[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M755HH76JPQ0AX0ERHTMPZGA":3,"skill-same:01M755HH76JPQ0AX0ERHTMPZGA":97},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"publishedAt":18,"linkOnly":19,"markdownContent":20,"markdownBody":21,"frontMatter":22,"refName":43,"commitSha":44,"skillDirectory":45,"forksCount":13,"repositoryHtmlUrl":46,"repositoryHomepageUrl":6,"ownerHtmlUrl":47,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":48,"install":49,"resources":62},"01M755HH76JPQ0AX0ERHTMPZGA","rfp-responder",null,"Use when an RFP, RFI, RFQ, security questionnaire, vendor questionnaire, or proposal request arrives and the team needs a structured response — parsing multi-section buyer-dictated requirements (MANDATORY vs WEIGHTED vs NICE-TO-HAVE), building a Shipley-method proof-point matrix mapping each requirement to a verifiable proof point, articulating 3-5 win-themes that ladder up across requirements, and producing a Shipley-derived winrate estimate that informs a bid \u002F no-bid \u002F partner-bid recommendation. For Bid Managers, Proposal Leads, Directors of Sales, and Sales Engineers at the response-strategy moment. Surfaces GAP requirements explicitly — never invents claims. NOT free-form proposal narrative authoring, NOT contract redline, NOT marketing collateral.","msm47\u002FGitskil","msm47","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F288053465?v=4","01M754J0R2V3Q31DD8BV9CCDEN","01M754J0S8Q6Y7P0S3AYCY0YQ5",0,"MIT","https:\u002F\u002Fgithub.com\u002Fmsm47\u002FGitskil\u002Fblob\u002Fmain\u002Fcommercial\u002Fskills\u002Frfp-responder\u002FSKILL.md",[],[],"2026-11-12 13:55:43",false,"---\nname: rfp-responder\ndescription: \"Use when an RFP, RFI, RFQ, security questionnaire, vendor questionnaire, or proposal request arrives and the team needs a structured response — parsing multi-section buyer-dictated requirements (MANDATORY vs WEIGHTED vs NICE-TO-HAVE), building a Shipley-method proof-point matrix mapping each requirement to a verifiable proof point, articulating 3-5 win-themes that ladder up across requirements, and producing a Shipley-derived winrate estimate that informs a bid \u002F no-bid \u002F partner-bid recommendation. For Bid Managers, Proposal Leads, Directors of Sales, and Sales Engineers at the response-strategy moment. Surfaces GAP requirements explicitly — never invents claims. NOT free-form proposal narrative authoring, NOT contract redline, NOT marketing collateral.\"\ncontext: fork\nversion: 2.8.0\nauthor: claude-code-skills\nlicense: MIT\ntags: [commercial, rfp, rfi, rfq, shipley, win-theme, proof-points, structured-response, bid-management]\ncompatible_tools: [claude-code, codex-cli, cursor, antigravity, opencode, gemini-cli]\n---\n\n# rfp-responder\n\n## Purpose\n\nHelp Bid Managers, Proposal Leads, and Directors of Sales answer five questions at the response-strategy moment:\n\n1. **What is this RFP actually asking?** (parse sections, tag every requirement MANDATORY \u002F WEIGHTED \u002F NICE-TO-HAVE, extract scoring criteria, surface deadlines and format constraints)\n2. **What is our true fit?** (proof-point matrix per requirement: STRONG \u002F PARTIAL \u002F GAP, each backed by a verifiable source — case study, certification, customer quote, technical attestation, benchmark)\n3. **What is our win-theme strategy?** (Shipley method: 3-5 themes that ladder up across requirements, not generic value-prop bullets)\n4. **What is our realistic winrate?** (Shipley-derived factor model: fit, incumbent, relationship strength, decision-criteria alignment, late-entry, competitor count, deal size — produces estimate + confidence band)\n5. **Should we bid?** (deterministic verdict: BID \u002F PARTNER-BID \u002F NO-BID with named factors driving the call)\n\nThe skill surfaces GAPs explicitly. Leadership decides whether to close them, partner around them, or no-bid. **It never invents claims.**\n\n## When to use\n\n- A 30+ page RFP \u002F RFI \u002F RFQ has landed with a 7-14 day response deadline\n- A security questionnaire (SIG, CAIQ, custom-buyer) needs structured Q&A — not prose\n- The team is preparing a bid \u002F no-bid review and needs a defensible winrate estimate\n- Sales Engineering has a proof-point library but no system to map proofs to requirements\n- Leadership wants to see fit % (STRONG \u002F PARTIAL \u002F GAP) before committing pursuit budget\n- A late-entry opportunity needs honest assessment of the relationship deficit\n\n**Do not use for:**\n- Free-form proposal narrative authoring → `business-growth\u002Fcontract-and-proposal-writer`\n- Contract redline AFTER award → `c-level-advisor\u002Fgeneral-counsel-advisor`\n- Marketing collateral \u002F category content → `marketing-skill\u002F*`\n- Discount approval on the awarded deal → `commercial\u002Fdeal-desk`\n- Pricing-model design for a new product → `commercial\u002Fpricing-strategist`\n\n## Workflow\n\n### Step 1 — Parse the RFP\n\nDrop the RFP markdown \u002F text into `scripts\u002Frfp_parser.py`. Output: structured JSON listing every requirement, tagged MANDATORY \u002F WEIGHTED \u002F NICE-TO-HAVE based on cue words (must \u002F shall = MANDATORY; should \u002F weighted scoring numbers = WEIGHTED; may \u002F preferred \u002F desired = NICE-TO-HAVE). Captures section structure, scoring criteria if disclosed, deadline, submission format constraints.