--- name: grants description: "NIH grant research skill for clinical researchers. Grill-me intake (research idea + career stage + preliminary data + environment + submission posture + known institute targets) locks down the funding strategy before any search runs. Runs a 5-facet Consensus positioning analysis (with draft Significance/Innovation language), maps the research to the right NIH institutes and study sections via RePORTER, finds NOSIs and funded overlap, and produces an editable Word document (.docx) with budget/scope-aware mechanism recommendations, submission timelines, and a mandatory program officer recommendation. Triggers: 'grants for [topic]', 'find grants for my research idea', 'what grants match my research', 'help me find NIH funding', 'grant opportunities for my research', or any grant-related request. NIH-only scope — non-NIH funders (PCORI, DOD CDMRP, VA, foundations) are out of scope and flagged at intake." license: MIT metadata: source_spec: "megaprompts/08-grants-megaprompt.md" build_pattern: "Path B (direct conversion)" research_pack_convention: "Agent Integrity Rules verbatim per PR #657 audit" version: 1.0.0 --- # Grants — NIH Funding Intelligence > **Portability:** Requires `bash_tool` (for RePORTER POST via curl), Node.js with `docx` package, and a Consensus MCP connection. Works in Claude Code CLI natively. In Claude.ai with Code Execution + Consensus MCP, the workflow is supported but slower. > **Scope: NIH-only.** Non-NIH funders (PCORI, DOD CDMRP, VA, foundations) are out of scope and flagged at intake. For a clinical researcher with a research idea, produce a strategic NIH funding overview as an editable `.docx`. Output covers research positioning analysis, institute mapping, targeted grant discovery, and strategic recommendations the researcher can edit, copy from, and share with their mentor. ## Agent Integrity Rules (Research-Pack Convention) Inherited; locked verbatim per PR #657 audit. - **Execution discipline.** A step isn't complete until result is confirmed received. Consensus calls **sequential with 1+ sec pause**. RePORTER calls sequential. - **Data sourcing.** Count only what tool calls returned this session. Never supplement with training knowledge. Training knowledge labeled `[Not from Consensus/RePORTER — reference information]` and excluded from counts. - **Counts & attribution.** Queries sent / results shown / results cited — three separate numbers, never conflate. Every cited paper has retrievable URL from this session. - **Error handling.** On failure → wait 3s → retry once → log. After 3 consecutive failures across tools: stop, alert researcher, explain what's missing. Never silently skip. - **Transparency.** Audit Log section in the DOCX. Same standards in chat summary as in document. See [`references/reporter_post_patterns.md`](references/reporter_post_patterns.md) for the RePORTER POST canon + plan-tier detection. ## Phase 1: Grill-Me Intake (6 forcing questions, one at a time) ### Q1 (root) — Research idea > **Describe the research idea in 2–3 sentences. What's the question, what's new, and what's the clinical relevance? Vague answers ("AI for healthcare", "biomarkers for disease X") will be rejected — push for specificity.** > > *Why I'm asking:* Five Consensus searches (established / stakes / current approaches / adjacent methods / gaps) depend on a precise research idea. Vague ideas produce vague gap quotes and useless positioning narrative. Refuse mush. Re-ask once with examples if user is too broad. ### Q2 (depends on Q1) — Career stage > **Career stage — pick one:** > > 1. Pre-doctoral (PhD student, T32 trainee) > 2. Postdoctoral fellow (F32, K99 candidate) > 3. Early career (K-award candidate, first R01) > 4. Independent investigator (multiple R01s, established lab) > 5. Senior PI (R35, P-series, U01 leadership) > > *Why I'm asking:* Career stage filters mechanism recommendations. F-series for trainees, K-series for early career, R-series for independent. Picking the wrong stage produces unfundable mechanism suggestions. Forcing choice. ### Q3 (depends on Q2) — Preliminary data status > **Preliminary data — pick one:** > > 1. None (de novo project, no pilot data yet) > 2. Pilot data (early findings, single-site) > 3. Strong preliminary (multi-experiment, ready for R01-scale) > 4. Validated and ready (multi-site, publication-ready) > > *Why I'm asking:* Prelim data status drives mechanism budget. No data → R03 / R21 pilot scope. Strong prelim → R01 / U01 multi-site scale. Mismatch produces uncompetitive applications. ### Q4 (depends on Q2) — Environment > **Research environment — pick one:** > > 1. R01-eligible (research-intensive