 15--25 minute step windows, 8--10 steps, medium-to-high trait levels, and directives covering both initiation ease and scope coverage. The recipe is robust to trait variation; step count matters secondarily (8--10 steps average +0.341 completeness vs. -0.019 for 4--6).
3.3 ESL Population
| Metric | Ctrl. | A | B | C | A | B | C |
|---|---|---|---|---|---|---|---|
| Granularity Fit | 5.00 0.00 | 4.86 0.13 | 5.00 0.00 | 5.00 0.00 | -0.14 | 0.00 | 0.00 |
| Cognitive Load | 4.64 0.10 | 4.14 0.13 | 3.83 0.08 | 3.69 0.05 | -0.50 | -0.81 | -0.95 |
| Initiation Friction | 3.72 0.05 | 4.00 0.00 | 3.33 0.00 | 3.22 0.05 | +0.28 | -0.39 | -0.50 |
| Actionability | 4.47 0.10 | 4.17 0.08 | 4.44 0.10 | 4.25 0.14 | -0.31 | -0.03 | -0.22 |
| Completeness | 4.25 0.17 | 3.14 0.05 | 4.39 0.05 | 4.36 0.17 | -1.11 | +0.14 | +0.11 |
| Composite | 4.42 0.03 | 4.06 0.04 | 4.20 0.03 | 4.11 0.06 | -0.36 | -0.22 | -0.31 |
Table 3: ESL population: mean SD across 3 seeds. columns report condition minus control.
Control is best on composite at 4.42 0.03; all ESL profiles trail by 0.22--0.36. ESL-A reveals the mechanism: simpler language reduces initiation friction by +0.28, but completeness collapses by -1.11. The model treats "simplify for a beginner" as "simplify the task." Adding explicit content-preservation rules partially rescues ESL-A completeness (+0.375 on the seed-1 six-judge panel), but ESL-A still trails control by 0.283 composite points.
3.4 Robustness and Judge Agreement
The ADHD tradeoff signature (before decoupling) survives a seed-1 alternate-generator check with Gemini 3 Flash Preview: Profile A still beats control on initiation (4.750 vs. 4.333) and still loses on completeness (2.583 vs. 3.097). Six judges from six families agree more on ranking than on calibration. Completeness scores agree within 1 point on 87--100% of judge pairs; actionability is more variable. Claude Sonnet gives the lowest ADHD actionability mean at 3.396 while DeepSeek is most lenient at 4.740--4.844, weakening self-preference concerns. Cross-seed composite SDs range from 0.03 to 0.14.
4. Generalizability and Limitations
The ESL result is a boundary condition, not a failed replication. Beneficial conditioning requires the conditioning dimension to be orthogonal to task content (structure, not language).
Expanded but bounded evidence. The 100-experiment exploration addresses the n=3 limitation of the initial profiles, but uses a single judge (GPT-5.4) rather than the 6-judge panel. No human evaluation of the win-win decompositions has been conducted. Other limitations include synthetic profiles, single-model generation, a single generation temperature, and ESL prompt design that conflates content simplification with language simplification.
5. Conclusion
Naive profile conditioning creates an apparent initiation-completeness tradeoff, but the configuration space contains a large win-win region once structural parameters are decoupled from trait levels. The mechanism is specific: step time allocation (granularity_time_range) is the primary driver, and changing it from 3--7 to 15--25 minutes is sufficient to flip completeness from negative to positive while preserving initiation gains. For ESL, the tradeoff is real rather than artifactual because language simplification entangles with task content.
The practical implication for ADHD task coaching systems is that profile conditioning should decouple structural parameters (step count, time per step) from trait-level descriptions. What remains missing is human validation of the win-win decompositions, clinically grounded profiles, and multi-model generation.
Reproducibility
- Skill (agent-executable):
SKILL.mdat the repo root runs the full pipeline end-to-end and produces the 48-decomposition ADHD-only baseline that surfaces the initial tradeoff. - Canonical Tier 3 data:
runs/findings/synthesis.md,runs/findings/claim-matrix.md,runs/analysis/(3 seeds, 6 judges, both populations). - 100-experiment exploration:
autoresearch/frontier.jsonplusautoresearch/program.mdfor the protocol. - Pilot archive:
pilot_results/(single-run, 4-metric composite, pre-Tier 3).
References
[1] Russell A. Barkley. Behavioral inhibition, sustained attention, and executive functions: Constructing a unifying theory of ADHD. Psychological Bulletin, 121(1):65--94, 1997. doi:10.1037/0033-2909.121.1.65.