\n\n```bash\npython scripts\u002Frfp_parser.py --input rfp.md --output json > parsed.json\n```\n\n### Step 2 — Score fit per requirement\n\nFill `assets\u002Frfp_intake_template.md` with your proof-point library (each proof tagged with type + verifiable source + which requirement-tags it covers) and proposed win-themes. Feed parsed RFP + intake into `scripts\u002Fresponse_drafter.py`. Output: proof-point matrix per requirement with STRONG \u002F PARTIAL \u002F GAP, win-theme injection, GAP audit.\n\n```bash\npython scripts\u002Fresponse_drafter.py --input draft_input.json --output markdown > matrix.md\n```\n\n**Hard rule:** GAP requirements are surfaced, never invented around. Leadership reads the GAP audit and decides: close the gap, partner-bid, or no-bid.\n\n### Step 3 — Apply win-theme strategy\n\nShipley method: 3-5 themes that span requirements. Each theme answers \"why us over the incumbent \u002F competitor on the criteria the buyer named.\" `response_drafter.py` shows which themes thread through which requirements — a theme appearing in \u003C2 requirements is decorative, not strategic, and gets flagged.\n\n### Step 4 — Estimate winrate\n\nFeed deal context (fit %, incumbent strength, relationship, decision-criteria alignment, late-entry, competitor count, deal size vs. average) into `scripts\u002Fwinrate_predictor.py`. Output: Shipley-derived estimate 0-100% + confidence band + factor breakdown + BID \u002F PARTNER-BID \u002F NO-BID verdict.\n\n```bash\npython scripts\u002Fwinrate_predictor.py --input deal_context.json --profile enterprise-software --output markdown\n```\n\n**No-bid threshold:** estimate \u003C 20% triggers automatic no-bid recommendation.\n\n### Step 5 — Decide\n\nTake parsed RFP + proof-point matrix + GAP audit + winrate estimate into the go \u002F no-go review. Skill does not commit pursuit budget — leadership does.\n\n## Scripts\n\n- `scripts\u002Frfp_parser.py` — section + requirement extractor (regex + cue-word heuristics, stdlib only)\n- `scripts\u002Fresponse_drafter.py` — proof-point matrix + win-theme injection + GAP audit\n- `scripts\u002Fwinrate_predictor.py` — Shipley-derived factor model + bid\u002Fno-bid verdict, industry-profile-tuned\n\nAll scripts: stdlib only (argparse, json, sys, pathlib, re, collections, statistics). `--help` and `--sample` work on all three.\n\n## References\n\n- `references\u002Fshipley_method_canon.md` — Shipley Proposal Guide v6, Shipley Capture Guide, APMP BoK, Tom Sant, Tom Searcy + Henry DeVries, Strategic Proposals research, Larry Newman\n- `references\u002Frfp_strategy_canon.md` — FAR, GSA, Forrester, Gartner, Bain, McKinsey, B2B International on RFP win-rates and buyer behavior\n- `references\u002Frfp_anti_patterns.md` — Shipley failure modes, APMP cases, Strategic Proposals research, federal loss reviews, MIT Sloan, Bain commercial-discipline, Gartner\n\n## Assumptions\n\n- **The RFP is the ground truth.** If the buyer asked it, answer it — in the order they asked, in the format they specified. Re-organizing for narrative flow is for proposals, not RFPs.\n- **Proof points must be verifiable.** A claim is only as strong as the case study, certification, customer reference, or technical attestation backing it. Unsourced claims become GAPs.\n- **Win-themes are buyer-side, not seller-side.** \"We're the leader in X\" is a marketing claim; \"Your operations team reduces incident MTTR by 60% with the same headcount\" is a win-theme. Shipley canon, not optional.\n- **Winrate estimates are directional.** The model is a discipline tool to force honest pursuit-qualification — not an oracle. Confidence band always wider than the point estimate suggests.\n- **Industry profiles tune base rates** — government RFPs reward compliance discipline; enterprise SaaS rewards reference accounts; healthcare rewards regulatory + security depth.\n- **Late entry is a structural disadvantage.** Entering after the RFP issued, with no relationship history, drops base rate ~15%. The skill names this, doesn't hide it.\n\n## Anti-patterns\n\n- **Inventing a proof point to fill a GAP.** Hard rule violation. GAPs surface for leadership decision, not for prose-laundering. See `references\u002Frfp_anti_patterns.md`.\n- **Responding to every RFP.