# Nemock Press Release Archiver Press Release Analyzer > Press Release Analyzer - Skill: `tomevault-io/nemock-press-release-archiver-press-release-analyzer` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/nemock-press-release-archiver-press-release-analyzer` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/nemock-press-release-archiver-press-release-analyzer/raw - Safety review: pending (external: skill-scanner PASS, skillspector CAUTION) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/nemock-press-release-archiver-press-release-analyzer --- # Press Release Analyzer Three deterministic Python scripts produce structured inputs (`stats.json`, `ladder.json`, `sample.md`, etc.). YOU then read those artifacts and write the strategic synthesis directly as markdown. No external API calls — all the LLM work happens in the conversation, charged against the user's Claude subscription. ## When invoked Ask the user for, or infer: 1. **Archive directory** — the output of `press-release-archiver`, e.g. `acme-medtech/` 2. **Mode** — single-company OR comparative (the user will say "compare X and Y" or just give one company) 3. **CEO/founder names** — improves stakeholder-quote attribution. Optional but worth asking about. 4. **Optional filter** — for multi-product-line companies (Abbott, Stryker, Boston Scientific, Johnson & Johnson, etc.) where the archive mixes unrelated business units, ask whether the user wants to narrow the analysis to a specific product line or therapeutic area. If yes, get 3–8 keywords (e.g. `"DBS,deep brain stimulation,Infinity DBS"`). The filter applies word-boundary regex matching across headline + summary + body. Filtered output lands in `/analysis-/` so the same archive can drive multiple parallel playbooks (`analysis-dbs/`, `analysis-diabetes-care/`, etc.). 5. **For comparative**: anchor dates for each company. Default = first release in archive. Override with **the IPO/SPAC/Series-A date** if you want stage alignment. ## Stage 1 — Run the deterministic pipeline For a **single-company** analysis: ```bash python3 stats.py --ceo-names "Name1" "Name2" python3 ladder.py python3 sample.py ``` This writes: - `/analysis/stats.json` — full structured stats - `/analysis/patterns.md` — human-readable cadence/topic/headline summary - `/analysis/ladder.json` + `/analysis/ladder.md` — candidate credibility-ladder rungs - `/analysis/sample.md` — strategic-sample bundle of ~30-80 releases **With an optional product-line filter** (multi-line conglomerates): ```bash python3 stats.py --ceo-names "..." --filter "DBS,deep brain stimulation,Infinity DBS" python3 ladder.py --filter "DBS,deep brain stimulation,Infinity DBS" python3 sample.py --filter "DBS,deep brain stimulation,Infinity DBS" ``` The filter must be passed **identically** to all three scripts. Output lands in `/analysis-/` (the slug is the slugified first keyword, here `dbs`). Override with `--analysis-name ` to control the subdirectory name explicitly. `patterns.md` and `stats.json` both record the active filter and the match rate (e.g. "73 of 487 releases matched"). The synthesis output you write later should explicitly note that the analysis is narrowed to a product line — frame insights as "the company's *DBS communications strategy*" rather than "the company's PR strategy." For a **comparative** analysis, also run `compare.py`: ```bash python3 compare.py \ --anchor-a --anchor-b \ --out -vs-/ ``` With a filter (compares only product-line-relevant releases from each): ```bash python3 compare.py \ --anchor-a --anchor-b \ --filter "DBS,deep brain stimulation" \ --out -vs--dbs/ ``` This writes: - `/comparison.json` + `/comparison.md` — stage-aligned cross-company comparison - (When filtered) `/comparison.json` also records the match rate for each company. If either company has zero matches the script exits with a clear error. **Important: pick anchor dates that align by company stage, not calendar year.** For each company, choose the date it became a public-storytelling entity (typically the IPO date, SPAC merger close, or first material wire release). Aligning these makes the comparison strategically meaningful — what was each company doing at equivalent maturity, regardless of calendar year. ## Stage 2 — Read the artifacts Read in this order: 1. `patterns.md` — get the deterministic stats fixed in your head (cadence, topic mix, etc.) 2. `ladder.md` — see the candidate rungs in chronological order 3. `sample.md` — read the actual release content for ~30-80 strategically-selected releases 4. (For comparative) `comparison.md` — see the side-by-side year-by-year deltas The user is on a Claude monthly subscription, so the cost is your context window, not API tokens. The whole bundle for one company is typically ~15–25K tokens; for a two-company comparison, ~30–50K. Easily fits. ## Stage 3 — Write the strategic synthesis Produce **five files** in the analysis directory (or the comparison output dir for comparative mode), each as a focused, standalone deliverable. Then concatenate them into a combined `playbook.md`. Use the Write tool. ### File 1 — `credibility-ladder.md` The chronological proof-point sequence. Curate the candidate rungs from `ladder.md`: keep what's strategically meaningful (capital