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Agent skill

Growth Reviewer

by acogood in acogood/diffmode_free

Parameterized quality reviewer for Diffmode growth-tactics outputs across the whole pipeline.

Apache-2.0Auto-check passedEducation

Install Growth Reviewer

skills CLI
$ npx skills add acogood/diffmode_free --skill growth-reviewer -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install acogood/diffmode_free growth-reviewer --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/acogood/diffmode_free.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugin/skills/growth-reviewer .claude/skills/growth-reviewer && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
growth-reviewer
GitHub stars
163
Token cost
~1.8k tokens
SKILL.md length
772 words
Files
8 (incl. references)
Skills in repo
13
Repo updated
First seen
Licence
Apache-2.0

At a glance

Parameterized quality reviewer for Diffmode growth-tactics outputs across the whole pipeline.

  • Works in 4 steps: Load the rubric for dimension from… → Read spec_path (what the output MUST… → Apply the rubric exactly. The enrichment… → …
  • Gating any generating stage before downstream stages consume it
  • SKILL.md covers Invocation contract, Procedure, Readability (non-blocking… and Return shape (machine-readable…, plus 1 more section
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Growth Reviewer is an agent skill from acogood/diffmode_free. Parameterized quality reviewer for Diffmode growth-tactics outputs across the whole pipeline. Given a dimension name — an enrichment dimension (competitors|audience|acquisition-tactics), a think-tank dimension (competitor-gaps|cross-industry|platform-arbitrage), or the synthesis dimension (demand-gen-synthesis) — plus the spec and output paths, load the matching rubric and return a machine-readable verdict — score 1-10, APPROVED/REJECTED, format-compliance, and specific blocking issues. Use when gating any…

Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files, including reference files (for example `references/acquisition-tactics.md`, `references/audience.md` and `references/competitor-gaps.md`).

It sits in Education, covering Quizzes and assessments. The repository describes itself as: Free guerrilla growth tactics for startups, the kind your competitors won't come up with on their own. Runs in Claude Code or Codex: competitor read, buyer map, and 7 to 9… The licence is Apache-2.0.

When your agent uses it

  • Gating any generating stage before downstream stages consume it
  • Tasks that involve Quizzes and assessments

Example prompts

  • “/growth-reviewer”

Requirements

  • Python 3

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. Load the rubric for dimension from references/.md (relative to this
  2. Read spec_path (what the output MUST contain) and output_path (what you're
  3. Apply the rubric exactly. The enrichment rubrics use the three-part template
  4. Decide with the rubric's Decision Logic

What it can do on your machine

Read from SKILL.md and the folder at commit c175a4d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are json).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Growth Reviewer loads about 1.8k tokens when it runs, and up to ~32k if it reads all its reference files. Until then it costs about 145 tokens; SKILL.md has 772 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~145
When it runs · the whole SKILL.md, loaded when a task matches
~1.8k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~32k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from acogood/diffmode_free at commit c175a4d, republished under its Apache-2.0 licence (© acogood). 772 words, ~1,825 tokens.

Download SKILL.mdSave it as .claude/skills/growth-reviewer/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.
name
growth-reviewer
description
Parameterized quality reviewer for Diffmode growth-tactics outputs across the whole pipeline. Given a dimension name — an enrichment dimension (competitors|audience|acquisition-tactics), a think-tank dimension (competitor-gaps|cross-industry|platform-arbitrage), or the synthesis dimension (demand-gen-synthesis) — plus the spec and output paths, load the matching rubric and return a machine-readable verdict — score 1-10, APPROVED/REJECTED, format-compliance, and specific blocking issues. Use when gating any generating stage before downstream stages consume it.
metadata.version
2.0.0

Growth-Tactics Reviewer (parameterized)

You are an experienced reviewer verifying that a pipeline output meets quality standards before downstream prompts consume it. This skill is parameterized by dimension — one rubric per dimension is bundled in references/.

Distilled from the Diffmode AI-CMO enrichment reviewers (SR-ENR-001..006) and the demand-generation think-tank + synthesis reviewers, with paths normalized in the bundled copies. Threshold: score ≥ 7 = APPROVED; < 7 = REJECTED (the pipeline's output_validation_config norm).

Reviewer-model calibration (open item): in the Python pipeline these rubrics ran on gemini-pro / claude; here the reviewer agent is Sonnet. Scores may calibrate slightly differently. Treat 7 as the gate but lean on the blocking_issues (concrete, quotable gaps) rather than the raw number when a verdict is borderline. Same caveat carried from the enrichment pilot.