** Without a qualified bid \u002F no-bid gate, the team burns capacity on \u003C20% winrate pursuits and loses the 50%+ pursuits to lack of focus. Bain commercial-discipline research.\n- **Generic response with no win-theme.** A proposal that could be sent verbatim by any competitor is decorative. Shipley failure mode #1.\n- **Missing a mandatory disqualifier late.** FedRAMP \u002F HIPAA \u002F ISO 27001 \u002F SOC 2 \u002F on-shore data residency caught on Day 12 of a 14-day response = wasted pursuit. Parser surfaces these on Day 1.\n- **Answering the question YOU wanted asked.** RFP responder discipline: answer what they asked, in their words, in their order. Re-framing belongs in cover letters, not in the compliance matrix.\n- **No compliance matrix.** Every requirement should map to a response section + page number. Evaluators score on a matrix; respondents who don't provide one self-disqualify on traceability.\n- **Late-entry without acknowledging the relationship deficit.** Entering cold against an incumbent with a 3-year relationship and no champion = sub-20% winrate. Pretending otherwise wastes Sales Engineering capacity.\n- **Treating WEIGHTED requirements like MANDATORY.** Score-weighted requirements reward depth on the high-weight items, not uniform mediocrity across all. Shipley capture method.\n\n## Distinct from\n\n- **`business-growth\u002Fcontract-and-proposal-writer`** — free-form narrative proposals where YOU set the structure (executive briefs, capability statements, unsolicited proposals). RFP-responder handles **buyer-dictated structured Q&A** where the buyer set the questions, sections, scoring criteria, and format. Different artifact, different decision logic.\n- **`c-level-advisor\u002Fgeneral-counsel-advisor`** — contract redline and IP\u002Frisk review AFTER award. RFP-responder operates BEFORE award, on the response strategy.\n- **`marketing-skill\u002F*`** — external marketing assets (web copy, content, ASO, SEO, brand voice) for many-to-many audiences. RFP-responder produces a **single-buyer artifact** with deterministic compliance requirements.\n- **`commercial\u002Fdeal-desk`** — per-deal discount routing on a closing opportunity. RFP-responder is pursuit-stage; deal-desk is close-stage.\n- **`commercial\u002Fpricing-strategist`** — pricing-model design for a new product. RFP-responder consumes existing pricing as input to the commercial-terms section.\n\n## Forcing-question library (Matt Pocock grill discipline)\n\nWalked one at a time before any script runs. Recommended answer + canon citation per question. Never bundled.\n\n1. **\"What's your STRONG \u002F PARTIAL \u002F GAP split on the MANDATORY requirements?\"**\n   Recommended: STRONG ≥ 70% on MANDATORY before bidding. PARTIAL\u002FGAP on any MANDATORY = either close the gap pre-submission or no-bid.\n   Canon: Shipley *Proposal Guide v6* — capture-management discipline, \"Pgw (probability of win) is bounded by your weakest MANDATORY.\"\n\n2. **\"Is there an incumbent, and how strong is their position?\"**\n   Recommended: strong incumbent (3+ years, no displacement event) drops base winrate ~30%. Don't bid without a named displacement trigger.\n   Canon: Forrester B2B-RFP research — incumbents win 70-80% of renewal RFPs absent a named failure event.\n\n3. **\"Did you enter the conversation before or after the RFP issued?\"**\n   Recommended: late-entry (after RFP issued, no prior engagement) drops winrate ~15% and signals the RFP was scoped to someone else's strengths.\n   Canon: Tom Searcy + Henry DeVries *How to Win Big Business* — \"If you didn't help write the RFP, you're column fodder.\"\n\n4. **\"What are your 3-5 win-themes, and does each thread through ≥2 requirements?\"**\n   Recommended: themes that appear in only one requirement are decorative. Themes must ladder up across MANDATORY + WEIGHTED sections.\n   Canon: Shipley *Capture Guide* — win-themes are the buyer-side answer to \"why us\" across the evaluation criteria, not seller-side feature lists.\n\n5. **\"For every claim in the response, can you name the verifiable source?\"**\n   Recommended: every claim → case study \u002F certification \u002F customer reference \u002F technical attestation \u002F benchmark. Unsourced claims = GAPs.\n   Canon: APMP BoK — \"Substantiation: every assertion in a proposal must be backed by evidence the evaluator can independently verify.\"\n\n6. **\"What's the bid \u002F no-bid threshold you committed to BEFORE seeing this RFP?\"**\n   Recommended: pre-committed threshold (e.g., winrate ≥ 25%, STRONG ≥ 70% on MANDATORY, named champion). Post-hoc rationalization is how teams end up bidding 5% pursuits.\n   Canon: Bain RFP-win-rate studies — disciplined bid\u002Fno-bid gates lift win-rate from ~15% to ~35%.\n\n7. **\"What does the buyer's evaluation team actually score on?