hyperresearch-8-corpus-critic

name

hyperresearch-8-corpus-critic

description

Step 8 of the hyperresearch V8 pipeline. Spawns one corpus-critic subagent to identify "what source, if found, would overturn the current direction?" gaps, then runs a targeted fetch wave to fill them. Highest-leverage intervention point: corrections applied before drafting cost nothing; corrections applied after drafting require patches. Invoked via Skill tool from the entry skill (full tier only).

Step 8 — Pre-draft corpus critic (targeted gap-fill)

Tier gate: full tier ONLY. Skip for light.

Goal: before drafting, ask "what source, if found, would overturn the current direction?" and run a targeted fetch wave to fill the most dangerous gaps. This is the highest-leverage intervention point in the pipeline — corrections applied before drafting cost nothing; corrections applied after drafting require patches and risk structural drift.

---

Recover state

Read these inputs:

• research/scaffold.md — vault_tag
• research/comparisons.md — cross-locus tensions
• research/loci.json — scored loci
• research/temp/source-tensions.json — expert disagreements
• research/prompt-decomposition.json — specifically the time_periods array

---

Pre-flight: period-pinned primary-source coverage check

Run this BEFORE spawning the corpus-critic subagent. If prompt-decomposition.json -> time_periods is non-empty, walk every entry and verify the vault contains a primary source filed for that exact period — not "most recent", not narrative commentary, not earnings-call transcripts standing in for tabular filings.

For each time_period entry:

1. Search the vault for a primary source matching the primary_source description and the issuer:

   PYTHONIOENCODING=utf-8 {hpr_path} search "<period> <issuer>" --tag <vault_tag> --include-body -j
   

2. Open the candidate notes (note show <id> -j) and verify the document's actual reporting period — the filing must cover the SPECIFIC period named in the prompt, not an adjacent one. A Q1 2025 10-Q does NOT satisfy "Q3 2024" — different period, different tabular data.
3. If the period-pinned filing is missing, add it to research/corpus-critic-gaps.json as a priority: critical gap of type period-pinned-primary BEFORE spawning the corpus-critic subagent. Schema:

   {{
     "type": "period-pinned-primary",
     "target_position": "<period> exact figures for <issuer>",
     "search_queries": [
       "site:sec.gov 10-Q \"period ended September 30, 2024\" <issuer>",
       "<issuer> Q3 2024 10-Q filing PDF"
     ],
     "source_type": "primary-filing",
     "priority": "critical",
     "rationale": "Prompt names <period>; vault has no filing covering that exact period. Tabular line items only exist in the period-pinned filing. Without it, the draft will paraphrase rounded numbers from earnings calls and miss the rubric's exact figures."
   }}
   

The targeted fetch wave in the next step will pull these filings BEFORE the corpus-critic finishes its broader gap analysis. This ordering matters: numerical-precision misses are the largest single category of avoidable factual-accuracy failures.

---

Procedure

1. Spawn ONE hyperresearch-corpus-critic subagent (Sonnet).

   Spawn template:

   subagent_type: hyperresearch-corpus-critic
   prompt: |
     RESEARCH QUERY (verbatim, gospel):
     > {{paste research/query-<vault_tag>.md body}}
   
     QUERY FILE: research/query-<vault_tag>.md
   
     PIPELINE POSITION: You are step 8 of the hyperresearch V8 pipeline.
     Step 6 produced research/comparisons.md. After you return, the
     orchestrator runs a targeted fetch wave, then step 10 drafts.
   
     YOUR INPUTS:
     - corpus_tag: <vault_tag>
     - comparisons_path: research/comparisons.md
     - loci_path: research/loci.json
     - output_path: research/corpus-critic-gaps.json
   

2. Read the gaps output (research/corpus-critic-gaps.json). Each gap has a priority (critical / high) and a type (overturning / strengthening / independent-verification).

3. Targeted fetch wave. Spawn 2–4 fetcher subagents (Sonnet) to search for and fetch the sources identified in the gaps.

   Spawn template:

   subagent_type: hyperresearch-fetcher
   prompt: |
     RESEARCH QUERY (verbatim, gospel):
     > {{paste research/query-<vault_tag>.md body}}
   
     QUERY FILE: research/query-<vault_tag>.md
   
     PIPELINE POSITION: You are a step 8 fetcher (corpus-critic gap-fill)
     of the hyperresearch V8 pipeline. The corpus critic identified specific
     gaps; you fetch sources targeting those gaps. After you return, the
     orchestrator updates comparisons.md based on what you found.
   
     YOUR INPUTS:
     - vault_tag: <vault_tag>
     - search_queries: [<gap.search_queries>]
     - source_type: <gap.source_type>
     - gap_id: <gap.id>
   

4. Assess results.

   • Overturning source found: re-read the relevant committed position from the interim note. If the new source genuinely undercuts it, update research/comparisons.md to note the weakened position — the draft will handle it with appropriate calibration. Do NOT re-run the full depth investigation; adjust the position's confidence level.
   • Overturning source NOT found: the committed position gains confidence. Note this in comparisons.md — "adversarial search for counter-evidence to [position] returned no substantive challenges."
   • Strengthening/verification source found: note the additional support in comparisons.md. The draft can assert more confidently.

5. Log results to research/temp/corpus-critic-results.md:

   • Each gap: what was searched, what was found (or not), how it affects the committed positions
   • Updated confidence levels for any positions that changed

---

Exit criterion

• research/corpus-critic-gaps.json exists
• All critical gaps attempted (fetched or documented as unfindable)
• research/temp/corpus-critic-results.md exists
• research/comparisons.md updated with confidence/strengthening/overturning notes

---

Next step

Return to the entry skill (hyperresearch). Invoke step 9:

Skill(skill: "hyperresearch-9-evidence-digest")