agentsclimarketplace

Gtm reverse engineer

Skill ndpvt-web/gtm-reverse-engineer

Reverse-engineer any company's public go-to-market into an evidence-graded, multi-file playbook (copy / adapt / skip). Use whenever the user wants competitor GTM teardown, growth forensics, channel analysis (Instagram, X/Twitter, LinkedIn, ads, Reddit/community, SEO, jobs, pricing), "how did X grow", unicorn/breakout GTM playbook, modular GTM research, or to install a 90-day motion plan from public signals. Trigger even if they say "spy on their GTM", "tear down their acquisition", or name a single channel (ads only, X only, community only). Prefer this skill over ad-hoc web search when a structured pack or playbook is desired.From its SKILL.md

Install
npx -y skills add ndpvt-web/gtm-reverse-engineer

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 2 stars2 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

10.6 KB, ~2.4k tokens by cl100k_base, as published. Nobody here has run it

GTM Reverse Engineer

General, company-agnostic skill. Nothing about a specific brand is hardcoded in runtime logic. The target company is always supplied by the user (or clarified before run).

Defaults (override if user already chose otherwise)

SettingDefault
Skill idgtm-reverse-engineer
Output root./outputs/gtm-playbook-<slug>/ under current workspace
Depthstandard unless user says quick/max/full
Modulesuser-selected; if user says "full" / "in-depth" / "everything" → all modules
Collectorsauto-detect (see below); never require Kimi or any browser bridge
Extra vs base researchChannel Completeness Contracts on every gather + scorecard

If product choices are still ambiguous after reading the user message, ask before spending a large run. Do not invent the target company.

When this skill triggers

  • Competitor / company GTM, growth, acquisition, positioning teardown
  • Single-channel asks: "only Instagram", "only X", "only ads", "only community"
  • Full playbook / 90-day copy plan / what not to copy
  • Modular or full-depth GTM research workflows

Non-goals / honesty fence

  • Not insider or "stolen secrets." Public and user-session-visible signals only.
  • Not audited financials. Company-claimed ARR/users/valuation stay labeled.
  • Not equal depth for every company. Thin public exhaust → thin pack + explicit coverage.
  • Do not hardcode example companies as the run target. Examples in docs are illustrations only.

Required inputs

  1. Target (required): company name and/or website URL.
  2. Modules (required unless full): list or preset (full, quick, or explicit module ids).
  3. Optional: geo focus, B2B vs B2C emphasis, output directory, depth (quick | standard | max).

If target is missing or ambiguous (multiple entities share the name), stop and disambiguate (entity lock) before gather.

Module catalog (v1)

Compose any subset. full enables all gather modules + analyze bundle + verify + playbook.

Module idPurpose
entityCompany lock, founders/team surface, timeline anchors
press_fundingPress, launches, funding claims (graded)
product_pricingSite, pricing, packaging, enterprise/affiliates pages
jobsCareers/jobs as GTM org proxy
social_xX/Twitter official + founders (archives/web first)
social_linkedinLinkedIn company (+ founders if reachable)
social_instagramInstagram official
adsMeta/Google ad library and paid creative angles
communityReddit/community sentiment and objections
seo_distributionSEO content, PH, affiliates, distribution surfaces
youtube_interviewsTalks/interviews index + transcripts when available
analyzeAnalysis suite (scoped to enabled evidence)
verifyAdversarial review + claim audit + confidence/gaps
playbookCopyable playbook chapters (scoped)

Presets (resolved by scripts/init_run.py into concrete module ids):

  • quickentity, product_pricing, press_funding, analyze, verify
  • standard → all gather modules + analyze, verify, playbook
  • full / max → same module set as standard; depth controls effort/browser willingness, not a different module list

Scorecard is not a module. After any gather, always run completeness rollup (phase), then optional analyze slices.

Depth vs modules: modules = what runs; depth = how hard (query volume, browser enrich only if confirmed).

Read references/modules.md for per-module outputs, done-when, and analyze scope.

Pipeline (always) — load order

0. Parse args → resolve modules (init_run.py) → output paths
1. Detect collectors (detect_collectors.py) — browser_enrich_possible defaults false
2. Entity lock (serial; stop if ambiguous)
3. Gather enabled modules in parallel (each returns completeness_contract)
4. Orchestrator rollup COMPLETENESS-SCORECARD + RUN-CONFIG contracts
5. validate_run.py --phase post_gather  (block on FAIL)
6. Analyze (only dimensions supported by evidence)
7. Verify if enabled (claim audit + adversarial)
8. Playbook if enabled (only if module enabled)
9. validate_run.py --phase final; index + confidence map + gaps

For large multi-module runs, prefer Claude Code Workflow orchestration (dynamic-workflow patterns): phase, parallel, pipeline, structured agent returns. Pass modules_resolved, not preset strings. For single-module asks, inline agents are enough — Completeness Contracts remain mandatory.

Collector auto-detect (never hardcode Kimi)

Run scripts/detect_collectors.py (or follow references/collector-matrix.md) at start of every run.