\"**\n   Recommended: if the RFP discloses scoring criteria, weight your response effort proportionally. If undisclosed, ask. If you can't ask, that itself is a relationship-deficit signal.\n   Canon: Strategic Proposals proposal-management research — evaluators score on the rubric they were given, not on your narrative.\n\nWalk depth-first. Lock 1-3 before opening 4-7. After all 7 are answered, invoke `rfp_parser.py` → `response_drafter.py` → `winrate_predictor.py` in sequence. If question 6 lands on \"we don't have a threshold,\" set one now or no-bid.\n","# rfp-responder\n\n## Purpose\n\nHelp Bid Managers, Proposal Leads, and Directors of Sales answer five questions at the response-strategy moment:\n\n1. **What is this RFP actually asking?** (parse sections, tag every requirement MANDATORY \u002F WEIGHTED \u002F NICE-TO-HAVE, extract scoring criteria, surface deadlines and format constraints)\n2. **What is our true fit?** (proof-point matrix per requirement: STRONG \u002F PARTIAL \u002F GAP, each backed by a verifiable source — case study, certification, customer quote, technical attestation, benchmark)\n3. **What is our win-theme strategy?** (Shipley method: 3-5 themes that ladder up across requirements, not generic value-prop bullets)\n4. **What is our realistic winrate?** (Shipley-derived factor model: fit, incumbent, relationship strength, decision-criteria alignment, late-entry, competitor count, deal size — produces estimate + confidence band)\n5. **Should we bid?** (deterministic verdict: BID \u002F PARTNER-BID \u002F NO-BID with named factors driving the call)\n\nThe skill surfaces GAPs explicitly. Leadership decides whether to close them, partner around them, or no-bid. **It never invents claims.**\n\n## When to use\n\n- A 30+ page RFP \u002F RFI \u002F RFQ has landed with a 7-14 day response deadline\n- A security questionnaire (SIG, CAIQ, custom-buyer) needs structured Q&A — not prose\n- The team is preparing a bid \u002F no-bid review and needs a defensible winrate estimate\n- Sales Engineering has a proof-point library but no system to map proofs to requirements\n- Leadership wants to see fit % (STRONG \u002F PARTIAL \u002F GAP) before committing pursuit budget\n- A late-entry opportunity needs honest assessment of the relationship deficit\n\n**Do not use for:**\n- Free-form proposal narrative authoring → `business-growth\u002Fcontract-and-proposal-writer`\n- Contract redline AFTER award → `c-level-advisor\u002Fgeneral-counsel-advisor`\n- Marketing collateral \u002F category content → `marketing-skill\u002F*`\n- Discount approval on the awarded deal → `commercial\u002Fdeal-desk`\n- Pricing-model design for a new product → `commercial\u002Fpricing-strategist`\n\n## Workflow\n\n### Step 1 — Parse the RFP\n\nDrop the RFP markdown \u002F text into `scripts\u002Frfp_parser.py`. Output: structured JSON listing every requirement, tagged MANDATORY \u002F WEIGHTED \u002F NICE-TO-HAVE based on cue words (must \u002F shall = MANDATORY; should \u002F weighted scoring numbers = WEIGHTED; may \u002F preferred \u002F desired = NICE-TO-HAVE). Captures section structure, scoring criteria if disclosed, deadline, submission format constraints.\n\n```bash\npython scripts\u002Frfp_parser.py --input rfp.md --output json > parsed.json\n```\n\n### Step 2 — Score fit per requirement\n\nFill `assets\u002Frfp_intake_template.md` with your proof-point library (each proof tagged with type + verifiable source + which requirement-tags it covers) and proposed win-themes. Feed parsed RFP + intake into `scripts\u002Fresponse_drafter.py`. Output: proof-point matrix per requirement with STRONG \u002F PARTIAL \u002F GAP, win-theme injection, GAP audit.\n\n```bash\npython scripts\u002Fresponse_drafter.py --input draft_input.json --output markdown > matrix.md\n```\n\n**Hard rule:** GAP requirements are surfaced, never invented around. Leadership reads the GAP audit and decides: close the gap, partner-bid, or no-bid.\n\n### Step 3 — Apply win-theme strategy\n\nShipley method: 3-5 themes that span requirements. Each theme answers \"why us over the incumbent \u002F competitor on the criteria the buyer named.\" `response_drafter.py` shows which themes thread through which requirements — a theme appearing in \u003C2 requirements is decorative, not strategic, and gets flagged.\n\n### Step 4 — Estimate winrate\n\nFeed deal context (fit %, incumbent strength, relationship, decision-criteria alignment, late-entry, competitor count, deal size vs. average) into `scripts\u002Fwinrate_predictor.py`. Output: Shipley-derived estimate 0-100% + confidence band + factor breakdown + BID \u002F PARTNER-BID \u002F NO-BID verdict.\n\n```bash\npython scripts\u002Fwinrate_predictor.py --input deal_context.json --profile enterprise-software --output markdown\n```\n\n**No-bid threshold:** estimate \u003C 20% triggers automatic no-bid recommendation.