strength-readiness

name	strength-readiness
description	Produce a bounded StrengthProposal for today by consuming the runtime-computed `classified_state` + `policy_result` and applying judgment-only steps — action matrix, rationale prose, cross-checks. The runtime already did every band, every score, every R-rule; this skill does not re-derive them.
allowed-tools	Read, Bash(hai state snapshot *), Bash(hai state read *), Bash(hai propose *), Bash(hai intent list *), Bash(hai target list *)
disable-model-invocation	false

Strength Readiness

All arithmetic happens in code. Your job is: read the bundle, honour the policy result, pick an action, write the rationale, persist.

Load the bundle

hai state snapshot --as-of <today> --user-id <u> --evidence-json <hai clean output>

Under snapshot.strength you receive these blocks:

• today — today's accepted_resistance_training_state_daily row (session_count, total_sets, total_reps, total_volume_kg_reps, exercises, volume_by_muscle_group_json, estimated_1rm_json, unmatched_exercise_tokens_json), or null.
• history — trailing rows for 28-day context.
• signals — runtime-derived dict the classifier consumed: volume_ratio_7d_vs_28d_week_mean, sessions_last_7d, sessions_last_28d, days_since_heavy_by_group, today_volume_by_muscle_group, estimated_1rm_today, unmatched_exercise_tokens, goal_domain. Context only; never re-derive.
• classified_state — recent_volume_band, freshness_band_by_group, coverage_band, strength_status, strength_score, volume_ratio, sessions_last_7d, sessions_last_28d, unmatched_exercise_tokens, uncertainty. Source of truth.
• policy_result — policy_decisions[], forced_action, forced_action_detail, capped_confidence, and (v0.2.0 W-PROV-2) optional evidence_locators[]. The runtime always emits a row-level locator citing today's accepted_resistance_training_state_daily; an R-volume-spike firing additionally cites total_volume_kg_reps. Source of truth.
• missingness — per state_model_v1.md §5.

Protocol

1. If the policy forced an action, use it

If policy_result.forced_action is set, action is that value and action_detail is policy_result.forced_action_detail. Confidence: low for defer_decision_insufficient_signal, else moderate. Skip the action matrix; jump to rationale.

Specifically:

• defer_decision_insufficient_signal → R-coverage fired; record the decision verbatim.
• escalate_for_user_review → R-volume-spike fired; record the volume_ratio + threshold_ratio in the action_detail so the user can inspect.

2. Otherwise, pick from the action matrix

Keyed on classified_state.strength_status:

status	action + action_detail
progressing	proceed_with_planned_session
maintaining	proceed_with_planned_session with {"caveat": "steady_state"} when every freshness band for the session's target groups is fresh
maintaining (fatigued group)	downgrade_to_technique_or_accessory with {"reason_token": "fatigued_group:<group>"} when the session targets a group whose freshness band is fatigued
undertrained	proceed_with_planned_session with {"caveat": "undertrained_resume_gradually"}
overreaching	this path is unreachable — R-volume-spike already forced escalate_for_user_review in step 1

3. Confidence

Default from classified_state.coverage_band: full → high, partial → moderate, sparse → moderate, insufficient → low. If policy_result.capped_confidence is set, it lowers the default but never raises it.

4. Cross-checks (no arithmetic)

• If classified_state.unmatched_exercise_tokens is non-empty, surface it in the rationale — the agent should invite the user to extend the taxonomy via hai intake exercise so those sets participate in classification next time.
• If classified_state.uncertainty contains goal_domain_is_resistance_training, frame the rationale around progression: current 1RM estimates, volume trend, per-group freshness.

5. Rationale (5–8 lines)

One line per band or signal that informed the decision. Name the band; do not re-derive it.

Examples: recent_volume_band=<band>, strength_status=<status>, freshness_band[quads]=<band>, unmatched_exercise_tokens_present, volume_spike_detected (if R-volume-spike fired), goal_domain_is_resistance_training (if flagged).

6. Uncertainty

Start with classified_state.uncertainty (already sorted + deduped). Append any tokens you added (e.g. *_unavailable_at_source derived from the snapshot's missingness token). Re-sort alphabetically; deduplicate.

7. Follow-up

Strength emits a StrengthProposal, not a recommendation, so it has no follow_up field. Synthesis assigns review semantics per finalised plan. Skip this step.

On defer_decision_insufficient_signal, synthesis uses the strength-domain template "Did you train yesterday? Anything worth logging?" (owned by core.narration.templates.DEFER_REVIEW_QUESTION_TEMPLATES).

Output

Emit a StrengthProposal JSON and call hai propose --domain strength --proposal-json <path>. The propose tool validates the shape and appends to proposal_log; it is your determinism check.

proposal_id = prop_<for_date>_<user_id>_strength_01 (idempotent on (for_date, user_id, domain); re-running on the same day does not produce a new row).

Copy policy_result.policy_decisions into the output's policy_decisions verbatim — the runtime decided them; you do not re-edit or add new ones. v0.2.0 W-PROV-2: if policy_result.evidence_locators is present, copy that list verbatim into the proposal's evidence_locators field — do NOT derive locators yourself; the runtime computed them. If the field is absent, omit evidence_locators from the proposal.

Invariants

• You never compute a band, a score, a ratio, or a 1RM. classified_state is the source of truth.
• You never evaluate an R-rule (require_min_coverage, no_high_confidence_on_sparse_signal, volume_spike_escalation, unmatched_exercise_confidence_cap). policy_result is the source of truth; you honour forced_action and capped_confidence.
• You never apply X-rule mutations. Synthesis owns all cross-domain reasoning (X3 ACWR caps strength; X4 yesterday's heavy lower body caps running; X5 yesterday's long run caps lower-body strength). This skill emits one domain's bounded proposal; synthesis mutates the draft mechanically, based on x_rule_firing rows, before the skill ever sees it as "final."
• You never emit an action outside the v1 enum (proceed_with_planned_session, downgrade_to_technique_or_accessory, downgrade_to_moderate_load, rest_day_recommended, defer_decision_insufficient_signal, escalate_for_user_review). hai propose enforces this.
• You never fabricate values for missing evidence; missing stays missing.
• If a decision isn't reasoned in rationale[] or policy_decisions[], it didn't happen.