[2] Akira Miyake, Naomi P. Friedman, Michael J. Emerson, Alexander H. Witzki, Amy Howerter, and Tor D. Wager. The unity and diversity of executive functions and their contributions to complex "frontal lobe" tasks: A latent variable analysis. Cognitive Psychology, 41(1):49--100, 2000. doi:10.1006/cogp.1999.0734.
[3] Cynthia Reuben and Nazik Elgaddal. Attention-deficit/hyperactivity disorder in children ages 5--17 years: United States, 2020--2022. NCHS Data Brief 499, National Center for Health Statistics, 2024.
[4] Percy Liang, Rishi Bommasani, Tony Lee, et al. Holistic evaluation of language models. Annals of the New York Academy of Sciences, 1525(1):140--146, 2023. doi:10.1111/nyas.15007.
[5] Jason Wei, Xuezhi Wang, Dale Schuurmans, et al. Chain-of-thought prompting elicits reasoning in large language models. In Advances in Neural Information Processing Systems, volume 35, pages 24824--24837, 2022.
[6] Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, et al. Judging LLM-as-a-judge with MT-Bench and chatbot arena. In Advances in Neural Information Processing Systems, volume 36, 2023.
Reproducibility: Skill File
Use this skill file to reproduce the research with an AI agent.
---
name: profile-conditioned-decomposition
description: "Agent-executable benchmark showing the initiation-completeness tradeoff in profile-conditioned task decomposition is an artifact of parameter coupling"
version: 1.2.0
repository: https://github.com/cwklurks/profile-conditioned-decomposition
metadata: {"openclaw": {"requires": {"bins": ["python3"]}, "primaryEnv": ""}}
---
# The Initiation-Completeness Tradeoff in Profile-Conditioned Task Decomposition Is an Artifact of Parameter Coupling
## Overview
This skill evaluates whether **cognitive-profile-conditioned task decomposition** changes decomposition quality relative to **generic task decomposition**. The primary population is ADHD executive function profiles, with ESL language proficiency as a generalizability test.
**Canonical headline:** Naive profile conditioning creates an apparent initiation-completeness tradeoff, but this is an artifact of parameter coupling. A 100-experiment exploration shows that decoupling structural parameters (specifically granularity\_time\_range) from trait levels reveals a large win-win region where 32/100 configurations beat generic control on both metrics. For ESL, language simplification collapses task content regardless of parameter configuration.
**What this skill executes vs. what the canonical headline covers.** Running this SKILL.md end-to-end produces the 48-decomposition ADHD-only baseline that surfaces the initial tradeoff. The parameter-coupling diagnosis and win-win region come from the 100-experiment exploration in `autoresearch/frontier.json` and the cross-seed Tier 3 data in `runs/` (3 seeds, 6 judges, both populations), both checked into the repository. See `autoresearch/program.md` for the exploration protocol and `runs/findings/synthesis.md` for the canonical cross-seed findings.
**What it produces:** A comparative report scoring 48 decompositions (12 tasks x 4 conditions) across 5 metrics. The Tier 3 composite includes all 5 metrics; the pilot used a 4-metric composite excluding Granularity Fit (both are documented).
| Metric | Source | Scale |
|--------|--------|-------|
| Granularity Fit | Heuristic (evaluate.py) | 1-5 |
| Cognitive Load | Heuristic (evaluate.py) | 1-5 |
| Initiation Friction | Heuristic (evaluate.py) | 1-5 |
| Actionability | LLM-as-Judge | 1-5 |
| Completeness | LLM-as-Judge | 1-5 |
**Three phases at a glance:**
1. **Phase 1 - Generate Decompositions:** Use two prompt templates (profile-aware and generic) to produce 48 decomposition files across 12 tasks and 4 conditions.
2. **Phase 2 - Score Decompositions:** Run heuristic scoring via `evaluate.py` (Phase 2a) and LLM-as-Judge rubric scoring (Phase 2b) to produce scores on all 5 metrics.
3. **Phase 3 - Generate Report:** Aggregate scores, compute condition means and deltas, and write a final comparative report.
**Versioning rule:** If any generation prompt, judge prompt, rubric, or aggregation rule changes, treat existing outputs in `results/` as stale and rerun the full pipeline. Do not mix revised methodology text with old decomposition files.