institution with NIH base funding) > 2. Mid-tier (regional academic medical center, modest NIH portfolio) > 3. Resource-constrained (smaller institution, minimal NIH base) > 4. Industry-collaborative (academic + industry partnership) > > *Why I'm asking:* Environment affects scope realism (multi-site U01 requires R01-eligible) and which mechanism categories are competitive (R15 specifically targets resource-constrained). ### Q5 (depends on Q1) — Submission posture > **Submission posture — pick one:** > > 1. New application (first submission, no prior reviews) > 2. Resubmission (A1 with reviewer responses needed) > 3. Exploring (haven't decided yet whether to submit) > > *Why I'm asking:* Resubmissions need reviewer-response guidance in the DOCX (Section 7). New applications skip that. Exploring shifts emphasis to landscape over strategy. ### Q6 (depends on Q1) — Known institute targets > **Are you already considering specific NIH institutes? List names (NCI / NHLBI / NIMH / NINDS / NIDDK / etc.) or say "no preference — find the right ones".** > > *Why I'm asking:* If you have an institute hypothesis, I'll validate it against RePORTER data. If not, I'll surface the top-3 institutes funding adjacent work from the institute-tally. Accept "no preference" as the common case. **Stop condition:** After Q6, commit and start Phase 2A. Never re-open intake after Phase 2A begins. ## Phase 2A: Research Positioning (5 Consensus searches) Run sequentially at 1 q/sec. Each search corresponds to one positioning facet: 1. **Established** — `"" established evidence` — what's known 2. **Stakes** — `"" mortality OR burden OR cost OR prevalence` — why it matters 3. **Current Approaches** — `"" current treatment OR standard of care OR approach` — state of the art 4. **Adjacent Methods** — `"" applied to ` — methodological possibilities 5. **Gaps** — `"" limitations OR unanswered OR future directions OR challenge` — gap signals Use `scripts/citation_tracker.py --action record_consensus_search` for each. Plan-tier detected from first response. **Synthesis:** for each facet, extract 2-3 quotable findings (becomes Section 2 gap quotes). Draft Significance/Innovation language using "the field has established X (refs), but Y remains unanswered (refs)" pattern. ## Phase 2B: Institute Mapping + Grant Discovery (RePORTER POST) RePORTER is **POST-only**. Use `bash_tool` + `curl` — never `web_fetch`. ### Dynamic fiscal year window Compute at runtime via `scripts/fiscal_year_calculator.py`. Default: current FY + 3 prior. Federal FY starts Oct 1, so: ```bash python ../scripts/fiscal_year_calculator.py --output json # Returns: {"current_fy": 2026, "window": [2023, 2024, 2025, 2026]} ``` ### Narrow (AND) search — finds direct overlap ```bash curl -X POST 'https://api.reporter.nih.gov/v2/projects/search' \ -H 'Content-Type: application/json' \ -d '{ "criteria": { "fiscal_years": [2023, 2024, 2025, 2026], "include_active_projects": true, "advanced_text_search": { "operator": "AND", "search_field": "all", "search_text": " " } }, "limit": 50, "include_fields": ["project_num", "project_title", "agency_ic_admin", "study_section", "fiscal_year", "principal_investigators", "abstract_text"] }' ``` ### Broad (OR) search — finds adjacent work ```bash curl -X POST 'https://api.reporter.nih.gov/v2/projects/search' \ -H 'Content-Type: application/json' \ -d '{ "criteria": { "fiscal_years": [2023, 2024, 2025, 2026], "advanced_text_search": { "operator": "OR", "search_field": "all", "search_text": " " } }, "limit": 50 }' ``` ### Institute tally + study section ranking After RePORTER responses: - Tally `agency_ic_admin` (institute code: NCI, NHLBI, NIMH, etc.) → top-3 funding institutes - Tally `study_section` → top-2 study sections (where applications go for review) ### NOSI discovery Parse RePORTER responses for `NOT-*` opportunity numbers. For each: ```bash # NOSIs live at predictable URLs: # https://grants.nih.gov/grants/guide/notice-files/NOT---.html web_fetch ``` If fetch fails: log `[NOSI {number} — fetch failed, not included]`, continue. ## Mechanism Matching (Scope-Aware) NOT career stage alone. Career stage **+** project scope **+** prelim data drive recommendation. Use `scripts/mechanism_matcher.py`: ```bash python ../scripts/mechanism_matcher.py \ --career-stage "early_career" \ --prelim-data "pilot" \ --environment "r01_eligible" \ --scope "single_site" \ --output json # Returns mechanism shortlist with rationale ``` See [`references/nih_mechanism_matching.md`](references/nih_mechanism_matching.md) for the full matrix. ## Phase 3: DOCX Generation 9 sections via Node.js + `docx` library. See [`references/docx_9_sections.md`](references/docx_9_sections.md) for