# Audit Accomplishments > Use when preparing a performance self-reflection, midyear or annual review, promotion packet, or brag document; when asked to collect, mine, gather, or summarize your accomplishments, contributions, or impact over a period; or when assembling cited evidence of work done across GitHub, Jira, Notion, Slack, and local agent histories over the last N months. - Skill: `paultyng/audit-accomplishments` (Agent Skill) - Install (CLI): `npx skillmds@latest add paultyng/audit-accomplishments` - Raw SKILL.md: https://api.skillmd.com/api/skills/paultyng/audit-accomplishments/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: paultyng (https://skillmd.com/u/paultyng) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/paultyng/audit-accomplishments --- # Audit Accomplishments Mine a person's contribution evidence across every source where their work lives, over a configurable window, and emit **cited** achievement summaries for review prep. Collection only — this skill does not draft the review. Sibling of `audit-history`: that skill mines sessions to improve agent config; this one mines work across external systems to surface achievements. The **local-history + memory discovery** is shared — reuse `audit-history` Phase 1 rather than re-deriving it. ## Inputs - **window** — default **last 6 months**. Accept an override as dates (`2026-01-01..2026-06-24`) or duration (`3 months`). Compute concrete start/end dates up front and state them. - **sources** — default all (below). Accept a subset override. - **taxonomy** (optional) — a review template's dimensions / career-level areas, supplied as an arg or a path. When absent, group by discovered theme only; do not invent a rubric. - **output dir** — default `./review-material/` in the cwd. ## Guardrails - **Read-only** across every external system. Never post, edit, comment, transition, or mutate. - **Cite everything.** Each claim carries a re-fetchable reference (URL, `file:line`, ticket id, transcript UUID) so it can be expanded during drafting. - **Completion over creation.** Weight what was *finished* in the window (PR merged, issue resolved), not what was opened or merely discussed. - **No invention or embellishment.** If a section has no evidence, say so. Stay grounded in what was found. ## Phase 1 — Discover identities & sources (mechanical, read-only) Resolve *who* the person is on each source and *where* to look. Do not summarize yet. - **GitHub** — `gh auth status` enumerates logged-in hosts/accounts. Record each `host` + `login`. The person may have multiple (e.g. work + personal); mine all unless overridden. - **Jira** — call the Atlassian MCP `atlassianUserInfo` (server name varies by install) for the account id; `getAccessibleAtlassianResources` for the cloud id(s). - **Notion** — get the self user via the Notion MCP (`get-users` / self lookup). - **Slack** — resolve the logged-in `user_id` (the search tool reports it). Mine **two axes**, since neither alone is complete: (a) **authored** — `from:<@USERID> after:` to enumerate the channels the person posts in (a global `from:me` misses DMs and needs real keywords, not stopwords), then drive Phase 2 per-channel; (b) **mentions of the person** — `<@USERID> after: -from:<@USERID>` and `to:<@USERID>`, surfacing where others defer to, route work to, or @-mention them. Prefer the `<@USERID>` token over literal-name search — a common first name is noisy and misses @-mentions entirely. - **Local agent histories + memory** — reuse `audit-history` Phase 1: Claude Code transcripts under `~/.claude/projects/*`, Cursor under `~/.cursor/projects/*`, memory under `~/.claude/projects/*/memory/`. Filter to files modified in the window. Report a one-screen inventory (accounts found per source, channel count, transcript/memory counts) before proceeding. ## Phase 2 — Mechanical raw dump (read-only) Run read-only queries per source and write **raw** results to `review-material/.*`. No interpretation. These are the evidence base and the citation source. Representative queries (adapt to tool versions; prefer `jq -c` for JSON): - **GitHub** — for each host/account (switch via `gh auth switch` or `GH_HOST`): - Merged in window: `gh search prs "author:@me merged:>=" --json number,title,repository,url,createdAt,closedAt --limit 500` - Opened in window (for in-flight work): `... "author:@me created:>="` - Reviews given: `gh search prs "reviewed-by:@me updated:>=" --json ...` - Substantive commits where PR data is thin: `gh search commits "author:@me committer-date:>=" --json ...` - **Contribution rank (optional — grounds "top/most/primary contributor" claims):** only for repos where the person is materially active (derive the repo set from the merged-PR dump above; cap the count to respect rate limits). For each such repo: - `gh api repos/{owner}/{repo}/stats/contributors` → per-contributor weekly `{w, c, a, d}`. **Returns HTTP 202 while GitHub computes the stats** — retry with backoff until 200. Sum the weeks falling inside the window, then rank the person by commits / additions / deletions. The endpoint caps at the last **52 weeks** (fine for the 6-month default; flag the gap for longer windows). - Merged-PR rank: group the window's merged PRs by `user.login` for a merged-PR-count rank; each PR's `additions`/`deletions` gives a line-churn rank. Reviews-per-author is **not** in the stats endpoint (needs per-PR review listing) — defer. - Ignore bot authors; where feasible exclude generated/vendored paths (they skew additions/deletions). **Squash-merge attributes a PR's whole churn to the squash author** — note this caveat in the output. - Iterate gh accounts/hosts as elsewhere in this phase. - **Jira** — `searchJiraIssuesUsingJql`: - `assignee = currentUser() AND resolved >= "" ORDER BY resolved DESC` (primary — completed) - `(reporter = currentUser() OR assignee = currentUser()) AND updated >= ""` (broader activity) - **Notion** — search is keyword-based and cannot filter by editor+date directly. Search broadly for likely topics, then `fetch` candidates and keep those whose `created_by`/`last_edited_by` is the self user and whose edit time is in-window. Capture page title + url + edit date, **plus reach + engagement signals** (applies to any shared-document source). Record which signal drove the rating: - **engagement** — comment/discussion volume (`get-comments`; `fetch` with `include_discussions`). More cross-team discussion ⇒ more relevant. - **visibility/audience** — lives in a team wiki or org-wide database vs a personal/scratchpad space; shared-to-web; collaborator breadth. - **inbound references (PageRank-style)** — how many other docs link to it (search the workspace for the page URL/title). Heavily-referenced docs are load-bearing. - **external corroboration** — search Slack for the doc URL to see where it was shared/discussed. Positive-only: Slack **retention** means *absence* isn't proof of low reach. - **view/impression counts** — not exposed by the Notion API/MCP; rely on the proxies above. - **Slack** — three sweeps: (a) **authored** — per channel from Phase 1, `from:<@USERID> in:<#channel> after:`; keep substantive messages (unblocking, explaining, decisions, proposals), **drop** acks (👍, "thanks", "sgtm") and recurring standups. (b) **mentions** — `<@USERID> after: -from:<@USERID>` and `to:<@USERID>`; this is the richest Collaboration/Influence/Leadership signal (where others route decisions to the person), and a `from:`/literal-name pass misses it. (c) **praise received** — shoutouts / kudos / Bonusly naming the person. Use `detailed` output to capture **resolvable permalinks**, not the search tool's raw timestamps. - **Local** — extract per-transcript work topic + tools + outcomes via `jq` (see `audit-history` extraction patterns). Pull project memory files in-window. ## Phase 3 — Fan-out summarization (subagents) Promote raw dumps into structured **achievement cards**. Hybrid two-stage: 1. **Enumerate** the work-list per source (cheap; from Phase 2 dumps). 2. **Fan out** summarization subagents only where volume warrants. Subdivide **per-artifact** (one PR/doc/ticket) for low volume, or **per-time-bucket** (e.g. per week / per month) when a source has many small items — bucketing keeps each subagent's context tight and preserves chronology. Each subagent follows `subagent-prompt-contract`: one-sentence goal, the relevant raw dump pasted inline (do **not** ask it to re-read this SKILL.md or re-query the source), the card schema below as the output cap, and a `Status:` prefix line. Use `model: haiku` for schema-driven extraction, `model: sonnet` where interpreting impact requires judgment (per `subagent-model-routing`). ### Achievement card schema ``` - title: - what: <1-2 sentences: what was done> - impact: - timing: # explicit dates - evidence: [] - theme: - dimension: - rank: " with raw numbers; omit otherwise> - ai_usage: # an aspect, not a required field ``` ## Phase 4 — Synthesize In the parent, after subagents return: 1. **Dedup cross-source.** The same work surfaces as a PR *and* a Jira ticket *and* a Notion doc *and* a Slack thread *and* a transcript. Merge into one card; collect all refs under `evidence`. 2. **Group by theme.** Cluster cards into a handful of named themes. 3. **Map to taxonomy** (if supplied). Tag each card's `dimension`; note which dimensions are well-covered. 4. **Gap-flag.** Call out dimensions/themes with thin or no evidence — so the person knows where to add detail or seek opportunities. Do not pad. 5. **Weight shared docs by reach + engagement.** Rank shared-document evidence up when it shows organizational reach/engagement — broad audience, high comment/discussion volume, many inbound links from other docs (PageRank-style), or corroborating Slack shares — and down when narrowly shared, undiscussed, or in a personal scratchpad. Say which signal drove the call; a widely-read, cited, discussed doc is far stronger evidence than a private one. External/Slack signals are positive-only (retention/access gaps mean absence ≠ low reach). 6. **Ground superlatives with rank.** Where a card implies "top / most / primary contributor," attach the computed GitHub rank (`#1 of N by commits / merged PRs over `) with the raw numbers. If no rank was computed for that repo, soften the claim — never assert a superlative the stats don't support. 7. **Surface AI-capability examples.** Collect cards with an `ai_usage` aspect into a dedicated list — concrete examples of AI/agentic work, with citations. ### Output - `review-material/` — per-source raw dumps (retained as the evidence base). - `review-material/highlights.md` — synthesized, grouped, cited cards; a **Gaps** section; an **AI-capability examples** section. Optionally seed reflection with these prompts (answer only from the cards, not invention): - What did I do that made someone else's job easier? - Where was there impact — speed, reliability, quality, understanding? - Which "small wins" might I forget in six months? ## Anti-patterns - Summarizing before the raw dump is written — you lose the citations. - Relying only on `from:me` or literal-name Slack search — misses DMs and @-mentions; also sweep the `<@USERID>` mention token + `to:<@USERID>`. - Counting opened/planned work as accomplished — weight completion. - Asserting "top contributor" or other superlatives without the stats to back them — compute the rank (`stats/contributors` + merged-PR group-by) or soften the claim. - Inventing a rubric when none was supplied — group by theme instead. - Any write/post/mutate call — this skill is strictly read-only. - Hardcoding identities, hosts, org names, or level taxonomies — discover them at runtime. ## Sources - Reuses local-history/memory discovery from the sibling `audit-history` skill.