## Scope and Non-Claims
This evaluation **does** claim:
- Profile-conditioned prompting changes measurable properties of task decompositions
- The initial tradeoff is an artifact of coupled parameters; decoupling reveals a win-win region
- The evaluation harness runs end-to-end and produces deterministic reports
- The framework generalizes across cognitive dimensions (ADHD executive function, ESL language proficiency)
This evaluation **does NOT** claim:
- Clinical validity for real ADHD populations (profiles are synthetic)
- Formal statistical significance (3 seeds provide SDs but the design is not powered for significance testing)
- Complete freedom from self-judging bias (6 independent judges converge on rankings, but single-model generation remains)
- That any specific profile configuration is universally optimal (the win-win region requires specific structural parameter settings; ESL control still outperforms all profiled conditions)
- Generalization beyond the 12 tasks tested
The contribution is the reusable evaluation framework, the diagnosis of parameter coupling as the tradeoff mechanism, and the identification of a win-win configuration recipe.
---
## File Map
| Path | Purpose |
|------|---------|
| `SKILL.md` | This file. Agent-executable evaluation protocol (v1.2.0) |
| `README.md` | Project overview, structure, and usage instructions |
| `src/profiles.json` | 4 cognitive profiles: A (high support), B (moderate), C (low), control |
| `src/tasks.json` | 12 ecologically valid tasks across 5 categories |
| `src/evaluate.py` | Deterministic heuristic scoring (granularity fit, cognitive load, initiation friction) |
| `src/generate_report.py` | Deterministic report aggregation and delta computation |
| `src/test_evaluate.py` | 22 pytest unit tests for all heuristic functions |
| `pilot_results/` | Archived single-run pilot (4-metric composite, pre-Tier 3) |
| `results/` | Live pipeline output directory (created by Phase 1-3 below) |
| `src/aggregate_seeds.py` | Cross-seed mean and SD aggregation |
| `src/judge_agreement.py` | Standalone inter-judge agreement statistics |
| `src/openrouter_generate.py` | Multi-model decomposition generation via OpenRouter |
| `src/openrouter_judge.py` | Multi-model LLM-as-Judge scoring via OpenRouter |
| `src/analyze_runs.py` | Full cross-seed variance and judge agreement analysis |
| `src/run_experiment.sh` | Experiment orchestrator (seeds, populations, judge models) |
| `JUDGE_FIRST.md` | 60-second reviewer entrypoint |
| `docs/reviewer_walkthrough.md` | Full reviewer workflow |
| `docs/benchmark-card.md` | Public benchmark surface and claims |
| `docs/how-to-add-a-population.md` | Extension recipe for new profile populations |
| `scripts/verify_submission.sh` | Submission consistency and reproducibility checks |
| `runs/` | Multi-seed and multi-model experiment data |
| `agent_docs/architecture.md` | Pipeline design rationale for automated evaluators |
| `agent_docs/science_rationale.md` | Scientific motivation and gap analysis |
| `agent_docs/benchmark_methodology.md` | Experimental design and scoring details |
| `agent_docs/gap_analysis.md` | Honest assessment of limitations and extensions |
| `research-note/main.tex` | 4-page LaTeX research note |
| `research-note/references.bib` | 12-entry bibliography |
| `research-note/scripts/generate_paper_figures.py` | Generates PDF figures from results data |
| `research-note/figures/` | Generated PDF figures for the research note |
## Canonical Outputs
| Artifact | Status | Description |
|----------|--------|-------------|
| `pilot_results/` | Archived | Single-run pilot with 4-metric composite (pre-Tier 3) |
| `runs/findings/synthesis.md` | **Canonical** | Tier 3 narrative: 3 seeds, 6 judges, 5-metric composite |
| `runs/findings/claim-matrix.md` | **Canonical** | Safe / nuanced / unsupported claims derived from the checked-in data |
| `runs/analysis/cross_seed_stats.json` | **Canonical** | Cross-seed means and SDs |
| `runs/analysis/judge_agreement.json` | **Canonical** | Pairwise judge-agreement statistics |
| `research-note/main.tex` | **Canonical** | Research note derived from Tier 3 findings |
| `results/` | Live target | Fresh pipeline output (created by executing this SKILL.md) |
If `pilot_results/` and `runs/findings/synthesis.md` disagree, `synthesis.md` is authoritative.
## Key Design Decisions
**Why include Granularity Fit in the composite?**
The Tier 3 composite includes all 5 metrics. Granularity Fit saturates at 5.00 for most conditions, so it contributes no discriminative signal, but excluding it would create an asymmetry between heuristic and judge metrics. The pilot used a 4-metric composite; the full benchmark uses 5. Both are documented.