\n\n### Step 5 — Decide\n\nTake parsed RFP + proof-point matrix + GAP audit + winrate estimate into the go \u002F no-go review. Skill does not commit pursuit budget — leadership does.\n\n## Scripts\n\n- `scripts\u002Frfp_parser.py` — section + requirement extractor (regex + cue-word heuristics, stdlib only)\n- `scripts\u002Fresponse_drafter.py` — proof-point matrix + win-theme injection + GAP audit\n- `scripts\u002Fwinrate_predictor.py` — Shipley-derived factor model + bid\u002Fno-bid verdict, industry-profile-tuned\n\nAll scripts: stdlib only (argparse, json, sys, pathlib, re, collections, statistics). `--help` and `--sample` work on all three.\n\n## References\n\n- `references\u002Fshipley_method_canon.md` — Shipley Proposal Guide v6, Shipley Capture Guide, APMP BoK, Tom Sant, Tom Searcy + Henry DeVries, Strategic Proposals research, Larry Newman\n- `references\u002Frfp_strategy_canon.md` — FAR, GSA, Forrester, Gartner, Bain, McKinsey, B2B International on RFP win-rates and buyer behavior\n- `references\u002Frfp_anti_patterns.md` — Shipley failure modes, APMP cases, Strategic Proposals research, federal loss reviews, MIT Sloan, Bain commercial-discipline, Gartner\n\n## Assumptions\n\n- **The RFP is the ground truth.** If the buyer asked it, answer it — in the order they asked, in the format they specified. Re-organizing for narrative flow is for proposals, not RFPs.\n- **Proof points must be verifiable.** A claim is only as strong as the case study, certification, customer reference, or technical attestation backing it. Unsourced claims become GAPs.\n- **Win-themes are buyer-side, not seller-side.** \"We're the leader in X\" is a marketing claim; \"Your operations team reduces incident MTTR by 60% with the same headcount\" is a win-theme. Shipley canon, not optional.\n- **Winrate estimates are directional.** The model is a discipline tool to force honest pursuit-qualification — not an oracle. Confidence band always wider than the point estimate suggests.\n- **Industry profiles tune base rates** — government RFPs reward compliance discipline; enterprise SaaS rewards reference accounts; healthcare rewards regulatory + security depth.\n- **Late entry is a structural disadvantage.** Entering after the RFP issued, with no relationship history, drops base rate ~15%. The skill names this, doesn't hide it.\n\n## Anti-patterns\n\n- **Inventing a proof point to fill a GAP.** Hard rule violation. GAPs surface for leadership decision, not for prose-laundering. See `references\u002Frfp_anti_patterns.md`.\n- **Responding to every RFP.** Without a qualified bid \u002F no-bid gate, the team burns capacity on \u003C20% winrate pursuits and loses the 50%+ pursuits to lack of focus. Bain commercial-discipline research.\n- **Generic response with no win-theme.** A proposal that could be sent verbatim by any competitor is decorative. Shipley failure mode #1.\n- **Missing a mandatory disqualifier late.** FedRAMP \u002F HIPAA \u002F ISO 27001 \u002F SOC 2 \u002F on-shore data residency caught on Day 12 of a 14-day response = wasted pursuit. Parser surfaces these on Day 1.\n- **Answering the question YOU wanted asked.** RFP responder discipline: answer what they asked, in their words, in their order. Re-framing belongs in cover letters, not in the compliance matrix.\n- **No compliance matrix.** Every requirement should map to a response section + page number. Evaluators score on a matrix; respondents who don't provide one self-disqualify on traceability.\n- **Late-entry without acknowledging the relationship deficit.** Entering cold against an incumbent with a 3-year relationship and no champion = sub-20% winrate. Pretending otherwise wastes Sales Engineering capacity.\n- **Treating WEIGHTED requirements like MANDATORY.** Score-weighted requirements reward depth on the high-weight items, not uniform mediocrity across all. Shipley capture method.\n\n## Distinct from\n\n- **`business-growth\u002Fcontract-and-proposal-writer`** — free-form narrative proposals where YOU set the structure (executive briefs, capability statements, unsolicited proposals). RFP-responder handles **buyer-dictated structured Q&A** where the buyer set the questions, sections, scoring criteria, and format. Different artifact, different decision logic.\n- **`c-level-advisor\u002Fgeneral-counsel-advisor`** — contract redline and IP\u002Frisk review AFTER award. RFP-responder operates BEFORE award, on the response strategy.\n- **`marketing-skill\u002F*`** — external marketing assets (web copy, content, ASO, SEO, brand voice) for many-to-many audiences. RFP-responder produces a **single-buyer artifact** with deterministic compliance requirements.