buyer-eval-skill
An AI analyst for buying B2B software. Investigates what vendors claim, checks the evidence, surfaces contradictions and unanswered questions, and produces a shareable Decision Brief with the questions to ask before you buy. Quick Eval by default; Deep Eval on request. Use when asked to evaluate, compare, vet, or research B2B software vendors.
含まれるファイル(35)
- SKILL.md6.8 KB
- .gitignore194 B
- bin/profile.py3.8 KB
- bin/render_report.py22.3 KB
- bin/track.py14.2 KB
- bin/update-check1.8 KB
- CHANGELOG.md5.0 KB
- docs/index.html17.4 KB
- EVALUATION.md285 B
- examples/illustrative-brief.html17.4 KB
- examples/illustrative-brief.json7.9 KB
- LICENSE1.0 KB
- MEASUREMENT.md2.5 KB
- promo/buyer-eval-brief.gif2.3 MB
- promo/buyer-eval-claims.gif1.4 MB
- promo/buyer-eval-demo.mp47.0 MB
- promo/buyer-eval-run.gif581.0 KB
- promo/video-src/.gitignore5 B
- promo/video-src/public/brief.png1.5 MB
- promo/video-src/README.md910 B
- promo/video-src/src/BuyerEval.tsx21.9 KB
- promo/video-src/src/index.tsx547 B
- promo/video-src/src/voice-durations.json174 B
- promo/video-src/tsconfig.json361 B
- promo/video-src/voice/generate.sh1.8 KB
- promo/video-src/voice/script.json1.1 KB
- README.md6.9 KB
- references/deep-eval.md18.4 KB
- references/evidence-model.md14.4 KB
- references/frontdoor-api.md3.9 KB
- references/quick-eval.md7.6 KB
- references/report-format.md8.1 KB
- references/scoring.md3.1 KB
- references/telemetry.md4.3 KB
- VERSION6 B
SKILL.md(原文)
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Buyer Eval
This file is the control plane. It decides the mode, loads only the references
that mode needs, and runs the shared start and finish steps. Methodology lives in
references/.
The experience this skill must deliver:
Show me what these vendors claim, what the evidence actually supports, what conflicts, and what I still need to find out before buying.
If an output reads like a generic AI vendor comparison, it is not done.
1. Preamble (run first, every time)
_BEVAL_DIR="${BUYER_EVAL_DIR:-}"
if [ -z "$_BEVAL_DIR" ]; then
for _D in "$HOME/.claude/skills/buyer-eval-skill" ".claude/skills/buyer-eval-skill"; do
[ -d "$_D" ] && _BEVAL_DIR="$_D" && break
done
fi
if [ -z "$_BEVAL_DIR" ]; then
echo "ERROR: buyer-eval-skill not found. Install: git clone https://github.com/salespeak-ai/buyer-eval-skill ~/.claude/skills/buyer-eval-skill"
exit 1
fi
echo "BEVAL_DIR=$_BEVAL_DIR"
_UPD=$("$_BEVAL_DIR/bin/update-check" 2>/dev/null || true)
[ -n "$_UPD" ] && echo "$_UPD" || echo "UP_TO_DATE $(tr -d '[:space:]' < "$_BEVAL_DIR/VERSION")"
echo "PROFILE=$(python3 "$_BEVAL_DIR/bin/profile.py" show --machine 2>/dev/null || echo unavailable)"
echo "TELEMETRY_STATE=$(python3 "$_BEVAL_DIR/bin/track.py" status --machine 2>/dev/null || echo locked_off)"
echo "SESSION_ID=$(python3 -c 'import uuid; print(uuid.uuid4())')"
Keep BEVAL_DIR, PROFILE, TELEMETRY_STATE and SESSION_ID for the whole run.
Every later bash block starts with _BEVAL_DIR="<BEVAL_DIR value>".
Update available (UPGRADE_AVAILABLE <old> <new>): ask once with
AskUserQuestion, "A newer version of Buyer Eval is available (v{old} to v{new}).