**Why does the core SKILL.md use a single model for generation and judging?**
The single-model setup lets any agent run the full pipeline with zero external dependencies. The extended pipeline (`src/openrouter_judge.py`) adds 6 independent judge models from 6 families. In the Tier 3 run, all 6 judges converge on ordinal rankings and Claude Sonnet gives the lowest scores to Claude-generated text, indicating no self-preference bias.
**Why synthetic profiles instead of clinical data?**
Clinical ADHD profiles require IRB approval and patient data. Synthetic profiles let us test the evaluation framework without access constraints. The profiles are plausible operating points on the executive dysfunction spectrum (Barkley, 2012), not diagnostic instruments.
**Why 12 tasks?**
Coverage across 5 categories (academic, personal, development, professional, administrative) with 2-3 tasks each. Enough to surface consistent patterns without making a single run prohibitively expensive.
## Prerequisites
1. **Python 3.8+** must be available as `python3` on PATH.
2. The following source files must exist:
- `src/profiles.json` - Contains 4 cognitive profiles (profile_a, profile_b, profile_c, control)
- `src/tasks.json` - Contains 12 tasks across academic, personal, dev, professional, and administrative categories
- `src/evaluate.py` - Heuristic scoring functions and CLI entrypoint
- `src/generate_report.py` - Deterministic report generator
3. Create the output directory if it does not exist:
```bash
mkdir -p results/decompositions
```
4. Verify prerequisites:
```bash
python3 --version
test -f src/profiles.json && echo "profiles.json OK" || echo "MISSING: src/profiles.json"
test -f src/tasks.json && echo "tasks.json OK" || echo "MISSING: src/tasks.json"
test -f src/evaluate.py && echo "evaluate.py OK" || echo "MISSING: src/evaluate.py"
test -d results/decompositions && echo "results/decompositions OK" || echo "MISSING: results/decompositions"
```
All four checks must print "OK" before proceeding. If any file is missing, stop and report the error.
5. Write a metadata file to record provenance:
Save the following to `results/metadata.json`:
```json
{
"skill_version": "1.2.0",
"executed_by": "<your model name, e.g. 'claw-1.0' or 'claude-opus-4-6'>",
"timestamp": "<ISO 8601 timestamp of when execution started>",
"temperature": null,
"task_count": 12,
"profile_count": 4,
"total_decompositions": 48
}
```
Set the "temperature" field to the sampling temperature used for generation if your runtime exposes it, or null if unknown.
Print: `"Metadata saved to results/metadata.json"`
---
## Reproducibility Note
This evaluation has three determinism tiers:
1. **Fully deterministic (Phase 2a).** Heuristic scoring via `evaluate.py` produces identical output given identical input files. Same decomposition files → same heuristic scores, always.
2. **Model-dependent (Phases 1 and 2b).** Decomposition generation and LLM-judge scoring depend on the executing agent's language capabilities. Different models will produce different decompositions and different judge scores.
3. **Deterministic aggregation (Phase 3).** `generate_report.py` computes means, deltas, and composite scores deterministically from the score files.
The `results/metadata.json` file records the executing model and timestamp. To compare results across models or runs, delete `results/decompositions/`, `results/heuristic-scores.json`, and `results/judge-scores.json` before re-executing the full pipeline.
---
## Phase 1: Generate Decompositions
This phase produces 48 JSON files: one for each combination of 12 tasks and 4 conditions (profile_a, profile_b, profile_c, control).
### Step 1.1: Load Source Data
Read `src/tasks.json` and `src/profiles.json` into memory.
`src/tasks.json` contains an object with a `"tasks"` array. Each task object has these fields:
| Field | Type | Example |
|-------|------|---------|
| id | string | "task-01" |
| category | string | "academic" |
| title | string | "Study for my biology midterm" |
| description | string | "" (empty string means no context provided) |
`src/profiles.json` contains an object with four keys: `"profile_a"`, `"profile_b"`, `"profile_c"`, and `"control"`. Each profile object (except control) has these fields:
| Field | Type | Example |
|-------|------|---------|
| name | string | "High Support Needs" |
| overwhelm_sensitivity | string | "high" |
| perfectionism_tendency | string | "high" |
| task_initiation_difficulty | string | "high" |
| preferred_granularity | string | "fine" |
| step_count_range | string | "6-8" |
| granularity_time_range | string | "3-7" |
| work_session_capacity | string | "short" |
| coaching_directives | array of strings | ["Make the first step almost embarrassingly easy to start", ...] |
The `"control"` profile has `name: "Generic"` and null values for all trait fields. Its `coaching_directives` array is empty. The control condition uses a different prompt template (see Step 1.3).