\n- **`commercial\u002Fdeal-desk`** — per-deal discount routing on a closing opportunity. RFP-responder is pursuit-stage; deal-desk is close-stage.\n- **`commercial\u002Fpricing-strategist`** — pricing-model design for a new product. RFP-responder consumes existing pricing as input to the commercial-terms section.\n\n## Forcing-question library (Matt Pocock grill discipline)\n\nWalked one at a time before any script runs. Recommended answer + canon citation per question. Never bundled.\n\n1. **\"What's your STRONG \u002F PARTIAL \u002F GAP split on the MANDATORY requirements?\"**\n   Recommended: STRONG ≥ 70% on MANDATORY before bidding. PARTIAL\u002FGAP on any MANDATORY = either close the gap pre-submission or no-bid.\n   Canon: Shipley *Proposal Guide v6* — capture-management discipline, \"Pgw (probability of win) is bounded by your weakest MANDATORY.\"\n\n2. **\"Is there an incumbent, and how strong is their position?\"**\n   Recommended: strong incumbent (3+ years, no displacement event) drops base winrate ~30%. Don't bid without a named displacement trigger.\n   Canon: Forrester B2B-RFP research — incumbents win 70-80% of renewal RFPs absent a named failure event.\n\n3. **\"Did you enter the conversation before or after the RFP issued?\"**\n   Recommended: late-entry (after RFP issued, no prior engagement) drops winrate ~15% and signals the RFP was scoped to someone else's strengths.\n   Canon: Tom Searcy + Henry DeVries *How to Win Big Business* — \"If you didn't help write the RFP, you're column fodder.\"\n\n4. **\"What are your 3-5 win-themes, and does each thread through ≥2 requirements?\"**\n   Recommended: themes that appear in only one requirement are decorative. Themes must ladder up across MANDATORY + WEIGHTED sections.\n   Canon: Shipley *Capture Guide* — win-themes are the buyer-side answer to \"why us\" across the evaluation criteria, not seller-side feature lists.\n\n5. **\"For every claim in the response, can you name the verifiable source?\"**\n   Recommended: every claim → case study \u002F certification \u002F customer reference \u002F technical attestation \u002F benchmark. Unsourced claims = GAPs.\n   Canon: APMP BoK — \"Substantiation: every assertion in a proposal must be backed by evidence the evaluator can independently verify.\"\n\n6. **\"What's the bid \u002F no-bid threshold you committed to BEFORE seeing this RFP?\"**\n   Recommended: pre-committed threshold (e.g., winrate ≥ 25%, STRONG ≥ 70% on MANDATORY, named champion). Post-hoc rationalization is how teams end up bidding 5% pursuits.\n   Canon: Bain RFP-win-rate studies — disciplined bid\u002Fno-bid gates lift win-rate from ~15% to ~35%.\n\n7. **\"What does the buyer's evaluation team actually score on?\"**\n   Recommended: if the RFP discloses scoring criteria, weight your response effort proportionally. If undisclosed, ask. If you can't ask, that itself is a relationship-deficit signal.\n   Canon: Strategic Proposals proposal-management research — evaluators score on the rubric they were given, not on your narrative.\n\nWalk depth-first. Lock 1-3 before opening 4-7. After all 7 are answered, invoke `rfp_parser.py` → `response_drafter.py` → `winrate_predictor.py` in sequence. If question 6 lands on \"we don't have a threshold,\" set one now or no-bid.\n",{"name":5,"tags":23,"author":33,"context":34,"license":14,"version":35,"description":7,"compatible_tools":36},[24,25,26,27,28,29,30,31,32],"commercial","rfp","rfi","rfq","shipley","win-theme","proof-points","structured-response","bid-management","claude-code-skills","fork","2.8.0",[37,38,39,40,41,42],"claude-code","codex-cli","cursor","antigravity","opencode","gemini-cli","main","56d41613d1ca1e2b0614a19c3816ec97a6159ec2","commercial\u002Fskills\u002Frfp-responder","https:\u002F\u002Fgithub.com\u002Fmsm47\u002FGitskil","https:\u002F\u002Fgithub.com\u002Fmsm47","2026-06-22 18:36:50",{"source":8,"skillName":5,"commands":50,"prompt":60,"zipAvailable":61},[51,54,57],{"tool":52,"command":53},"npx","npx skills add msm47\u002FGitskil --skill rfp-responder",{"tool":55,"command":56},"bunx","bunx skills add msm47\u002FGitskil --skill rfp-responder",{"tool":58,"command":59},"pnpm","pnpm dlx skills add msm47\u002FGitskil --skill