Invocation contract

The invoker (orchestrator/worker) supplies:

  • dimension — one of:
    • enrichment: competitors, audience, acquisition-tactics
    • think-tank research: competitor-gaps, cross-industry, platform-arbitrage
    • synthesis: demand-gen-synthesis
  • spec_path — the source skill that defines what the output must contain (e.g. ${CLAUDE_PLUGIN_ROOT}/skills/enrichment-competitors/SKILL.md, or the stage skill's SKILL.md). Use the path supplied by the invoker — do NOT bake a path from the rubric (the original rubrics hardcoded wrong paths; that is the bug this skill avoids).
  • output_path — the file being reviewed.
  • context_paths (optional) — founder-input.md and any upstream outputs the rubric lists as optional context (e.g. competitors-analysis.md for the audience review; growth-factors.json + synthesis-constraints.json + the think-tank reports for the demand-gen-synthesis review).

Procedure

  1. Load the rubric for dimension from references/<dimension>.md (relative to this skill directory). It contains the structured review (Format Compliance, Expert Quality 1-10, Downstream Utility / blocking check) + Decision Logic + Calibration notes.
  2. Read spec_path (what the output MUST contain) and output_path (what you're reviewing); read context_paths if provided. The rubric's own hardcoded ## Input Files paths are reference scaffolding — the authoritative paths are the ones the invoker passed.
  3. Apply the rubric exactly. The enrichment rubrics use the three-part template: Part 1 (PASS/FAIL format compliance, incl. the "Automatic FAIL" list), Part 2 (1-10 expert quality across its lettered dimensions), Part 3 (downstream-utility / blocking check). The think-tank + synthesis rubrics (competitor-gaps, cross-industry, platform-arbitrage, demand-gen-synthesis) are critique-style instead (evaluation lenses + a 1-10 grade + the rubric's own automatic-fail / blocking conditions) — follow each rubric's NATIVE structure, then map your result onto the standard return shape below: derive score from its 1-10 grade, format_compliance from any hard structural/automatic-fail conditions it lists (PASS if none triggered), and blocking_issues from its fail conditions + the most important gaps it raises.
  4. Decide with the rubric's Decision Logic:
    • format_compliance = FAIL → REJECTED, blocking.
    • quality_score < 7 → REJECTED, blocking.
    • quality_score ≥ 9 and PASS → APPROVED, confidence HIGH.
    • otherwise (7-8, PASS) → APPROVED, confidence MEDIUM (borderline; note improvements).
Show full SKILL.md (350 more words)Show less
Notes for the demand-gen-synthesis rubric (clean-room)

The demand-gen-synthesis rubric is clean-room native — it already scores against the per-run LIGHT DB and treats synthesis as the terminal free-plugin stage. Two principles it bakes in, restated here so the contract is explicit:

  • No proprietary-DB cross-check. Score vector usage, novelty, and white-space retention against the per-run growth-factors.json and synthesis-constraints.json the invoker passes — NOT against tactics_DB/. Do not penalize the output for using freshly mined vector IDs instead of canonical ones.
  • Synthesis is the last stage. There is no prioritization/implementation downstream. Judge the output as a set of 7-9 novel demand-gen tactic IDEAS: novelty (unconventional ratio), demand-gen purity, no deception, category diversity, and that each tactic is traceable to a vector combination in growth-factors.json. Do not require Week-1 day-by-day depth, a global "Technical Capabilities" table, or any artifact the synthesis-build skill does not emit (the rubric scores only the synthesis-build template's actual fields).

Readability (non-blocking feedback ONLY — not a scored lens)

Deliverable body copy is meant to follow ${CLAUDE_PLUGIN_ROOT}/reference/writing-style.md (plain English at grade 6–8, no marketing jargon, tactic names a smart friend would understand, vector IDs only in Source/traceability lines). When an output drifts far from that, mention it briefly in summary as a suggestion — but do NOT score it, do NOT add it to blocking_issues, and NEVER reject on readability alone. The scored rubrics are unchanged; readability is advisory.

Return shape (machine-readable — this is what the orchestrator consumes)

Return ONLY this JSON object as your final message (no prose around it):

json
{
  "dimension": "competitors",
  "score": 8,
  "verdict": "APPROVED",
  "format_compliance": "PASS",
  "blocking_issues": [],
  "confidence": "MEDIUM",
  "summary": "1-2 sentences for the orchestrator and founder"
}
  • verdict: APPROVED | REJECTED.
  • format_compliance: PASS | FAIL.
  • blocking_issues: array of specific, actionable strings (empty when APPROVED). When REJECTED, each item must be concrete enough for the worker to fix on re-dispatch — quote the missing section / failing requirement and the rubric rule it violates.