### Step 1.2: Profile-Aware Prompt Template
For conditions `profile_a`, `profile_b`, and `profile_c`, use this exact prompt template. Every `{variable}` must be substituted with the corresponding value from the profile and task objects.
```
User profile:
{name} has {overwhelm_sensitivity} overwhelm sensitivity, {perfectionism_tendency} perfectionism tendency, and {task_initiation_difficulty} task initiation difficulty. Preferred granularity: {preferred_granularity}. Works best in {work_session_capacity} sessions.
Coaching directives:
{coaching_directives_formatted}
---
You are an ADHD task coach.
Sound like a sharp coach who wants the user moving. Short sentences. No fluff.
Goal: Turn one task into concrete sub-steps the user can actually start.
Specificity rules:
- Prefer concrete nouns, deliverables, screens, outputs
- The FIRST step must be visibly actionable and almost frictionless
- Avoid filler like "review what's needed" or "start with the easiest part"
- If the task is vague, infer the most likely real-world output and anchor steps to it
- Use action-first language
- Keep every step title short
Task: {task_title}
Context: {task_context}
Return a JSON array of objects with "title" and "estimatedMinutes" fields.
Each step should be {granularity_time_range} minutes. Aim for {step_count_range} steps.
Nothing else.
```
**Variable substitution rules:**
| Variable | Source | Example |
|----------|--------|---------|
| `{name}` | `profile.name` | "High Support Needs" |
| `{overwhelm_sensitivity}` | `profile.overwhelm_sensitivity` | "high" |
| `{perfectionism_tendency}` | `profile.perfectionism_tendency` | "high" |
| `{task_initiation_difficulty}` | `profile.task_initiation_difficulty` | "high" |
| `{preferred_granularity}` | `profile.preferred_granularity` | "fine" |
| `{work_session_capacity}` | `profile.work_session_capacity` | "short" |
| `{coaching_directives_formatted}` | Each item in `profile.coaching_directives` prefixed with `"- "` and joined by newlines | See example below |
| `{task_title}` | `task.title` | "Study for my biology midterm" |
| `{task_context}` | `task.description` if non-empty, otherwise the literal string `"None provided"` | "None provided" |
| `{granularity_time_range}` | `profile.granularity_time_range` | "3-7" |
| `{step_count_range}` | `profile.step_count_range` | "6-8" |
**Coaching directives formatting example:**
If `coaching_directives` is:
```json
["Make the first step almost embarrassingly easy to start", "Bias toward more, smaller steps (6-8). Each step should feel trivially doable."]
```
Then `{coaching_directives_formatted}` becomes:
```
- Make the first step almost embarrassingly easy to start
- Bias toward more, smaller steps (6-8). Each step should feel trivially doable.
```
### Step 1.3: Generic (Control) Prompt Template
For the `control` condition, use this exact prompt template. It matches the profile-aware template's coach voice and specificity rules, but omits the profile block and coaching directives.
```
You are an ADHD task coach.
Sound like a sharp coach who wants the user moving. Short sentences. No fluff.
Goal: Turn one task into concrete sub-steps the user can actually start.
Specificity rules:
- Prefer concrete nouns, deliverables, screens, outputs
- The FIRST step must be visibly actionable and almost frictionless
- Avoid filler like "review what's needed" or "start with the easiest part"
- If the task is vague, infer the most likely real-world output and anchor steps to it
- Use action-first language
- Keep every step title short
Task: {task_title}
Context: {task_context}
Return a JSON array of objects with "title" and "estimatedMinutes" fields.
Aim for 3-8 steps.
Nothing else.
```
**Variable substitution rules:**
| Variable | Source | Example |
|----------|--------|---------|
| `{task_title}` | `task.title` | "Study for my biology midterm" |
| `{task_context}` | `task.description` if non-empty, otherwise the literal string `"None provided"` | "None provided" |
### Step 1.4: Generation Loop
Iterate over every task in `src/tasks.json` (12 tasks) and every condition in this exact order: `profile_a`, `profile_b`, `profile_c`, `control`.