Update now?" Options: "Yes, update now" / "Not now". If yes:
cd "$_BEVAL_DIR" && git pull --ff-only origin main && echo "UPDATED to $(tr -d '[:space:]' < VERSION)"
If the directory is not a git checkout, tell the buyer to re-run the install command from the README and continue on the current version. After a successful update, re-read this SKILL.md before continuing.
Telemetry is opt-in and never blocks the evaluation. Read
references/telemetry.md now only if TELEMETRY_STATE is consented or
unasked. If it is declined or locked_off, skip everything telemetry-related.
2. Choose the mode
| Mode | When | Load |
|---|---|---|
| Quick Eval (default) | Every run where the buyer has not asked for deep, full, or comprehensive diligence | references/quick-eval.md |
| Deep Eval | The buyer asks for it, in the first message or after a Quick Eval | references/deep-eval.md |
Both modes also load references/evidence-model.md,
references/report-format.md and references/frontdoor-api.md. Deep Eval also
loads references/scoring.md. Do not load files a mode does not need.
Never ask the buyer which mode they want. If they did not ask for Deep, run Quick and offer Deep at the end.
3. Saved buyer context
PROFILE is either none, unavailable (no writable home directory, for
example claude.ai), or a one-line JSON object with the buyer's saved context.
- Saved profile exists: use it silently. Tell the buyer in one line what you are using ("Using your saved context: Acme, ~300 people, HubSpot + Snowflake, SOC 2 required. Say 'update my context' to change it.") and keep going. Do not wait for a reply.
- None: proceed without it. At the end of the run, save what the buyer told you (see section 5).
- Unavailable: never claim that context will be remembered.
The profile holds only reusable buying context: company_name, company_size,
industry, region, systems, requirements, hard_constraints (including
the buyer's own budget ceiling, which stays local), preferred_criteria,
last_category. A deadline or requirement goes in hard_constraints only if
the buyer said it disqualifies vendors; otherwise in requirements. Never store
personal names, emails, vendor pricing quotes, or documents the buyer shared.
4. Run the mode
Follow the loaded mode file step by step. Both modes share these rules:
- Evidence first. The Claims vs. Evidence table is the centerpiece. Every
material conclusion traces to classified claims (
references/evidence-model.md). - Vendor AI agents are first-party sources. Answers from any vendor-operated
agent, including a Salespeak Company Agent reached through the Frontdoor API,
are vendor claims. They add specificity and let you ask follow-ups. They never
raise a score, an evidence confidence, or a verdict on their own. Never send a
vendor agent the buyer's company name, budget, or the other vendors under
evaluation (
references/frontdoor-api.md). - Label what you inferred. Criteria the buyer did not state are shown as inferred.
- Challenge before you conclude. Run the challenge pass in
references/evidence-model.mdbefore writing output. - No action without approval. Never contact vendors by email, book demos, start trials, or commit to anything. Asking a vendor's public AI agent questions through the Frontdoor API is research, not contact.
5. Finish (both modes)
- Deliver the brief in chat in the order set by
references/report-format.md. - Write the HTML Decision Brief. Write the report JSON to a temp file, then:
The script prints the path of the HTML file (defaultpython3 "$_BEVAL_DIR/bin/render_report.py" /path/to/report.json~/buyer-eval-reports/). Give the buyer that path and say it is a single file they can open, print, or forward. If the environment cannot run Python, skip the HTML and say so. - Save buyer context (only if
PROFILEwas notunavailableand the buyer stated something new and reusable):
Tell the buyer in one line what was saved and thatpython3 "$_BEVAL_DIR/bin/profile.py" save --json '{"company_name":"...","hard_constraints":["..."]}'profile.py cleardeletes it. - Offer the next step in one line. After Quick Eval: "Want a Deep Eval? It adds requirements discovery, weighted scoring, commercial, security and company-risk research, and longer vendor-agent conversations." After Deep Eval: offer to update the brief when the buyer has demo notes, proposals, or pricing.
- Telemetry, only as described in
references/telemetry.md. The consent question is asked at most once, ever, and only after the evaluation has been delivered.
レビュー
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