full spec. 1. **Executive Summary** — title + career stage + environment + 3-4 key findings bullets 2. **Research Positioning** — 3-5 gap quotes (italicized, inline Consensus citations) + 2-3 paragraph positioning narrative + supporting evidence table 3. **Target Institutes** — ranking table (institute, project count in window, % match to your idea) + 2-3 sentence interpretation 4. **Grant Opportunities** — bold NOSI callout if any. Top-3 grants table with hyperlinked FOAs + per-grant scope/budget fit paragraph 5. **Funded Overlap** — top-5 projects table (PI, project_num, IC, year, hyperlinked to RePORTER) + differentiation paragraph 6. **Study Sections** — ranking table + best-match interpretation 7. **Strategic Recommendations & Next Steps** — 3-4 numbered recs + **mandatory program officer rec** + submission timeline note + (if resubmission Q5=2) reviewer-response guidance + closing paragraph 8. **References** — numbered bibliography, hyperlinked to Consensus 9. **Audit Log** — Consensus searches table, plan-tier note, RePORTER searches table, NOSI fetches table, summary stats, tool constraints note, failed steps ### Styling Arial 12pt body, navy headings (#1a3a5c), light blue table headers (#e8f0f8), amber NOSI callout. `ExternalHyperlink` patterns: - Paper citations: `https://consensus.app/papers/...` - FOA links: `https://grants.nih.gov/grants/guide/...` - RePORTER projects: `https://reporter.nih.gov/project-details/` ## Mandatory Program Officer Recommendation Always include in Section 7: > **Recommended next step: contact program officer at {top institute}.** Find their staff page at https://www.nih.gov/institutes-nih/list-nih-institutes-centers-offices → {institute} → Program Officers. Prepare: 1-page specific aims + your CV + 3 specific questions about fit. Email subject: "Pre-application inquiry: ". This is the single most valuable advice for any applicant. Never skip. ## Submission Timeline (Embedded in DOCX Section 7) | Mechanism | Standard receipt dates | |---|---| | R01, R21, R03 | Feb 5, Jun 5, Oct 5 | | K awards (K01, K08, K23, K99) | Feb 12, Jun 12, Oct 12 | | R34, R61/R33 | Feb 16, Jun 16, Oct 16 | | F31, F32 | Apr 8, Aug 8, Dec 8 | ## Phase 4: Deliver - Save DOCX to `/grants__.docx` - Chat summary: file path + audit counts + plan tier + verdict on institute targets - Validate: `python scripts/office/validate.py ` ## Tooling | Script | Role | |---|---| | `scripts/citation_tracker.py` | Three-count audit (Consensus sent/shown/cited + RePORTER projects/cited) at `~/.grants_sessions/.json` | | `scripts/fiscal_year_calculator.py` | Current FY + 3-prior window. Computed at runtime, never hardcoded. | | `scripts/mechanism_matcher.py` | Career stage × scope × prelim → mechanism recommendation shortlist | ## References - [`references/nih_mechanism_matching.md`](references/nih_mechanism_matching.md) — career stage × scope × prelim → mechanism canon (7+ sources) - [`references/reporter_post_patterns.md`](references/reporter_post_patterns.md) — RePORTER curl POST templates + plan-tier detection (7+ sources) - [`references/docx_9_sections.md`](references/docx_9_sections.md) — 9-section .docx spec + technical requirements (7+ sources) ## Error Handling | Failure | Behavior | |---|---| | Consensus rate-limit hit | Wait 3s, retry once, log; if still failing, alert researcher | | Consensus returns 0 for a facet | Surface explicitly; never fill with training knowledge | | Consensus plan-tier cap detected | Log tier, note in audit, surface to researcher | | RePORTER POST returns error | Retry once after 3s; if still failing, log and continue | | RePORTER returns <5 on narrow | Document; broad OR should compensate; surface low count | | NOSI fetch fails | Log `[NOSI {n} — fetch failed]`, continue | | 3 consecutive tool failures | Stop, alert researcher with what's missing | | DOCX generation fails | Save raw data as JSON fallback so researcher doesn't lose work | ## Anti-Patterns To Reject - Parallelizing Consensus calls (will hit rate limit) - Using `web_fetch` for RePORTER (POST-only — `web_fetch` is GET) - Hardcoded fiscal year values - Mechanism recommendations based on career stage alone (must consider scope too) - Silently filling thin facet results with training knowledge - Skipping the audit log - Skipping the program officer recommendation - Conflating "papers found" with "papers shown" with "papers cited" - Fabricating NOSI details when fetch fails --- **Version:** 1.0.0 **Source spec:** [`megaprompts/08-grants-megaprompt.md`](../../../../megaprompts/08-grants-megaprompt.md) **Build pattern:** Path B (direct conversion). Research-pack sibling of pulse + litreview.