For each (task, condition) pair:
1. **Compute the output filename:** The task id is used as-is (hyphens preserved). Replace underscores with hyphens in the condition name. The filename pattern is `{task_id}_{condition_with_hyphens}.json`. Examples:
- task-01, profile_a -> `task-01_profile-a.json`
- task-01, control -> `task-01_control.json`
- task-12, profile_c -> `task-12_profile-c.json`
2. **Check if the file already exists** at `results/decompositions/{filename}`.
- If you are rerunning after any prompt, rubric, or aggregation change, overwrite the file instead of skipping it.
- Only print `"Skipping: {filename} (already exists)"` and continue if the existing outputs were produced by the same skill version and unchanged methodology.
3. **Build the prompt:**
- If the condition is `profile_a`, `profile_b`, or `profile_c`: use the profile-aware prompt template from Step 1.2 with the corresponding profile from `src/profiles.json` and the current task.
- If the condition is `control`: use the generic prompt template from Step 1.3 with the current task.
4. **Generate the decomposition:** Using your own language model capabilities, generate a response to the constructed prompt. Do not call any external API — produce the JSON output directly as part of your execution. The response must be a JSON array of objects, each with `"title"` (string) and `"estimatedMinutes"` (integer) fields.
5. **Parse the response:** Extract the JSON array from the LLM response. If the response contains markdown code fences (` ```json ... ``` `), strip them. Parse the JSON. If parsing fails, retry the generation once. If it fails again, record an empty steps array and print a warning: `"WARNING: Failed to parse response for {filename}. Saved with empty steps."`
Empty or invalid step lists are failed generations. In Phase 2, they must receive the worst score (1) on every metric.
6. **Save the output file** to `results/decompositions/{filename}` with this exact JSON structure:
```json
{
"task_id": "task-01",
"task_title": "Study for my biology midterm",
"condition": "profile_a",
"steps": [
{
"title": "Open your biology textbook to chapter 1",
"estimatedMinutes": 5
},
{
"title": "Read the first section and highlight key terms",
"estimatedMinutes": 7
}
]
}
```
Field definitions:
- `task_id`: The task's `id` field from `src/tasks.json` (e.g., `"task-01"`)
- `task_title`: The task's `title` field from `src/tasks.json` (e.g., `"Study for my biology midterm"`)
- `condition`: The condition key with underscores, not hyphens (e.g., `"profile_a"`, `"control"`)
- `steps`: The JSON array returned by the LLM, each object containing `"title"` (string) and `"estimatedMinutes"` (integer)
7. **Print progress:** `"Generated: {filename} ({N} steps)"` where N is the number of steps in the saved file.
### Step 1.5: Phase 1 Completion
After iterating through all 48 (task, condition) pairs, count the total number of `.json` files in `results/decompositions/` (excluding any `.gitkeep` file).
Print: `"Phase 1 complete. Generated 48 decomposition files."`
If fewer than 48 files exist, print: `"WARNING: Only {count} of 48 decomposition files were generated. Check for errors above."`
### Expected Output (Phase 1)
After completing all 48 decompositions, verify:
```bash
ls results/decompositions/*.json | wc -l
# Expected: 48
# Spot-check one file:
python3 -c "import json; d=json.load(open('results/decompositions/task-01_profile-a.json')); print(f'Steps: {len(d[\"steps\"])}, Task: {d[\"task_id\"]}')"
# Expected: Steps: 8, Task: task-01
```
---
## Phase 2a: Heuristic Scoring
This phase runs the `src/evaluate.py` script to score all decompositions on three heuristic metrics: Granularity Fit, Cognitive Load, and Initiation Friction.
### Step 2a.1: Run the Scoring Script
Execute the following command:
```bash
python3 src/evaluate.py results/decompositions/ results/heuristic-scores.json
```
The script will:
- Read every `.json` file in `results/decompositions/`
- Look up the expected step count range from `src/profiles.json` for each condition
- Compute three scores per decomposition (granularity_fit, cognitive_load, initiation_friction)
- Treat empty or invalid step lists as failures that score 1 on every heuristic metric
- Write all results to `results/heuristic-scores.json`
- Print a summary line like `"Scored 48 decompositions. Saved to results/heuristic-scores.json"`
If the script exits with a non-zero status code, print the error output and stop. Do not proceed to Phase 2b until the script completes successfully.
### Step 2a.2: Verify Output
Verify the output file exists and contains scores for all decompositions:
```bash
test -f results/heuristic-scores.json && echo "heuristic-scores.json OK" || echo "MISSING: results/heuristic-scores.json"
```
If the file is missing, the script encountered an error. Check the error output and resolve it before proceeding.