Priority order for raw collection:

  1. Always: web search skill (if present), curl/HTTP fetch, public APIs/archives
  2. If present: YouTube transcript scripts/skills
  3. If browser bridge available (chrome-session-bridge / Capy browser bridge online): optional logged-in enrich for login-walled surfaces
  4. If Kimi webbridge skill + daemon/extension available: optional; one session per agent, max 2 tabs; never default bulk
  5. If nothing beyond web: proceed web-only; document limits in completeness contracts

Rules:

  • Detection must be runtime, not baked into module lists as "requires Kimi."
  • If user is not logged in and wall blocks, set contract blockers to include login_wall and list login_required_surfaces — do not invent engagement.
  • Never open mass tabs. Prefer one tab reused per browser agent.
  • Omit browser tools from prompts when browser_enrich_possible is false.

Channel Completeness Contracts (mandatory)

Every gather agent (including entity and single-module/inline runs) MUST emit a full canonical contract. Schema + enums: references/completeness-contracts.md.

All required keys: module, channel, method (web|archive|browser|mixed), claimed_complete (default false), items_captured, date_span, sample_note, what_was_not_covered, grade_of_collection, login_required_surfaces, blockers.

Rollup: orchestrator writes 00-meta/COMPLETENESS-SCORECARD.md and RUN-CONFIG.completeness_contracts after gather. Missing contract for an enabled gather module = QA fail (scripts/validate_run.py).

Analyze/playbook must not imply full channel history/census when claimed_complete is false.

Evidence grades

  • A primary (official site/app, official social, filings, careers, primary founder post/transcript)
  • B strong secondary with attribution
  • C pattern from multiple weaker signals (list them)
  • D single weak source — never playbook foundation
  • UNKNOWN missing — do not invent

Company-claimed metrics: always label. See references/evidence-grades.md.

Output tree (generic)

outputs/gtm-playbook-<slug>/
  00-meta/     README, SOURCE-INDEX, CONFIDENCE-MAP, GAPS, COMPLETENESS-SCORECARD, RUN-CONFIG
  01-entity/   COMPANY-LOCK, ...
  02-raw-evidence/<module>/...
  03-analysis/...
  04-playbook/...   (if enabled)
  05-verification/... (if enabled)

<slug> from target domain or normalized company name. Init via scripts/init_run.py when available.

Templates: references/output-tree.md.

RUN-CONFIG.json

Written by init_run.py at start (update at end). Important fields:

{
  "target_input": "<user string>",
  "entity_locked": null,
  "slug": null,
  "modules_preset": "standard",
  "modules_resolved": ["entity", "…"],
  "gather_modules": ["entity", "…"],
  "synthesis_modules": ["analyze", "verify", "playbook"],
  "depth": "standard",
  "collectors_detected": {},
  "output_dir": "",
  "started_at": "",
  "completed_at": null,
  "completeness_contracts": []
}

No default target company field. Never set target to an example output path.

Modular execution examples

User: "only X GTM for https://example.com"
→ modules: entity, social_x; completeness rollup phase; light analyze slice (social scorecard only); skip full playbook unless asked.

User: "Instagram ads only"
→ modules: entity, social_instagram, ads (IG-relevant), completeness contracts, ads/social scorecards.

User: "full in-depth GTM playbook for Acme"
→ all modules; Workflow recommended; verify + full playbook.

Workflow templates

  • workflows/README.md — how to compose
  • workflows/modular-gather.md — gather agent prompt patterns (parameterized)
  • workflows/full-run.md — full depth phase map
  • Do not ship company-specific JS. Any Workflow script must take args: { target, modules, outputDir, depth, collectors }.

QA expectations while using this skill

Before final handoff:

  1. Entity lock file exists and matches user target
  2. Every enabled gather module has a completeness contract
  3. No invented metrics
  4. SOURCE-INDEX lists sources
  5. GAPS-AND-UNKNOWNS honest
  6. If playbook enabled: recommendations cite grades

References to load as needed

FileWhen
references/modules.mdModule plan / dependencies
references/completeness-contracts.mdEvery gather
references/collector-matrix.mdStart of run
references/output-tree.mdInit outputs
references/evidence-grades.mdAnalysis / verify / playbook
references/playbook-chapters.mdPlaybook module
references/agent-prompt-templates.mdSpawning subagents
references/guardrails.mdBrowser/safety

Scripts

ScriptPurpose
scripts/detect_collectors.pyPrint JSON of available collectors
scripts/init_run.pyExpand presets, create tree + RUN-CONFIG
scripts/validate_run.pyFail-closed QA (`--phase post_gather

Anti-patterns

  • Hardcoding a demo company’s handles as defaults
  • Requiring Kimi/browser to run at all
  • One tab per post / mass navigation
  • Claiming complete social history without contract proof
  • Turning company-claimed ARR into playbook foundation

What ships with it: 19 files

52.5 KB alongside SKILL.md, 3 of them executable

scripts/

workflows/

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