rfp-responder","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fmsm47\u002FGitskil\u002Fblob\u002Fmain\u002Fcommercial\u002Fskills\u002Frfp-responder\u002FSKILL.md",true,[63,69,75,79,83,89,93],{"path":64,"resourceType":65,"resourceTypeDesc":66,"sizeBytes":67,"sourceHtmlUrl":68},"assets\u002Frfp_intake_template.md","ASSET","资产文件",4612,"https:\u002F\u002Fgithub.com\u002Fmsm47\u002FGitskil\u002Fblob\u002Fmain\u002Fcommercial\u002Fskills\u002Frfp-responder\u002Fassets\u002Frfp_intake_template.md",{"path":70,"resourceType":71,"resourceTypeDesc":72,"sizeBytes":73,"sourceHtmlUrl":74},"references\u002Frfp_anti_patterns.md","REFERENCE","参考文档",9055,"https:\u002F\u002Fgithub.com\u002Fmsm47\u002FGitskil\u002Fblob\u002Fmain\u002Fcommercial\u002Fskills\u002Frfp-responder\u002Freferences\u002Frfp_anti_patterns.md",{"path":76,"resourceType":71,"resourceTypeDesc":72,"sizeBytes":77,"sourceHtmlUrl":78},"references\u002Frfp_strategy_canon.md",5862,"https:\u002F\u002Fgithub.com\u002Fmsm47\u002FGitskil\u002Fblob\u002Fmain\u002Fcommercial\u002Fskills\u002Frfp-responder\u002Freferences\u002Frfp_strategy_canon.md",{"path":80,"resourceType":71,"resourceTypeDesc":72,"sizeBytes":81,"sourceHtmlUrl":82},"references\u002Fshipley_method_canon.md",5758,"https:\u002F\u002Fgithub.com\u002Fmsm47\u002FGitskil\u002Fblob\u002Fmain\u002Fcommercial\u002Fskills\u002Frfp-responder\u002Freferences\u002Fshipley_method_canon.md",{"path":84,"resourceType":85,"resourceTypeDesc":86,"sizeBytes":87,"sourceHtmlUrl":88},"scripts\u002Fresponse_drafter.py","SCRIPT","脚本",12493,"https:\u002F\u002Fgithub.com\u002Fmsm47\u002FGitskil\u002Fblob\u002Fmain\u002Fcommercial\u002Fskills\u002Frfp-responder\u002Fscripts\u002Fresponse_drafter.py",{"path":90,"resourceType":85,"resourceTypeDesc":86,"sizeBytes":91,"sourceHtmlUrl":92},"scripts\u002Frfp_parser.py",10731,"https:\u002F\u002Fgithub.com\u002Fmsm47\u002FGitskil\u002Fblob\u002Fmain\u002Fcommercial\u002Fskills\u002Frfp-responder\u002Fscripts\u002Frfp_parser.py",{"path":94,"resourceType":85,"resourceTypeDesc":86,"sizeBytes":95,"sourceHtmlUrl":96},"scripts\u002Fwinrate_predictor.py",9111,"https:\u002F\u002Fgithub.com\u002Fmsm47\u002FGitskil\u002Fblob\u002Fmain\u002Fcommercial\u002Fskills\u002Frfp-responder\u002Fscripts\u002Fwinrate_predictor.py",[98,105,112,119,126,133],{"skillNo":99,"name":100,"nameZh":6,"description":101,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":102,"categories":103,"keywords":104,"publishedAt":18},"01M76S8VB9Q65XVZXC99QVF20M","syllabus","Generates a curated supplementary reading list from any course syllabus using Consensus academic search. Grill-me intake (syllabus input format + course audience + year range) plus a grouping forcing-options checkpoint before any search runs — so the reading list matches the course's level and recency need. Parses the syllabus to extract topics and learning outcomes, searches Consensus for recent peer-reviewed papers per topic, and produces a professionally formatted .docx with clickable Consensus links, plain-language summaries calibrated to audience level, and Bloom-higher-order discussion questions tied to course learning goals. Triggers whenever a user uploads a syllabus, course outline, or curriculum document and wants supplementary readings. Also triggers on: 'syllabus reading list', 'find papers for my course', 'create a reading list from this syllabus', 'recent research for my class', 'supplementary readings', 'find journal articles for these topics', 'what recent papers cover this material', 'any new research on these course topics', 'update my syllabus with recent papers'. Even casual mentions when a syllabus is attached should trigger this skill.","https:\u002F\u002Fgithub.com\u002Fmsm47\u002FGitskil\u002Fblob\u002Fmain\u002Fresearch\u002Fsyllabus\u002Fskills\u002Fsyllabus\u002FSKILL.md",[],[],{"skillNo":106,"name":107,"nameZh":6,"description":108,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":109,"categories":110,"keywords":111,"publishedAt":18},"01M76S8K8X17M3CV7RENS6KA32","research","Default entry point for any research request — a hybrid router that classifies the question deterministically and either delegates to a specialist research skill (pulse for trends\u002Fsentiment, grants for NIH funding, litreview for academic literature, syllabus for course reading, patent for prior-art + IP landscape, dossier for entity research) or runs its own plan-decompose-multi-source-search-synthesize-cite fallback workflow when no specialist matches. Always surfaces the routing decision so users can override. Triggers — \"research [topic]\", \"look into [topic]\", \"what do we know about [topic]\", \"investigate [topic]\", \"find me information on [topic]\", \"do some research on [topic]\", \"I need to understand [topic]\", or any research request that doesn't obviously match a more-specific specialist skill. Output is a markdown briefing (default) or .docx document (on request) with full citations and an audit log.","https:\u002F\u002Fgithub.com\u002Fmsm47\u002FGitskil\u002Fblob\u002Fmain\u002Fresearch\u002Fresearch\u002Fskills\u002Fresearch\u002FSKILL.md",[],[],{"skillNo":113,"name":114,"nameZh":6,"description":115,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":116,"categories":117,"keywords":118,"publishedAt":18},"01M76S8AZCNS7Q5M4G1SBQ8HDV","pulse","Multi-source recency research skill that takes the pulse of any topic across Reddit, Hacker News, the open web, and optionally X\u002FTwitter within a