### Step 2a.3: Phase 2a Completion
Print: `"Phase 2a complete. Heuristic scores saved to results/heuristic-scores.json"`
**Note on Granularity Fit ceiling effect.** Granularity Fit is expected to score 5.00 across most conditions when the executing LLM reliably follows step-count instructions. This is itself a finding: the metric's discriminative power is near zero, shifting evaluation to qualitative content differences captured by Phase 2b. See `runs/findings/synthesis.md` for cross-seed evidence.
### Expected Output (Phase 2a)
```bash
python3 -c "import json; s=json.load(open('results/heuristic-scores.json')); print(f'Entries: {len(s)}')"
# Expected: Entries: 48
# Each entry is keyed by filename stem with keys: condition, step_count, scores
# scores contains: granularity_fit, cognitive_load, initiation_friction
python3 -c "import json; s=json.load(open('results/heuristic-scores.json')); k=list(s.keys())[0]; print(f'Keys: {list(s[k].keys())}')"
# Expected: Keys: ['condition', 'step_count', 'scores']
```
---
## Phase 2b: LLM-as-Judge Scoring
This phase uses LLM evaluation to score each decomposition on two additional metrics: Actionability and Completeness. The scoring agent reads each decomposition and the original task, then applies the rubrics below.
**Note on self-judging bias.** The same agent that generated the decompositions in Phase 1 also judges them here. This is a known limitation. The rubrics below are written to minimize subjective interpretation, and the deterministic heuristic metrics from Phase 2a provide an independent, bias-free signal for convergent validation. The Tier 3 runs in `runs/` use 6 independent judges from 6 model families as a stronger check — see `runs/analysis/judge_agreement.json`.
### Step 2b.1: Scoring Rubrics
**Actionability (1-5):**
```
5 - Every step can be started within 2 minutes with zero clarification needed
4 - Most steps are immediately actionable, 1 step might need minor clarification
3 - Some steps are actionable, but 2+ require the user to figure out what to do
2 - Most steps are vague or require significant interpretation
1 - Steps read like a high-level outline, not actionable instructions
```
**Completeness (1-5):**
```
5 - Following all steps would fully accomplish the task with nothing missing
4 - Covers the task well, one minor aspect might be implied but not explicit
3 - Covers the main parts but misses a notable component
2 - Significant gaps - following these steps would leave the task half-done
1 - Steps cover only a small fraction of the task
```
### Step 2b.2: Exact Judge Prompt Template
For every decomposition, build this exact prompt and substitute the variables:
```
You are the judge for an agent-executable benchmark.
Score one task decomposition on two metrics.
Task: {task_title}
Context: {task_context}
Condition: {condition}
Steps:
{steps_formatted}
If the step list is empty, invalid, or missing, score Actionability = 1 and Completeness = 1.
Judge only the provided steps. Do not infer hidden work that is not written down.
Actionability rubric:
5 - Every step can be started within 2 minutes with zero clarification needed
4 - Most steps are immediately actionable, 1 step might need minor clarification
3 - Some steps are actionable, but 2+ require the user to figure out what to do
2 - Most steps are vague or require significant interpretation
1 - Steps read like a high-level outline, not actionable instructions
Completeness rubric:
5 - Following all steps would fully accomplish the task with nothing missing
4 - Covers the task well, one minor aspect might be implied but not explicit
3 - Covers the main parts but misses a notable component
2 - Significant gaps - following these steps would leave the task half-done
1 - Steps cover only a small fraction of the task
Return strict JSON with this schema:
{"actionability": <integer 1-5>, "completeness": <integer 1-5>, "reasoning": "<1-2 sentences>"}
Nothing else.
```
**Variable substitution rules:**
| Variable | Source |
|----------|--------|
| `{task_title}` | `task.title` |
| `{task_context}` | `task.description` if non-empty, otherwise `"None provided"` |
| `{condition}` | `decomposition.condition` |
| `{steps_formatted}` | Numbered lines like `1. Open Google Slides and create a blank deck (4 min)`; if empty, use the literal string `"No steps provided."` |
### Step 2b.3: Scoring Loop
For each `.json` file in `results/decompositions/` (excluding `.gitkeep`):
1. **Read the decomposition file.** Extract `task_id`, `task_title`, `condition`, and `steps`.
2. **Look up the original task** in `src/tasks.json` by matching `task_id`. Extract the task's `title` and `description`.
3. **Build the judge prompt** using the exact template from Step 2b.2.