The orchestrator uses score/verdict to gate the DAG and injects blocking_issues verbatim into the brief of a fresh worker spawned for a capped retry (max 3 iterations). It does not need the long prose -sr.md / -review.md report; producing one is optional.

Bundled rubrics

Enrichment: references/competitors.md · references/audience.md · references/acquisition-tactics.md

Think-tank + synthesis: references/competitor-gaps.md · references/cross-industry.md · references/platform-arbitrage.md · references/demand-gen-synthesis.md

All paths normalized to forward-slash plugin-relative form.

© acogood, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 7 other files (references) in plugin/skills/growth-reviewer of acogood/diffmode_free.

  • SKILL.md
  • references/acquisition-tactics.md
  • references/audience.md
  • references/competitor-gaps.md
  • references/competitors.md
  • references/cross-industry.md
  • references/demand-gen-synthesis.md
  • references/platform-arbitrage.md

Open the folder on GitHubat commit c175a4d

Compare with similar skills

Growth Reviewer next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Growth Reviewer this skillacogood/diffmode_free163—~1.8kAutomated safety check: PassApache-2.0
DeepTutor CLIHKUDS/DeepTutor41k—~2.8kAutomated safety check: PassApache-2.0
AI Engineering Placement Quizrohitg00/ai-engineering-from-scratch67k—~2kAutomated safety check: PassMIT
Codebase to Coursezarazhangrui/codebase-to-course5.7k—~4.4kAutomated safety check: PassNone
AI Engineering Phase Quizrohitg00/ai-engineering-from-scratch67k—~2.1kAutomated safety check: PassMIT
Scholar EvaluationK-Dense-AI/claude-scientific-writer2.4k2 repos~2.9kAutomated safety check: NotesMIT

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  • Enrichment Audience

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Categories

Questions about Growth Reviewer

What does Growth Reviewer do?

Parameterized quality reviewer for Diffmode growth-tactics outputs across the whole pipeline. Growth Reviewer is an agent skill from acogood/diffmode_free. Parameterized quality reviewer for Diffmode growth-tactics outputs across the whole pipeline.

When should I use Growth Reviewer?

Growth Reviewer fits situations like: gating any generating stage before downstream stages consume it; tasks that involve Quizzes and assessments.

How do I install Growth Reviewer in Claude Code?

Run `npx skills add acogood/diffmode_free --skill growth-reviewer -a claude-code`. Or copy the skill folder (plugin/skills/growth-reviewer in acogood/diffmode_free) into .claude/skills/growth-reviewer in your project. Claude Code loads it when a task matches its description.

How do I install Growth Reviewer in Codex?

Run `npx skills add acogood/diffmode_free --skill growth-reviewer -a codex`. Or copy the skill folder (plugin/skills/growth-reviewer in acogood/diffmode_free) into .agents/skills/growth-reviewer in your project. Codex loads it when a task matches its description.

Can I use Growth Reviewer in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add acogood/diffmode_free --skill growth-reviewer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/growth-reviewer, .gemini/skills/growth-reviewer, .github/skills/growth-reviewer and .opencode/skills/growth-reviewer in your project.

What does Growth Reviewer need to run?

SKILL.md names no scripts, command-line tools or credentials: Growth Reviewer is instructions for the agent only. Our summary lists: Python 3.

Does Growth Reviewer access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Growth Reviewer safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Growth Reviewer use?

Growth Reviewer is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Growth Reviewer use?

About 1.8k tokens (SKILL.md is roughly 7.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 30k tokens, read only when the agent opens those files.

What are the alternatives to Growth Reviewer?

Skills that share tags, products or a category with Growth Reviewer: DeepTutor CLI (HKUDS/DeepTutor, 41k stars), AI Engineering Placement Quiz (rohitg00/ai-engineering-from-scratch, 67k stars), Codebase to Course (zarazhangrui/codebase-to-course, 5.7k stars) and AI Engineering Phase Quiz (rohitg00/ai-engineering-from-scratch, 67k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Growth Reviewer?

acogood (a GitHub user) maintains it in acogood/diffmode_free, which has 163 GitHub stars. The repository holds 13 skills in this directory. The repository was last updated on August 10, 2026.

Source: acogood/diffmode_free on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.