configurable recent window (default 30 days). Forcing intake clarifies topic specificity, angle (trend\u002Fsentiment\u002Fproblems\u002Fopportunities\u002Fcomparison), time window, and platform scope before searching. Returns a synthesized briefing with citations, engagement metrics, and cross-platform pattern analysis. Triggers: 'pulse on [topic]', 'what's happening with [topic]', 'what are people saying about [topic]', 'current conversation about [topic]', 'take the pulse of [topic]', 'trending: [topic]', 'find me info on [topic]', or any variation requesting multi-source recency intelligence on a topic. Also use for competitor research, trend discovery, tool comparisons, and audience sentiment analysis.","https:\u002F\u002Fgithub.com\u002Fmsm47\u002FGitskil\u002Fblob\u002Fmain\u002Fresearch\u002Fpulse\u002Fskills\u002Fpulse\u002FSKILL.md",[],[],{"skillNo":120,"name":121,"nameZh":6,"description":122,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":123,"categories":124,"keywords":125,"publishedAt":18},"01M76S7YVS0NHD51AFTZM5965R","patent","Patent prior-art and landscape intelligence skill — not generic patent help. Commits to one of five sub-use-cases via forcing intake (novelty search \u002F freedom-to-operate \u002F competitive landscape \u002F acquisition diligence \u002F litigation prior-art) before any search runs. Searches Google Patents, Espacenet, USPTO, and optionally Lens.org for citation-graph signals. Output is an editable Word document (.docx) with verdict, ranked closest art (claim-text extracted), CPC-class-aware landscape, family-resolved hits, geographic coverage, FTO flags where applicable, strategy recommendations, and full audit log. Triggers: 'prior art search for [invention]', 'patent search on [topic]', 'freedom to operate analysis', 'FTO for [product]', 'patent landscape for [field]', 'is [invention] novel', 'patents on [topic]', 'competitive patent analysis', 'prior art for litigation', 'patent diligence on [company]'. Produces search signal, not legal advice — always recommends consulting a patent attorney before filing or licensing decisions. Trademark, copyright, and trade-secret questions are out of scope.","https:\u002F\u002Fgithub.com\u002Fmsm47\u002FGitskil\u002Fblob\u002Fmain\u002Fresearch\u002Fpatent\u002Fskills\u002Fpatent\u002FSKILL.md",[],[],{"skillNo":127,"name":128,"nameZh":6,"description":129,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":130,"categories":131,"keywords":132,"publishedAt":18},"01M76S7MQDZR49Y3DRXF394R2F","notebooklm","Browser automation skill for controlling Google's NotebookLM. Handles reading and querying notebooks, adding sources (URLs, text, files, YouTube links, synthesized content), generating Studio outputs (Audio Overview, infographics, slide decks, study guides, briefing docs, mind maps, timelines, FAQs), and creating new notebooks. Triggers on any phrase involving NotebookLM — 'open NotebookLM', 'check my [name] notebook', 'pull info from NotebookLM', 'ask my notebook about X', 'add [source] to NotebookLM', 'create an infographic in NotebookLM', 'use NotebookLM Studio', 'generate a slide deck from my notebook', or any variation where the goal involves NotebookLM. Requires browser automation environment — fails gracefully when unavailable.","https:\u002F\u002Fgithub.com\u002Fmsm47\u002FGitskil\u002Fblob\u002Fmain\u002Fresearch\u002Fnotebooklm\u002Fskills\u002Fnotebooklm\u002FSKILL.md",[],[],{"skillNo":134,"name":135,"nameZh":6,"description":136,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":137,"categories":138,"keywords":139,"publishedAt":18},"01M76S78K1ZJ092YYRKX0KBTYJ","litreview","Academic literature orientation skill that searches papers via Consensus, builds a strategic search plan using PICO (default) or SPIDER \u002F Decomposition \u002F hybrid as fallbacks, and synthesizes findings into a professionally formatted Word document (.docx) research guide. Grill-me intake (research question specificity + framework hint + tentative depth) before the recon search; a second forcing checkpoint after Phase 2 confirms framework + sub-areas + depth before searches consume budget. Configurable depth (5\u002F10\u002F20 queries) controls coverage vs. speed. Output is a 'launching pad' — not a finished review, but an orientation guide that lets a researcher dive in confidently. Triggers: 'litreview on [topic]', 'literature review on [topic]', 'I'm starting a literature review on X', 'I'm writing a paper on X', 'help me research X', 'I'm doing research on X', 'can you help me research X'. Do NOT trigger for single one-off paper searches where the user just wants a quick list — that's a plain Consensus search.","https:\u002F\u002Fgithub.com\u002Fmsm47\u002FGitskil\u002Fblob\u002Fmain\u002Fresearch\u002Flitreview\u002Fskills\u002Flitreview\u002FSKILL.md",[],[]]