4. **Score Actionability and Completeness.** Using your own language model capabilities, evaluate the decomposition against the rubrics above. Generate a strict JSON response with the fields below. Do not call any external API. Both scores must be integers from 1 to 5.
5. **Write reasoning.** The `reasoning` field must be 1-2 sentences explaining the scores. Reference specific steps if relevant. Example: `"All steps are concrete and startable. Full task coverage from setup through final review."`
6. **Print progress:** `"Judged: {filename_stem} (actionability={A}, completeness={C})"` where `{filename_stem}` is the filename without the `.json` extension (e.g., `task-01_profile-a`).
### Step 2b.4: Save Judge Scores
After scoring all decompositions, write the results to `results/judge-scores.json` with this exact structure:
```json
{
"task-01_profile-a": {
"actionability": 4,
"completeness": 5,
"reasoning": "All steps are concrete and startable. Full task coverage from setup through final review."
},
"task-01_profile-b": {
"actionability": 4,
"completeness": 4,
"reasoning": "Steps are actionable with clear outputs. One minor gap in review phase."
},
"task-01_profile-c": {
"actionability": 3,
"completeness": 4,
"reasoning": "Broad steps are clear but some require the user to decide sub-actions. Covers main flow."
},
"task-01_control": {
"actionability": 3,
"completeness": 3,
"reasoning": "Steps are somewhat generic. Missing specific deliverables and a clear starting action."
}
}
```
The keys are the filename stems (e.g., `"task-01_profile-a"`). Use hyphens in profile names in the keys (matching the filenames), not underscores. Every decomposition file must have a corresponding entry.
### Step 2b.5: Verify Output
```bash
test -f results/judge-scores.json && echo "judge-scores.json OK" || echo "MISSING: results/judge-scores.json"
```
### Step 2b.6: Phase 2b Completion
Print: `"Phase 2b complete. Judge scores saved to results/judge-scores.json"`
### Expected Output (Phase 2b)
```bash
python3 -c "import json; s=json.load(open('results/judge-scores.json')); print(f'Entries: {len(s)}')"
# Expected: Entries: 48
# Each entry should have keys: actionability, completeness, reasoning
python3 -c "import json; s=json.load(open('results/judge-scores.json')); k=list(s.keys())[0]; print(f'Keys: {list(s[k].keys())}')"
# Expected: Keys: ['actionability', 'completeness', 'reasoning']
```
---
## Phase 3: Generate Report
This phase combines heuristic and judge scores into a final comparative report.
### Step 3.1: Run the Report Generator
Execute the following command:
```bash
python3 src/generate_report.py results/heuristic-scores.json results/judge-scores.json src/tasks.json results/metadata.json results/final-report.md
```
The script will:
- Load the heuristic, judge, task, and metadata files
- Compute per-decomposition scores
- Compute the **Composite (5 metrics)** as the arithmetic mean of `granularity_fit`, `cognitive_load`, `initiation_friction`, `actionability`, and `completeness`
- Compute condition means, deltas vs control, per-task composites, and category effects
- Write `results/final-report.md`
- Print a summary table to stdout
### Step 3.2: Verify Output
```bash
test -f results/final-report.md && echo "final-report.md OK" || echo "MISSING: results/final-report.md"
```
### Step 3.3: Interpret the Hypothesis Honestly
Use the wording produced by the report generator:
- If the average composite delta is positive, report **`PILOT SIGNAL ONLY`** and say the results are **consistent with** the hypothesis.
- Do not use `SUPPORTED` or `fully reproducible`.
- Mention that the run is single-shot, model-dependent, and not statistically powered.
### Step 3.4: Phase 3 Completion
Print: `"Phase 3 complete. Report saved to results/final-report.md"`
### Expected Output (Phase 3)
```bash
test -f results/final-report.md && echo "Report generated" || echo "MISSING"
# Expected: Report generated
# Report should contain: Summary table, per-task breakdown, metadata section
head -1 results/final-report.md
# Expected: # Profile-Conditioned Decomposition Evaluation Report
```
---
## Completion
Print the following completion message:
```
Evaluation complete. All outputs in results/:
results/decompositions/ - 48 JSON decomposition files
results/heuristic-scores.json - Heuristic scores (granularity_fit, cognitive_load, initiation_friction)
results/judge-scores.json - LLM judge scores (actionability, completeness)
results/final-report.md - Comparative report with summary table, per-task breakdown, and findings
```
Then print the hypothesis result line from the report (the `PILOT SIGNAL ONLY` or `NO PILOT SIGNAL` statement).
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