events, regulatory milestones, clinical demonstrations, flagship partnerships); drop the noise (routine appointments unless transformative, building-opening fluff, conference scheduling). For each kept rung: - Date + headline - Why it matters strategically (1 sentence) - Days since previous rung (already computed) - What the rung enabled / what it set up End with a **Lessons section** answering: how were proof-points sequenced? What pattern in spacing? What rung-types dominated each phase? What's the *minimum viable ladder* an early-stage company could borrow from this? ### File 2 — `drumbeat-map.md` Cadence patterns and what they reveal. From `patterns.md`'s clusters, silences, and per-year counts: - Identify the 3–5 most strategically meaningful clusters (drumbeat campaigns) and what they were building toward - Identify the most meaningful silences (60+ day gaps) — what was being prepared during the quiet? - Note overall cadence rhythm — steady (Globex Corp-style) or campaign-driven (Acme Medtech-style)? - For each cluster, list the headlines + your inference of the strategic intent End with **applicable plays** for an early-stage client. ### File 3 — `validator-sequence.md` Stakeholder orchestration. From the stakeholder data in `stats.json` and the sample releases: - When did internal voices give way to clinical voices and institutional voices? - What was the company's first third-party-validated release? What did that signal? - How was the CEO quote rate used? Always a CEO quote, or selectively? - What kinds of validators were brought in at what stages? End with **the validator-recruitment playbook**: at what milestone do you bring in surgeons / customers / hospitals / board members / investors? What's the right order? ### File 4 — `foundation-plays.md` Specifically scoped to **Years 1–3** of the archive (Foundation→Build phase). For each year: - What categories of releases did the company prioritize? - What were the 5–8 dominant play types? (e.g., "headquarters opening," "key hire," "advisory board reveal," "first customer," "TIME's Best Inventions appearance") - For each play, give a generic template an early-stage client could adapt This is the highest-value deliverable for the user's stated client base (seed → pre-IPO). ### File 5 — `playbook.md` (combined) Stitch all four files together with a brief executive summary at the top: - 3-bullet TL;DR - "Top 3 plays this archive teaches" - Then sections for each of the four analyses above - Final "Caveats and limitations" section For **comparative mode**, also produce a sixth file: ### File 6 — `comparative-playbook.md` (only for `--compare`) Read `comparison.md` and write a head-to-head comparison along these dimensions: - Cadence rhythm (campaign-driven vs steady-state) - Credibility-ladder pacing (how fast each company climbed) - Stakeholder strategy (CEO-centric vs distributed voices) - Topic mix shift over equivalent stages - Three to five **inflection-point plays** that one company did and the other didn't (or did differently) End with a **stage-matched recommendations** section: which playbook best fits the user's seed/Series-A/pre-IPO clients, and why. ## Style guide for synthesis - **Be specific.** Quote actual headlines and dates from the archive. Don't say "they used a lot of partnerships" — say "they signed 4 hospital-system agreements in 18 months (Partner A Sept 2023, Partner B Aug 2024, Partner C May 2025, [next] X)." - **Surface contrasts.** When stats reveal a delta (e.g., one company has a much higher CEO quote rate than the other, or one has zero clusters while the other has many), call it out. The deltas are the strategy. - **Make it actionable.** End each file's analysis section with at least one "play" a client could literally execute next quarter. - **Don't moralize.** Note when crisis communications happened (delistings, departures) and analyze the language they used; don't praise or critique. - **Token discipline.** You don't need to load full bodies of every release. The sample bundle was specifically curated to give you what you need. ## Reporting back After writing the files, report to the user: - Which files were written and their paths - 2–3 most surprising findings from the analysis (the kind of thing they'd want to highlight to a client) - Any caveats (small-sample issues, classification noise, missing data) - For comparative mode: the most strategically actionable contrast between the two companies ## Failure modes to watch for - **Too few releases** — under ~15 releases, statistical patterns are unreliable; surface this caveat. - **Mature company with mostly earnings releases** — Globex Corp's archive is dominated by quarterly reporting; the *interesting* releases are the rare non-earnings events (FDA updates, share repurchases, settlements). Lean into those. - **Anchor mismatch** — for comparative, if the two anchor dates aren't actually equivalent in company stage, surface this and recommend a different anchor. - **CEO names not provided** — without them, CEO quote rate is undercounted. Ask the user for known CEO/founder names if they didn't provide. --- > Source: [nemock/press-release-archiver](https://github.com/nemock/press-release-archiver) — distributed by [TomeVault](https://tomevault.io).